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ROLE OF CULTURE AND MACROECONOMIC SHOCKS
IN DRIVING HERDING BEHAVIOR: EVIDENCE FROM
DEVELOPED, EMERGING AND FRONTIER STOCK
MARKETS
Researcher: Supervisor:
Zuee Javaira Dr. Arshad Hassan
REG NO. 35-FMS/PHDFIN/S11 Associate Professor, CUST
Co. Supervisor:
Dr. Syed Zulfiqar Ali Shah
Associate Professor, FMS, IIUI
Faculty of Management Sciences
INTERNATIONAL ISLAMIC UNIVERSITY,
ISLAMABAD
ii
ROLE OF CULTURE AND MACROECONOMIC SHOCKS
IN DRIVING HERDING BEHAVIOR: EVIDENCE FROM
DEVELOPED, EMERGING AND FRONTIER STOCK
MARKETS
Zuee Javaira
REG NO. 35-FMS/ Ph.D. Fin/S11
Submitted in partial fulfillment of the requirements for the
Ph.D. degree with the specialization in Finance
at the faculty of management sciences,
International Islamic University,
Islamabad.
Supervisor
Dr. Arshad Hassan April, 2018
Dr. Syed Zulfiqar Ali Shah
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In the name of Allah, the most merciful and beneficent
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DEDICATION
I dedicate this work to my parents Mr. and Mrs. Ghulam Mustafa Qureshi, who have
given me so much without expecting anything in return
v
(Acceptance by the Viva Voice Committee)
Title of Thesis: “Role of Culture and Macroeconomic Shocks in Driving Herding Behavior: Evidence
from Developed, Emerging and Frontier Stock Markets”.
Name of Student: Zuee Javaira
Registration No: 35-FMS/PHDFIN/S11
Accepted by the Faculty of Management Sciences INTERNATIONAL ISLAMIC UNIVERSITY
ISLAMABAD, in partial fulfillment of the requirements for the Ph.D. degree with the specialization
in Finance at the faculty of management sciences.
Viva Voce Committee
________________________
Dr. Arshad Hassan
(Supervisor)
________________________
Dr. Syed Zulfiqar Ali Shah
(Co-Supervisor)
_______________________
(External Examiner)
________________________
(Internal Examiner)
________________________
(Chairman HS & R)
________________________
(Dean)
Date:___________________
vi
ABSTRACT
The basic purpose of this research is to investigate herding behavior, the role of culture, the effect of
macroeconomic shocks on herding behavior, and the existence of contagion of herding due to the crisis
and extreme shocks in the market. This study investigates the herding behavior towards market
consensus in a set of Developed, Emerging and Frontier Markets of Asia, Asia Pacific and Europe.
For the identification of herding behavior towards market consensus this study employs return
dispersion models and the state space model. The time variation in herding behavior is identified by
estimating return dispersion model through Kalman filter estimation. One of the main objective of this
research is to determine the role of culture in driving herding behavior. This study employs Hofstede’s
six cultural dimensions of culture and introduces them as an explanatory variable in the return
dispersion model.
This study utilizes ARIMA residuals of macroeconomic variables and introduce them in state
space model to figure out the effect of unexpected shocks of this variable on investor herding behavior.
This study focuses on behavioral link namely contagion of herding (pure contagion) in a given set of
market and determines the cross country linkages of each market with a global leader (US) through
extreme shocks in macroeconomic components or crisis. For the said purpose conditional correlation
between herding measures is generated through diagonal VECH bivariate GARCH (1,1) model. The
major findings of the study report significant effect of herding behavior in the majority of the markets.
state space model measure provide better results for all measure.
Culture play a significant role in the determination of herding behavior. The presence of
cultural dimensions changes the magnitude of the herding behavior in sample countries. The herding
behavior of investor is affected by some macroeconomic shocks hitting the specific market. But
macroeconomic shocks do not seem to play a major role in defining herding behavior in most of the
markets. Majority of the markets exhibit a strong correlation with US market which is seen as turning
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points of herding measure during a global financial crisis of 2007-2009. This correlation also increased
due to some specific macroeconomic shocks.
This study provides a complete analytical framework in developed emerging and frontier markets of
Asia, Asia Pacific and Europe, where economic, cultural, behavioral and external factors are
simultaneously integrated to provide a better explanation of herding behavior. Therefore, in its unique
design, this research is an attempt towards bridging a gap between, cultural finance, behavioral finance,
and standard finance.
Keywords: Herding Behavior, Cultural Dimensions, Macroeconomic Shocks, Financial Crisis,
Contagion of Herding.
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COPYRIGHTS
Copyright 2017 by IIUI Student
All rights reserved. Reproduction in whole or in part in any form requires the prior written
permission of Ms. Zuee Javaira or designated representative.
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DECLARATION
I hereby declare that this thesis, neither as a whole nor as a part thereof, has been copied out from any
source. It is further declared that I have prepared this thesis entirely on the basis of my personal effort
made under the sincere guidance of my supervisor and colleagues. No portion of work, presented in
this thesis has been submitted in support of any application for any degree or qualification of this or
any other university or institute of learning.
Ms. Zuee Javaira
PhD (Finance)
Faculty of Management Sciences
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APPRECIATION AND GRATITUDE
The writing of this thesis has been one of the most significant academic challenges I have had. Without
the support, patience, and guidance of my supervisors, Dr. Arshad Hassan, and Dr. Syed Zulfiqar Ali
Shah, this study would not have been achieved. It is to them that I owe my deepest gratitude. Their
wisdom and knowledge have been of great value to me. Their assistance and guidance in getting my
career started on the right foot have provided me with the foundation for a career in academia.
I wish to express my gratitude to Dr. Abdul Rashid, who helped me to learn some techniques. A very
special thanks is due to my friend and colleague Dr. Najam us Sahar for being there whenever I needed
it. I would like to thank the members of Higher Studies office of Department of Management Sciences,
International Islamic University Islamabad for their support and guidance throughout my MS-PhD
degree completion. I am deeply grateful to all members of the examination committee for agreeing to
read the manuscript and to participate in the defense of this thesis.
Special thanks to my family for their unwavering love, continuous encouragement, and quiet support.
I would like to express my deepest appreciation to my beloved Daughter, Haya Fatima, for her
tolerance of my occasional bad moods. Without her patience, understanding, and unconditional love,
I would not be able to achieve this goal, one that I thought was far beyond my reach.
Above all, I bow before and thank ‘Allah Almighty’ without whose blessings this work would have
never been completed.
Ms. Zuee Javaira
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FORWARDING SHEET
The thesis entitled “Role of Culture and Macroeconomic Shocks in Driving Herding Behavior:
Evidence from Developed, Emerging and Frontier Stock Markets” submitted by Ms. Zuee Javaira as
partial fulfillment of Ph.D. degree with the specialization in Finance at the faculty of management
sciences, has completed under my guidance and supervision. The changes advised by the external and
the internal examiners have also been incorporated. I am satisfied with the quality of student’s research
work and allow her to submit this thesis for further process as per IIU rules & regulations.
Date:_______________________ Signature:___________________
Name : ____________________
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Contents
Contents ........................................................................................................................................ xii
List of Abbreviation ...................................................................................................................... xx
Chapter no 1 .................................................................................................................................... 1
1. Introduction: ............................................................................................................................ 1
Background of the Study ......................................................................................................... 1
Theoretical Background .......................................................................................................... 4
Psychological Forces ....................................................................................................... 6
Sociology Forces .............................................................................................................. 6
Biological Forces ............................................................................................................. 7
Theorizing and Conceptualizing Investor Behavior under Sociological and Psychological
Forces. ................................................................................................................................................ 7
Bounded Rationality Theory ............................................................................................ 7
Prospect Theory ............................................................................................................... 8
Theory of Mind ................................................................................................................ 9
The ABC Model ............................................................................................................... 9
Conceptualizing ............................................................................................................. 10
Motivation of Study .............................................................................................................. 11
Research Gap: ....................................................................................................................... 13
Statement of the Research Problem ...................................................................................... 15
Research Questions: .............................................................................................................. 16
Objectives of Study: .............................................................................................................. 16
The significance of the Study: ............................................................................................... 17
Theoretical Contribution: ............................................................................................... 17
Practical Contribution .................................................................................................... 21
xiii
Organization of the Study .................................................................................................. 23
2. Literature review and Theoretical Framework ..................................................................... 24
Traditional Finance: .............................................................................................................. 24
Rational preferences....................................................................................................... 24
Efficient Market Hypothesis: ......................................................................................... 25
Behavioral Finance ................................................................................................................ 28
Theories of Human Behavior: ........................................................................................ 30
Behavioral Biases: ......................................................................................................... 31
Herding Behavior: ................................................................................................................. 32
Theories of Herding Behavior ....................................................................................... 33
Models of Herding Behavior ......................................................................................... 34
Empirical Evidence on Institutional Investor Herding .................................................. 36
Empirical Evidence on Aggregate Market Activity....................................................... 38
Culture: .................................................................................................................................. 40
Empirical Studies of Cultural Finance: .......................................................................... 42
Contagion .............................................................................................................................. 43
Fundamental Based Contagion ...................................................................................... 45
Common Cause Contagion ............................................................................................ 45
Pure Contagion............................................................................................................... 46
Determinants of Herding Behavior ....................................................................................... 48
Stock Market Performance ............................................................................................ 48
Volatility of Stock .......................................................................................................... 49
Global Volatility ............................................................................................................ 49
Herding Behavior and Culture: ............................................................................................. 50
Individualism and Herding Behavior ............................................................................. 51
xiv
Power Distance and Herding Behavior: ......................................................................... 52
Masculinity vs Feminist and Herding Behavior ............................................................ 52
Uncertainty Avoidance and Herding Behavior: ............................................................. 53
Long-term Orientation and Herding Behavior:.............................................................. 54
Indulgence versus Restraints .......................................................................................... 55
Effect of Macroeconomic Shocks on Herding ...................................................................... 55
Herding Behavior and Financial Contagion .......................................................................... 57
3. Data and Methodology .......................................................................................................... 59
Research Design: ................................................................................................................... 59
Data and Sample: ........................................................................................................... 59
Methodology ......................................................................................................................... 61
Measure of Herding ....................................................................................................... 61
Model Selection Criteria Applied to Return Dispersion and State Space Model .......... 65
Determinants of Herding Behavior ................................................................................ 66
Herding Behavior and Culture ....................................................................................... 67
The Effects of Macroeconomic Shocks on Herding ...................................................... 70
Herding and Financial Contagion: ................................................................................. 71
4. Results and Discussion ......................................................................................................... 75
Return Dispersion Model Based on Christie & Huang (1995) ............................................. 75
Descriptive Statistics of Cross-Sectional Standard Deviation ....................................... 75
Estimates of Herding Measure in Extreme Market Conditions ..................................... 78
Return Dispersion Model based on Chiang et al. (2000) ...................................................... 80
Descriptive Statistics of Cross-Sectional Absolute Deviation of Returns ..................... 80
Estimates of Herding Measure Based on Constant Coefficient Model ......................... 83
Estimates of Herding based on Chang and Zheng (2010) ............................................. 86
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Time-Varying Model of Herding Behavior .......................................................................... 89
Descriptive Statistics of Time-Varying Herding Parameter .......................................... 89
The Time Series of Herding Coefficient ........................................................................ 92
Determinants of Herding Behavior ................................................................................ 99
Culture and Herding Behavior ............................................................................................ 103
Power distance and Herding Behavior ......................................................................... 103
Individualism and Herding Behavior ........................................................................... 103
Masculinity and Herding Behavior .............................................................................. 104
Uncertainty Avoidance and Herding Behavior ............................................................ 106
Long-Term Orientation and Herding Behavior ........................................................... 106
Indulgence and Herding Behavior ............................................................................... 107
Estimation of State Space Model: ....................................................................................... 108
Properties of Cross-Sectional Standard Deviation of betas ......................................... 108
Properties of Log of Cross-Sectional Standard Deviation ........................................... 111
Estimates of Herding Measure and State Space Model ............................................... 116
Properties of Herding Measure .................................................................................... 118
Evolution of Herding Measures over Time ................................................................. 120
Selection of best Model based on AIC and BIC ................................................................. 147
Estimation of Macroeconomic Shocks on Herding Behavior ............................................. 149
Contagion of Herding .......................................................................................................... 161
Impact of Crisis Dummy on Cross Correlation of Herding Behavior ......................... 162
Impact of Macroeconomic Shocks on Cross-Correlation of herding behavior ........... 166
5. Conclusion .......................................................................................................................... 170
Summary and Findings........................................................................................................ 170
Discussion ........................................................................................................................... 175
xvi
Methodological Implications and limitation ....................................................................... 177
Generalizability ................................................................................................................... 178
Theoretical Implications ...................................................................................................... 178
For Risk Diversification ............................................................................................... 179
For Behavioral Asset Pricing Model............................................................................ 179
For Behavioral Portfolio Management ........................................................................ 180
Policy and Practical Implication ......................................................................................... 180
For Individual Investors ............................................................................................... 180
For Institutional Investors ............................................................................................ 181
For International Investors ........................................................................................... 181
For Financial Market Regulators ................................................................................. 182
Limitations of the Study ...................................................................................................... 182
Future Recommendations .................................................................................................... 183
References ................................................................................................................................... 184
Appendix 1 .................................................................................................................................. 217
Appendix 2 .................................................................................................................................. 220
Appendix 3 .................................................................................................................................. 221
Appendix 4 .................................................................................................................................. 222
xvii
Table of Tables
Table 3.1: List of sample countries with starting dates ...................................................................... 60
Table 4.1: Descriptive statistics of Cross sectional Standard Deviation ............................................. 76
Table 4.2: Estimates of herding measure in extreme market conditions: CSSD ................................. 78
Table 4.3: Descriptive Statistics of Cross-Sectional Absolute Deviation CSAD ................................ 82
Table 4.4: Estimates of herding measure based on constant coefficient model: CSAD...................... 83
Table 4.5: Estimates of asymmetric herding behavior based on constant coefficient model: CSAD . 87
Table 4.6: Descriptive Statistics of herding coefficient determine by Dynamic Model ...................... 90
Table 4.7: Domestic and Cross-Market Determinants of Herding Behavior ..................................... 100
Table 4.8: Estimation of Individual Cultural Dimensions on Herding Behavior .............................. 105
Table 4.9: Descriptive statistics of Cross-Sectional Standard Deviation of betas ............................. 109
Table 4.10: Descriptive statistics of Log of cross-sectional standard deviation of betas .................. 111
Table 4.11: Estimates of Herding Measure and State Space Model .................................................. 114
Table 4.12: Descriptive statistics of Herding measure, Hmt ............................................................. 118
Table 4.13: Selection of model based on AIC and BIC ..................................................................... 147
Table 4.14: The structures of estimated ARIMA models with the order (p,d,q) of all macroeconomic
variables. ............................................................................................................................................ 150
Table 4.15: Impact of Macroeconomic shocks on Herding ............................................................... 159
Table 4.16: Tests of changes in correlations between herding towards market on behalf of unexpected
variations in macroeconomic variables in US countries. ................................................................... 164
xviii
Table of Figures
Figure 1-1: Theoretical perspective of Financial Market Functioning; Source: (Tuyon & Ahmad, 2016)
................................................................................................................................................................ 5
Figure 1-2: Conceptualizing the Irrational Investor behavior (Source: Tuyon & Ahmad, 2016) ....... 10
Figure 2-1: Source: Schindler (2007) .................................................................................................. 30
Figure 4-1: Time series of 2,t in developed markets(continued) ......................................................... 95
Figure 4-2: Time series of 2,t in emerging and frontier market .......................................................... 97
Figure 4-3: Herding evolution in Australian Market ......................................................................... 121
Figure 4-4: Herding evolution in Australian Market ......................................................................... 122
Figure 4-5: Herding Evolution in Belgium Market ........................................................................... 123
Figure 4-6: Herding Evolution in Chinese Market ............................................................................ 124
Figure 4-7: Herding Evolution in Danish Market .............................................................................. 125
Figure 4-8: Herding Evolution in French Market .............................................................................. 126
Figure 4-9: Herding Evolution in German Market ............................................................................ 127
Figure 4-10: Herding evolution in Greece Market ............................................................................ 128
Figure 4-11: Herding evolution in Hong Kong Market ..................................................................... 129
Figure 4-12: Herding Evolution in Indian Market ............................................................................. 130
Figure 4-13: Herding Evolution in Indonesian Market ...................................................................... 131
Figure 4-14: Herding Evolution in Italian Market ............................................................................. 132
Figure 4-15: Herding Evolution in Japanese Market ......................................................................... 133
Figure 4-16: Herding Evolution in Korean market ............................................................................ 134
Figure 4-17: Herding Evolution in Malaysian Market ...................................................................... 135
Figure 4-18: Herding Evolution in Netherlands Market .................................................................... 136
Figure 4-19: Herding Evolution in New Zealand Market .................................................................. 137
Figure 4-20: Herding Evolution in Norway Market .......................................................................... 138
xix
Figure 4-21: Herding Evolution in Pakistani Market ........................................................................ 139
Figure 4-22: Herding Evolution in Philippines Market ..................................................................... 139
Figure 4-23: Herding Evolution in Portugal Market.......................................................................... 140
Figure 4-24: Herding Evolution in Singapore market ....................................................................... 141
Figure 4-25: Herding Evolution in Spanish Market .......................................................................... 141
Figure 4-26: Herding Evolution in Sri Lankan Market ..................................................................... 142
Figure 4-27: Herding Evolution in Swedish Market.......................................................................... 143
Figure 4-28: Herding Evolution in Swiss Market .............................................................................. 144
Figure 4-29: Herding Evolution in Thai Market ................................................................................ 144
Figure 4-30: Herding Evolution in Turkish Market ........................................................................... 145
Figure 4-31: Herding Evolution in UK market .................................................................................. 146
Figure 4-32: Herding Evolution in US market .................................................................................. 147
Figure 4-33: Correlation between US and other developed markets herding measures .................... 161
Figure 4-34: Conditional Correlation Graph between US and other emerging and Frontier Markets
............................................................................................................................................................ 161
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List of Abbreviation
EMH Efficient Market Hypothesis
CAPM Capital Asset Pricing Model
APT Arbitrage Pricing Theory
CBOE Chicago Board of Exchange
CSSD Cross Sectional Standard Deviation
CSAD Cross Sectional Absolute Deviation
LSV Lakonishok, Shleifer, & Vishny Model
OECD Organization for Economic Co-operation and Development
MSCI Morgan Stanley Capital International
ICB Industry Classification Benchmark
IMF International Monetary Fund
IP Industrial Production Index
MS Money Supply
ER Exchange Rates
IR Interest Rates
VIX Global Volatility Index
PDI Power Distance Index
IDV Individualism Index
MAS Masculinity Index
UAI Uncertainty Avoidance Index
LTO Long-Term Orientation Index
IVR Indulgence Index
ARIMA Auto Regressive Integrated Moving Average
xxi
GARCH Generalized Autoregressive Conditional Heteroskedasticity
1
Chapter no 1
1. Introduction:
Background of the Study
In a world of capitalist economies, a linkage between saver and borrower is bridged through some
sort of capital markets. In a corporate sector particularly, funds are collected for investors in order to
make productive use either through direct financing (issuance of securities to the public) or indirect
financing (borrowing through financial intermediaries). Proper functioning of markets is a primary
concern of all market players. Other than the functioning of the market, scholars, practitioners, and
regulators are also concerned about the ability of a market to process information. Therefore, the
efficiency of a market remains debatable since decades. According to traditional efficient market,
theory markets are efficient and investor decisions are based on the rational expectation of future prices
and price quickly incorporate the new information in a homogenous manner (Fama, 1965). Several
standard models of finance are based on the assumption of the efficient market hypothesis (EMH).
Subrahmanyam (2008) classifies standard finance models as (i) portfolio allocation based on expected
risk and return (ii) risk-based asset pricing models (iii) the pricing of contingent claims and (iv)the
Miller and Modigliani theorem and its augmentation by the theory of agency. The underlying rationale
of all classifications is value maximization through rational decision-making process. Strong empirical
support for EMH is found in the literature (Lucas, 1978; Lo & Hasanhodzic, 2010) but there remains
many questions unanswered like (a) why trading is done by individual investors? (b) Why factors other
than risk alter the returns across stocks.
The efficient market hypothesis is critically analyzed both empirically and theoretically, and
literature highlights several flaws like behavioral anomalies in modeling real-life security returns
(Shiller, 1999; and Summers, 1986). In order to answer the above-stated questions, researchers from
2
social psychology interrupt in the financial world and find out that people behave in a very unusual
manner when there are monetary transactions. Imperfections in the market are attributed to numerous
cognitive biases (e.g., overconfidence, framing bias and loss aversion), human responses (social
influences, cultural norms, herding effect) and errors (investor overreaction) by behavioral economists
and investor’s decisions are sometimes very irrational and are often affected by emotional and
cognitive errors.
Herding behavior is one of the most popular behavioral explanations of market inefficiency and
excess volatility in financial markets. Investor choices on the basis of available information in a
financial market can be behaviorally explained as herd formation. This behavior often results in a
tendency of an investor to imitate the action of other market players. Herding is simply defined as
mimicking behavior where investors simply follow the market consensus or imitate the activities of
financial leaders (Hirshleifer & Teoh 2003).
Social interactions and psychological biases influence the decision-making process and a choice is
made from what is considered “rational”. So investor decision is not based exclusively on his own
information set rather it is a choice out of both, his judgments and how others are behaving in the
market. Therefore, Herding is a simple investment strategy based on the mimicking action of investors
based on market consensus (Bikhchandani & Sharma, 2000).
Theoretically, herding is classified into two main categories. One category tests herding behavior
with aggregate market data (Christie & Huang, 1995; Chang et al., 2000; Hwang & Salmon, 2004;
Chiang & Zheng 2010).The other category focuses on portfolio investors (e.g., Lakonishok et al., 1992;
Trueman, 1994; Welch, 2000). This study is an attempt to identify herding in major developed,
emerging and frontier markets using aggregate market data. This type of herding can be identified
using return dispersion or state space models. For return dispersion this study employed, linear and
nonlinear models proposed by Christie & Huang, (1995) and Chang et al. (2000), whereas herding due
to market consensus is examined by using state space model proposed by Hwang & Salmon (2004).
3
Investor psychology and the environment in which decisions are made play a critical role in
the decision-making process of investor and sometimes prevent him from making rational choices.
Investor psychology is interpreted into behavioral biases that are explained through the investor’s
unavoidable tendency towards errors. Gervais, Heaton, & Odean (2011) find that overconfident
managers try their best to collect information that will improve their success rates and the values of
projects, and so tend to accept compensation contracts that involve excessive risk. Hence, they exhibit
herding behavior in the market.
Investor decision is highly affected by the value, norms, and belief of people living in a society.
All these attributes define the culture of a specific community. A major contribution of this research
is to impact of cultural differences on the presence and intensity of herding measure. Culture has been
scientifically defined in many ways since decades (Kroeber & Kluckhohn, 1952). This study relied
on the understanding given by Hofstede (1980). He defines culture as “the collective programming of
the mind distinguishing the members of one group or category of people from others”. This study uses
all six dimensions of Hofstede’s cultural dimensions in this regard. Return dispersion models
developed by Chang et al. (2000) is modified to test the presence of herding behavior.
Another limitation of previous studies is the use of the constant coefficient model in using
nonlinear return dispersion model, where the average measure of herding is assumed to be constant
over time. In the time of market stress, due to structural changes, these constant parameters fail to
uncover information about the market dynamics. Brown, Wei, & Wermers (2013) find more
pronounced herding behavior in mutual funds trade over time. Therefore, this research uses the time-
varying model to identify the changing effect of imitative behavior and the factor that determine this
dynamic nature of herding behavior.
Other than psychological and empirical evidence, this study also includes the effect of market
fundamentals like macroeconomic variables on herd formation. If the markets are efficient then
volatility in the market is explained by market fundaments and herding behavior cease to exist.
4
Macroeconomic variables are fundamental in explaining market movements and expected to reflect
the market dynamics. The macroeconomic variables have long been used in research to figure out stock
market volatility (Bernanke & Kuttner, 2005; Rigobon & Sack, 2008). Specifically, the effect of
macroeconomic shocks and their effect on stock prices and volatility has been studied so far (Pearce
& Roley, 1985; and Bloom, 2009).The effect of these macroeconomic shocks and uncertainty shocks
like 9/11 on investor behavior is limited to developed markets only (Messis & Zapranis, 2014).
Therefore, this study attempt to explore the effects of macroeconomic shocks on the estimated herd
measures towards the market in a set of developed, emerging and frontier markets of Asia, Asia Pacific
and Europe. Previous studies on herding behavior include macroeconomic variable in the return
dispersion and state space models (Hwang & salmon, 2004; Tan, Chiang, Mason, & Nelling, 2008;
and Javaira & Hassan, 2015). These studies directly check the impact of macroeconomic variables on
herding behavior but this study is unique in a sense that the effect of unexpected variation in
macroeconomic variables is used to identify herding behavior.
When markets are integrated the financial crisis is transmitted from one market to another with
weak macroeconomic fundamentals. Chiang, Jeon, & Li (2007) suggest that during financial crisis risk
is transferred across markets due to contagion effect and herd formation further aggravates crisis in the
market. Therefore, this research investigates contagion due to herding in the period of financial crisis
between the sampled markets. Due to contagion the benefit of international portfolio diversification
eliminates. This situation results in the increase in co-movements of asset prices in two markets after
a crisis period. Economou et al. (2011) find a higher degree of co-movements in four European
markets. This study is an attempt to find out contagion of herding either due to macroeconomic shock
or crisis event.
Theoretical Background
Although significant, the theoretical background under the foundation of irrational behavior is
vague and kept aside in academic literature, policy and practice. In the literature of finance, the
5
empirical findings on the behavioral disturbances have increasingly questioned the validation of
standard finance theory and models. This issue has gained importance and challenging because the
paradigm of behavioral finance and modern finance are differentiated with the viewpoint of the
theoretical building block and actual market behavior of an investor. Modern Finance explains the
investor decision on the basis of rationality and market efficiency. Whereas, proponents of behavioral
finance are of the view that investor behavior is bounded rational and collectively give rise to an
irrational market.
One of the major drawbacks of the behavioral finance research is the nonexistence of a unified
theory that can explain the origin caused and effects of investor irrational behavior. Behavioral finance
paradigm explains the complexity and dynamism of investor behavior and their decision making in a
complex system (Jacobs & Levy, 1989; Mitroi & Oproiu, 2014) through interdisciplinary theories. To
draw a theoretical background, it is necessary to coordinate the investors, security prices and markets
in a dynamic system.
Figure 1-1: Theoretical perspective of Financial Market Functioning; Source: (Tuyon & Ahmad, 2016)
From the above figure, it is clear that the investor behavior is the nucleus of the financial system
that determines the market and asset price behavior. Behavioral finance is based on the view that
investor are not fully rational and efficiency is not constant. The current theoretical foundation for
investors in behavioral finance paradigm based on the field of psychology are the bounded rationality
Market Behavior
Asset prices Behavior
Investors Behavior
6
theory (Simon, 1955) and prospect theory (Kahneman & Tversky, 1979). According to psychology,
irrationality is a basic human trait (Ellis, 1976). It is argued that the anomalies are partially predictable
due to the human cognitive errors and Biases which normally leads to inefficiency (Schneider &
Lappen, 2000; Ritter, 2003). This offers support to hypothesis of the adaptive market efficiency Lo
(2004, 2005) as a representative theory for the financial market.
The theoretical perspective discussed above prove the collective effect of internal and external forces
namely, sociological, psychological and biological factors along with the modern finance paradigm on
investor behavior that ultimately shapes the asset pricing mechanism and overall market behavior.
Psychological Forces
Psychological forces affect the decision making and individual behavior through the influence of dual
cognitive-affective processes (Kahneman, 2003) errors and biases. These cognitive system thinking
generate errors in the decision-making process and these cognitive errors are collectively termed as
heuristics. Heuristics act as a rule of thumb to reduce the complexity of decision situation (Das &
Teng, 1999; Fuller, 1998; De Bondt, 1998). The biases also produced by the effective system of
cognition used in the decision-making process, these are sentiments, emotions, and mood (Etzioni,
1988).
Sociology Forces
In sociology investment in markets is considered as a social phenomenon, where individual
thinking decisions are not in isolation and the social influence occurs in a complex and uncertain
situation for decision making (Warneryd, 2001). This innate tendency is highlighted by Festinger
(1954) where people in social system compare their capacity and attitude to others and as a result it
affects their investment decisions. Keynes explain these traits as beauty contest where individuals
7
decisions are taken by the belief system as others think and the way others behave in a market (Keynes,
1936). The examples of these traits are Herding, Cultural norms and Social Influence.
Biological Forces
The biological factors in human decision making are explained by the (Ellis, 1976). (Harlow &
Brown, 1990) argued that Individual risk tolerance is affected by the complex set of human
neurochemical system and personality characteristics. From a theoretical perspective, Murphy (2012)
provides an insight into the neuroscientific explanation of time-varying risk aversion and sequential
changes in human expectations. They further explain that the irrational behavior that causes mispricing
and ultimately inefficiency in financial markets is caused by the internally omitted biological
chemicals (Testosterone and Cortisol) that modify the brain state.
Theorizing and Conceptualizing Investor Behavior under Sociological and Psychological
Forces.
This dissertation has its theoretical foundation on the behavioral finance. Therefore, in this part
key theories explaining the application of behavioral finance in the decision-making process are
discussed. This research collectively tried to explain the rationality and irrationality argument in
investor decision making and tried to explain the architecture of bounded rational investor effects on
asset prices behavior and market behavior through sociological forces of herding behavior and culture
traits.
Bounded Rationality Theory
Bounded rationality theory was first presented by Simon (1955) and give an alternative
framework for human decision making. According to this theory, individual decision making is an
outcome of both rational and irrational elements. The decision taken by investors are generally
adaptive and goal oriented (Jones, 1999). Kahneman (2003) provide a conceptual framework of human
8
decisions under the dual system of the human mind into intuition (system 1) and reasoning (system 2).
The first system is fast, emotional and effortless while the second system is relatively slower,
controlled and governed by rules (Tuyon & Ahmad, 2016). This theory solely fails to explain the
origin, cause, and effects of human behavior on decision making, rather it explains only the rational
and irrational component of the decision-making process (Ahmad, Ibrahim, & Tuyon, 2017).
According to literature, herding may occur due to the bounded rational behavior of investors because
their thinking process is affected by the variety of biases and follow a likely available rule of thumb
(Brunnermeier, 2001).
Prospect Theory
Kahneman & Tversky (1979) presented the prospect theory as an alternative model of decision
making under risk and uncertainty as the expected utility theory fails to explain the decision making
under risk and do not reflect the actual human behavior. This theory provides an alternative explanation
to the investor bounded rationality specifically under the uncertain situation. This theory evaluates
individual choice process under two phases, one is framing and the other is evaluation (Tuyon &
Ahmad, 2016). In the first phase, the individual frame a representation of acts, and outcome relevant
to the decision outcome. In a later stage, individuals assess all the available prospect and make choices.
Many scholars check the validity of applicability of this theory to asset prices like (Kliger &
Kudryavtsev, 2008; Hung & Wang, 2005; Barberis, 2013). According to this theory, the investment
decision making is influenced by some psychological factors that diverge them from rationality as
supported by the argument of bounded rationality as argued by Simon’s (1955) These psychological
factors are behavioral biases like herding behavior and could lead to decrease in investment returns
(Aggarwal, 2014). Therefore, in spite of following the rational logical processes for investment
decisions, investors generally make decisions based on these biases that exist in their mind.
9
Theory of Mind
The existing theories of behavioral finance, namely prospect theory and Bounded rational theory
fails to explain the origin and cause of rational and irrational element in individual minds. Schlinger
(2009) & Jurist (2010) discuss the complexity of the mind and give an integrated framework of the
theory of mind based on the dual system of human minds as described by the neuroscience perspective.
Recently, cognitive science presented two systems of Human minds (Evans, 2003) namely cognitive
and effective system. The former one is linked with the beliefs, knowledge and intentions, and the later
one system that is accounted for emotions, sentiment, and mood (Poletti, Enrici, & Adenzato, 2012;
Alos-Ferrer & Strack, 2014). This system help in understanding the rational and irrational component
of the human mind and how they react in financial markets (Berlin, 2011). Several researchers applied
this theory to explain the fickle nature of financial system during the financial bubbles (De Martino,
O’Doherty, Ray, Bossaerts & Camerer, 2013). This theory can be applied to explain the irrational
herding behavior in stock markets specifically during extreme market stress and the surprise
component of fundamental announcements when they hit the global financial system.
The ABC Model
The theory of mind is one step ahead of the bounded rationality and prospect theory as it explains
the origin of decision-making process but fails to explain the causes and effects. The ABC model is
psychology based theory adopted to explain the cause and effects of irrational behavior on stock market
activity. This theory was developed by clinical psychologist Dr. Albert Ellis in 1950s. According to
this model, human behavior is an outcome of what people think and believe about themselves, other
individuals in the society and the world in general. The human system can be understood through ABC
system. Where A is the activating event, B is the core belief and C is the behavioral consequence
(Behavioral anomaly) (Ellis, 1976, 1991). More recently Brahmana, Hooy, & Ahmad (2012a, 2012b)
applied this theory to conceptualize the irrational behavior of investors in the stock markets through a
10
psychological factor of mood. This theory can provide a helpful insight in explaining the sentiment-
driven herding behavior in a large set of global economies.
Conceptualizing
The above discussion provides two insight into the decision making the process of investors.
The origin of human behavior stems from the psychological factors. These psychological factors are
classified as both cognitive heuristics and affective biases (emotions, moods, and sentiments). These
biases along with sociological factors like herding and cultural traits cause the irrational investment
decision. These irrational investment decisions can be in the form of information use, investment
analysis, designing strategies and portfolio management strategies. If these strategies are affected by
the sociological and psychological factors, then they can ultimately affect the investment performance
and can result in an unfair determination of price and returns and exacerbated volatility due to increased
risk.
Figure 1-2: Conceptualizing the Irrational Investor behavior (Source: Tuyon & Ahmad, 2016)
11
Motivation of Study
Over the last few decades, the importance of studying investors’ behavior has increased.
Specifically, when the associated impact of behavioral patterns with the stock prices is observed
(Blasco et al., 2012). It occurs when the traditional models fail to translate certain events (e.g bubbles)
in financial markets and the researchers try to consider the role of behavioral finance in explaining the
mechanics of investor decision making and its impact on stock prices.
During 90’s financial markets around the globe faced several financial crises. This poses a
question against the fragility of the financial system in front of economists and policymakers.
Researchers in the era realized the role of behavioral components associated with such episodes
(Devenow & Welch, 1996; Shleifer, 2000; Brunnermeier, 2001; Hirshleifer & Teoh, 2003). Cipriani
& Guarino (2008) argue that although the fundamentals are used to predict the episodes of crisis, the
probability of hitting the crisis exists for the country with sound macroeconomic fundamentals and
country with weak economic indicators. Fenzl & Pelzman (2012) explore the incompetency of
traditional theories in explaining and predicting the trends in financial markets and highlighted the role
of social and psychological forces working motivating the investor behavior. Economou et al. (2011)
claims that behavioral factors are closely linked factors behind the global financial crisis and
investigates the role of herding behavior during the Global Financial crisis of 2008 in four
Mediterranean stock markets. International capital markets and particularly emerging markets exhibit
more herding tendency (Teng & Liu, 2014). Increased pace of capital flows makes the global markets
more closed and vulnerable to shocks and crisis, which in turn increases the chances of contagion
under circumstances (Fischer, 1998).
Most of the previous literature is concerned with determining the fundamental linkages to this
crisis situation (Forbes & Rigobon, 2000). This research is motivated by the linkage that is not
explained by the fundamental one, rather it is a result of irrational investor’s behavior. Increase in risk
aversion, Financial panic, and herding behavior are examples of this linkage known as Pure contagion
12
(Chiang et al., 2007). When an economy faces a shock, liquidity constraints compel them to take out
funds from the domestic and other markets due to the difference in economic fundamentals. This type
of trend is not only followed in domestic market but also in the foreign market and leads to shock that
is transmitted like a contagious disease (Teng & Liu, 2014).
With the emergence of behavioral finance little attention is paid to its linkage with culture.
Guizo, Sapienza, & Zingales (2006) studied that the economic outcomes might be affected by the
culture. The practices of finance are also affected by the culture of a society and an emerging field
called “cultural finance” has established itself, which was titled by Breuer & Quinten (2009). Culture
has gained the interest of academicians (Chui & Kwok, 2008; Breuer, Riesener, & Salzman, 2012). For
example, Chui & Kwok, (2008) investigate the impact of national culture on life insurance
consumption. Similarly, several authors document the role of culture in the development of financial
markets (De Jong & Semenov 2002; Dutta & Mukherjee 2012).
The relation of herding behavior and its impact on the stock market has been greatly explored
in literature but the focus of this research is to create a linkage between behavioral finance and cultural
finance. Culture and its effect as a variable have been studied in the literature (Chiang & Lin, 2015).
This study is unique in a way that it utilizes cultural dimensions as a cross-country determinants of
variance (as modulating variable) that change the magnitude of herding behavior in a cross-country
analysis.
This explanation makes the behavioral consideration relevant, as the financial decisions cannot be fully
explained under the traditional financial paradigm. Investors are prone to psychological biases and
affected by the social interactions. Thus, motivated by the above-stated argument this study in its
complete framework try to bridge the gap in the literature by investigating the impact of fundamental-
based shocks and sociological influences on the investor herding behavior.
13
Research Gap:
This study draws following conclusions from critical theoretical and empirical literature review. There
are several questions raised in herding behavior research.
1. In a cross-cultural research, it is worthy to notice the differences in national culture especially
when investigating the herding behavior of an investor. National culture dominates the
investment decision making followed by their behavioral reaction to an information shock.
Therefore, in international stock markets, differences in national culture may provide some
insight in explaining the herd behavior of an investor. For example, eastern and Confucian
culture emphasizes ethics, humanism, obedience, and collectivism, indicating that a society
may have a low individualism, high power distance, low uncertainty avoidance, high
masculinity, and low long-term orientation. On the other hand, western culture is based on
reality, science, individualism, and happiness. Based on the ideals of human fundamental rights
that may prevent the emergence of herding behavior in developed markets. The purpose of this
study is to investigate the impact of culture on herding behavior of an individual investor and
to make a comparative analysis of this behavioral trait in three different cultures, Western
culture, Eastern culture and Confucian culture. This study is a contribution to the literature by
taking in to account the relationship between cultural dimensions and herding behaviors in a
cross-cultural research. Chang and Lin (2015) utilize five cultural dimensions to test the impact
of culture on herding behavior of an investor. This study in addition to dimensions used in
Chang & Lin (2015) utilize indulgence vs restraint to check the impact of culture as a
modulating variable on the intensity of herding behavior which is an added contribution.
2. Chiang et al. (2013) emphasize on the utilization of nonlinear reaction of investor behavior to
extreme market condition during large price swings. This study is an attempt to investigate this
behavior in states of market returns and volatility conditions. As Chiang et al. (2013) indicate
that the omission of these state influences in an empirical model is likely to result in inaccurate
14
inferences regarding herding. The financial literature provides ample evidence of stock price
dynamics (Orléan, 2004; Akerlof & Shiller, 2009). Lux (1995) argues that market dynamics
and clustered volatility cannot be explained through rational bubbles, stationarity or rational
agent models, they explain the market dynamics through herding behavior and researchers
attest that stock prices continue to be abnormal and largely irregular in recent years (Blasco et
al., 2012; Chen, 2013). In a period of market stress, structural changes occur and constant
coefficient model fail to explain market dynamics (Balcilar & Demirer, 2014). When the
market undergoes extreme stress, structural changes are likely to result, and constant
coefficient estimators do not provide updated information to reveal market dynamics (Balcilar
et al., 2013). Therefore, this research is an attempt to identify nonlinear dynamic herding in a
large set of developed, emerging and frontier market.
3. Most of the research has documented the effect of unexpected variations in macroeconomic
variables or uncertainty shocks on stock market prices and volatility (Fama, 1981;
Wasserfallen, 1989; Pearce & Roley, 1985; Hondroyiannis & Papapetrou, 2001; Gjerde &
Saettem, 1999; Bloom, 2009). Messis & Zapranis (2014) find this effect in developed markets
of France, Germany, UK, USA, and China. This study motivates us to investigate the similar
effect to a large number of economies and contributes to the empirical literature by examining
the effect of unexpected component of macroeconomic variables on the correlation between
the US and other economies in a sample.
4. Previous research work like Coudert et al. (2011) examine the volatility spillover effect during
the financial crisis but ignore the pure linkages of contagion effect and the factors that
contribute to this effect. This study is an attempt to fulfill this gap by examining the contagion
of herding in the presence of adverse macroeconomic shocks and financial market crisis.
Messis & Zapranis (2014) conclude that mechanism of Chinese stock market fails to protect
investors from the contagion of herding in a limited set of developed economies but ignore the
15
shift of contagion in developing economies. This research is a contribution towards identifying
countries in a large set of economies with lower effect of pure contagion to enjoy full benefit
of portfolio diversification.
5. This research work provides a complete framework of analysis in developed, emerging and
Frontier markets of Asia, Asia Pacific and Europe, where economic, cultural, behavioral and
external factors are simultaneously integrated to provide a comprehensive analysis of herding
behavior. Therefore, in its unique design, this research is an attempt towards bridging a gap
between, cultural finance, behavioral finance, and standard finance.
Statement of the Research Problem
Current literature on herding behavior is mainly concerned with the identification of herding and its
market specific determinants (Tan et al., 2008). Individuals are prone to psychological biases that
influence their financial decisions; it is almost inevitable that their views of the world, as determined
by culture, play a role in how they act in financial markets (Shefrin & Statman, 2000). More
specifically in correlated markets, this mimetic behavior can lead to crisis spillovers or contagions in
adverse market conditions (Chiang et al., 2007). The recent turmoil in the global stock markets provide
reasons to believe that other than fundamental reasoning certain sociological factors and actions of
noise traders could have destabilized prices and pushed prices away from fundamentals. One of the
main underlying factors can be the herd behavior of investors. Therefore, there is an immense need to
identify imitating behavior of an investor particularly in the presence of diverse sociological (cultural)
and adverse macroeconomic (shocks and crisis) environment especially when markets are closely
linked, in a large set of developed, emerging and frontier markets of Asia, Asia Pacific and Europe.
16
Research Questions:
1. Does herding behavior of investor exist in Developed, Emerging and Frontier Markets of
Asia, Asia Pacific and Europe?
2. Which of the two measures (return dispersion or state space models) identify herding behavior
best in developed and emerging and frontier markets of Asia, Asia Pacific and Europe?
3. Does external environment like culture affect the irrational behavior of investor?
4. What are the domestic and cross-market determinants of dynamic herding?
5. Do macroeconomic shocks have an impact on the estimated herd measures towards the
market?
6. Whether contagion of herding exists between the selected countries when adverse economic
conditions prevail in a specific country?
Objectives of Study:
The purpose of this research is to
1. To investigate the existence of herding behavior in Developed, Emerging and Frontier Markets
of Asia, Asia Pacific and Europe.
2. To make a comparative analysis of return dispersion and state space models.
3. To investigate the impact of national culture on herding behavior of irrational investor.
4. To find out determinants of dynamic herding behavior.
5. To examine the effects of macroeconomic shocks on the estimated herd measures towards the
market.
6. To examine whether contagion of herding between the selected countries exists when adverse
economic conditions prevail in a specific country.
17
The significance of the Study:
Over the past few decade, behavioral analysis have attracted the attention of both academic
researchers (Thaler, 2003; Shefrin’s, 2001) and practitioners (Sewell, 2005). Researchers of behavioral
finance have turned their focus towards the behavior of actual market participants from the traditional
models of behavioral finance that integrate ideas from psychological theories.
Theoretical Contribution:
This study has some theoretical implications for academicians. They are listed below.
For Risk management and Investment Decisions.
It is important to understand that how investors react in the presence of herd behavior in financial
markets and how they react to shift in trends and manage their portfolios. Above all how the risk affects
the expected return on investment.
According to economic models of portfolio theory, equity premium is the main reason of
participation of individual investors in the stock market (Hodgson, Breban, Streatfield, & Urwin.
2000). Theoretical models suggest that individual portfolio choice is derived by maximizing an
expected utility function conditional on their preferences and the rate of risk aversion is an important
parameter (Breuer et al., 2012). According to Markowitz model of portfolio theory, the individual risk
aversion parameters designs their choices between risky and riskless assets (Markowitz 1952). Risk
aversion is termed as a preference for a certain outcome over a prospect with an equal or greater
expected value (Tversky & Kahnemann 1981). Thus, according to theory more risk-averse usually
invest less in risky assets (Guiso, Haliassos, & Jappelli 2003). It is evident from empirical literature in
actual investor participate less in markets as implied by the standard portfolio theory and it can be
attributed to the inadequacy of the standard assumptions (Campbell, 2000).
In Behavioral finance evident irrationality of investor is explained through certain
psychological factors that give rise to the investor’s portfolio diversity (Shiller 2003). Heterogeneity
18
in beliefs can be an outcome of intrinsic differences (Pompian, 2012). In a situation of high uncertainty
and complexity investors usually, ignore fundamental information and rely on their intuitions
(Kahneman & Riepe 1998). These intuitions give rise to several systematic errors and correlated
behavior in markets where investors overestimate their knowledge and underestimate risk and are
unable to control events (Giordani & Söderlind, 2006).
The psychology of an investor in a risky environment can be explained through sociological
forces like herding behavior and culture of a society that can provide a causal linkage from culture to
economic behavior (Guiso, Sapienza, & Zingales, 2009). The traditional relationship between risk and
return explained by the Capital Asset Pricing Model provides the basis for understanding the dynamics
of herding behavior on risk diversification. Chiang & Zheng (2010) stated that herd behavior describes
the increased correlation in trades due to the interaction of investors in stock market. So, in the
presence of herding behavior, a large number of securities are required in a portfolio to achieve the
same level of diversification due to the existence of an inferior degree of correlation. Herding has
negative implications in portfolio diversification, if there is a strong co-movement between stocks, the
benefit of diversification is eliminated as the stock prices tend to move in unison.
For Behavioral Asset Pricing Model
The risk involved in the pricing of investment under the current scenario has both fundamental
and behavioral components. Therefore, it is deemed necessary to incorporate behavioral factors like
herding while modeling the asset prices. As this research validates the effect of herding behavior and
its consequences on securities returns, therefore new asset pricing models suggested by several authors
should be utilized. For example, the behavioral asset pricing theory proposed by Shefrin and
Statman(1994), affects behavioral asset pricing model by Statman, Fisher & Anginer(2008) and
investor psychology in Hirshleifer (2001). By improving the information disclosure trading activity
of investors can be improved. This informational efficiency minimizes the risk of securities as prices
are determined on the basis of fundamentals. Better knowledge of market dynamics and clear
19
understanding of factors behind irrational behavior could result in more accurate valuation, estimation,
and forecasting decisions.
For behavioral Portfolio Management
In a recent study Cuthberston, Nitzsche & O’Sullivan (2016) discuss the effect of behavioral biases on
fund managers, fund governance, and institutional structure. Therefore, the behavioral factor like
herding behavior can be easily incorporated into device investment analysis and maintain funds’
portfolios. The behavioral portfolio theory is given by Shefrin & Statman (2000) illuminates the need
to manage the risk arise through behavioral factors and its effect on investment portfolio returns, their
selection and diversification. Bollinger (2008) recommends adopting a combination approach by
utilizing the technical, fundamental, quantitative and behavioral factors collectively in order to design
management investment strategies. Along with behavioral approach, they can also incorporate the
effect of social influences like national culture. Policymakers should focus on the communications
channel with financial markets and manage the prospects of fluctuations in policy because herding can
aggravate in financial market due to change in macro information.
For culturally Diversified Markets
This study selects Developed, Emerging and Frontier Markets of Asia, Asia Pacific and Europe.
Selection of these markets is based on two major reasons. First, this research is an attempt to make a
comparison of investor behavior between developed and emerging and frontier market. There are few
studies available where large number of markets are incorporated to test the herding behavior (Chiang
& Zheng, 2010; Chiang & Lin, 2015). In these studies only return dispersion model is employed, mean
risk based model of herding is ignored. While, Chen et al. (2013) incorporate both measures (return
dispersion and state space model) to test herding behavior in a large set off economies during a period
from 2000 to 2009 by employing firm specific daily returns data. This research added to empirical
literature in two ways, one by investigating herding in 31 stock markets using both measures but with
a larger data set with varying time length, secondly by employing industrial indices following the
20
approach of Chiang & Zheng, (2010). Finally, this study contributes to the literature by making a
comprehensive analysis of culturally diversified markets. This contribution is a major focus of this
research.
Cross country investigation of investor behavior is incomplete if we ignore differences in
national culture. In a society people combine their values and conventional belief with their behavioral
norms to form a national culture. When applied to the financial market, investor reaction to information
arrival and the decision-making process is dominated by a culture of a society. Further, if a large
number of investors follows same investment strategy then this behavior can be translated into herd
formation. Therefore, the difference in national culture can help understanding the reasons behind
herding behavior. Hence, this study includes culture as an important factor in investigating herd
behavior in international stock markets.
Breuer & Quinten (2009) explain the advantage of incorporating cultural finance literature to
behavioral finance by saying “although Behavioral Finance assumes bounded rationality to be valid for
most countries, Cultural Finance implicitly supports the diverging relevance of certain behavioral patterns
between countries, and thus rationality defects”. Therefore, when an investor make cross-cultural
investment decisions, change in behavior become significant as we move from one society to another
due to social influence. Hence, one cannot ignore the role of culture in financial markets.
Unexpected macroeconomic environment and contagion of herding
A number of studies are available where researchers check the impact of unexpected components
of macroeconomic variables or uncertainty shocks like terrorist attacks on stock prices and volatility
(Fama, 1981; Pearce & Roley, 1985; Gjerde & Saettem, 1999; Bloom, 2009). Similarly, Messis &
Zapranis (2014) empirically investigate this impact on selected macroeconomic shock on herding
behavior of some developed economies towards Chinese stock market. This study motivates us to
investigate the similar effect to a large number of economies variable where this study contributes to
the empirical literature by examining the effect of an unexpected component of macroeconomic
21
variables on the correlation between US and other economies in a sample. In order to figure out the
impact of certain macroeconomic shocks, this study employs Box-Jenkins methodology.
Financial contagion refers to transmission of crisis from one market to the other in different
locations (Corsetti, Pericoli & Sbracia, 2011). This is due to movements in asset prices in these
markets. Authors report two reasons for this contagion (Edward, 2000). Fundamental based contagion
where economic, financial, and trade linkages are the cause of this spillover effect. Second is the
behavioral approach where herding behavior is also responsible and plays important role in explaining
this contagion. This study is an attempt to separate this effect and examine financial contagion due to
herding behavior.
Practical Contribution
This study has several practical implications for practitioners and policymakers. Few are listed below
For Individual Investors
With the emergence of behavioral finance, few governments are designing a wide variety of programs
to provide financial and economic education to the general public and individual investors. The basic
purpose is to educate those individuals that are practically excluded from the formal financial sector.
They practically lack financial knowledge and make poor investment decisions. If these investment
decisions are influenced by the herd formation can lead to a huge disturbance in the financial markets.
Few researchers evaluate the impact of such program on subsequent financial behaviors (Lusardi,
2008; Lyons, 2010). The underlying benefit of these programs is based on lifecycle models and the
assumption of rational expectations, and the investors after this training sessions make optimal
decisions regarding saving and investment. Therefore, if the government of less sophisticated markets
provides financial education to the investors the problem of irrational decision making will resolve.
For Institutional Investors
22
Institutional investors and particularly fund managers can get benefit from this study. As
origin, cause and effect of behavioral factor like herding could be utilized to device investment analysis
and related management strategies to implement certain decisions under the positive or negative
environment under the effect of this bias. Shefrin (2000), & Montier (2002) stress on the utilization of
behavioral biases in investment analysis to overcome the consequence inefficiency and fragility of the
financial system. The strategies devices by Baker & Riccardi (2014) can be utilized to get the full
benefit of optimal decision making.
For International Investors
This research has implications not only for the local investor but also for the international investor.
If the herding measures are correlated between two markets especially with global leaders then the
benefit of portfolio diversification is reduced as the same risk factors are transmitted across the markets
due to volatility spillovers or contagion of herding. This research has implication not only for the local
investor but also for the international investor. If the herding measures are correlated between two
markets then the benefit of portfolio diversification is reduced.
As economies are integrating, there are several trade ties and mutual benefits associated.
Therefore, while making investment decisions, both in local and foreign markets. A better
understanding of culture can help them achieve a sound decision. According to Shefrin (2000), an
investor should be well aware two things, one is his own “investment mistakes” and other is his
counterpart “error of judgment”. Therefore, by using this research, investors can educate themselves
about certain behavioral biases they are likely to be affected by and can take better steps to avoid them
and make decisions efficiently.
For Financial Market Regulators
This paper has highlighted one of the behavioral risk (herding behavior) stressed in Daniel
Hirshleifer & Teoh (2002). They suggest two steps to improve the public policy, one is designing of
such policies that can minimize the errors and second is to improve the efficiency of the market. Thus,
23
the regulation of this risk factor will reduce the impact of irrational behavior and market imperfection.
Similarly, Suto & Toshino (2005) stress the need for governance of financial markets against the
behavioral risk factors. Asian markets are more vulnerable to sociological and psychological
inclinations as suggested by the (Kim & Nofsinger, 2007). Therefore, there is a need to frame the
financial regulations under the fund management industries in the markets of Asia. In the era of
globalization and transmission of shocks through behavioral channels has been validated through this
study, therefore there is an immense need to design global governance framework that will help the
international investors to play safely in the global financial market.
Organization of the Study
The basic structure of the thesis is given as follows:
Chapter 1 of the study includes a brief introduction, which outlines the purpose of research, the
gap in the literature, problem statement, main questions raised, objectives of the study and contribution
of the study (theoretical and practical).
Chapter 2, present a brief review of literature that includes the theoretical background and
framework of herding behavior and discusses the role of, culture and macroeconomic uncertainty in
investor herding behavior and how it affects the pricing mechanism underlying EMH. This section
also includes different models of herding, culture and its role in finance, its recent development and
discusses the relationship between behavioral and cultural finance.
Chapter 3, represents the research design, which includes the required data and methodology.
This section makes ways of linking the problem statement with outcome of the study.
Chapter 4, present results and their discussion in the light of theory and previous empirical
findings.
Chapter 5 provide the conclusion that includes summary of findings, implications of the study,
limitation of this research and recommendations for future research.
24
Chapter 2
2. Literature review and Theoretical Framework
Literature review
Traditional Finance:
In the past thirty years, standard theories of finance face certain criticism on their applicability. As a
reaction behavioral explanation and theories emerged. Therefore, there is the immense need to
understand the classical paradigms of economics and Finance. This part of literature will throw light
on the foundations of standard finance from the perspective of individual behavior, their decision-
making process in financial markets and the resultant outcomes. In order to explain standard finance
two subsections are developed. In first section, this study discusses the rational preference of
individuals, the second section highlights the importance and implications of the efficient market
hypothesis.
Rational preferences
The rational expectation model is based on certain assumptions, these are “ 1) Individuals have
rational preferences, 2) they maximize their utility, 3) they make independent decisions by using
information” Ackert & Deaves, (2009).
According to Burton & Shah (2013), an investor is said to be rational, who make informed
decisions by making choices among certain alternative and given constraints in order to maximize their
utility. They further elaborate that according to utility theory, each individual owns a utility function
which is a representation of their investment choices that makes them better off.
25
Efficient Market Hypothesis:
For more than thirty years, the efficient market hypothesis is considered as the building stone
of traditional financial system and a major investment theory. Since 90’s, financial economists widely
accepted this approach. Fama (1970) defines an efficient market as a market where active current
information is accessible to all market participants and market value of the individual security is
predicted by a large number of actively competing for rational profit maximizers. Individual and
aggregate stocks are efficient if they truly reflect all available information and with the arrival of news,
information is instantly incorporated in security prices (Malkiel, 2003).
About a century ago, Bachlier (1900) link the movement of stock returns to Brownian motion
while studying random processes in mathematical theory. After about fifty years this idea is elaborated
by Kendall(1953), he postulates that the commodity and stock prices follow a random walk. Cootner
(1964), Samuelson (1965), and Mandlebrot (2001) further supported this argument.
An efficient market is based on the notion of “random walk” or “fair game”. Former is based
on the idea that in an informationally efficient market future movement of the asset is independent of
the past movements. Whereas, later suggests that stock market speculation is a fair game, in which
overall none of the players gain a net profit (Bachelier, 1900).
Fama (1970) defines an efficient market hypothesis as a market, where all available
information is truly reflected in asset prices. He formally defines market efficiency in three levels
associated with the ability of a market to process information reflected in a stock price i.e., Weak form,
semi-strong form, and strong form efficiency. Weak form implies a random walk and prices
incorporates all historical information, the semi-strong form includes publically available information
which is reflected in stock prices of a firm, whereas strong form implies that prices reflect all private
and public information at any given time. Therefore, according to Fama (1970) market is efficient if
available information is classified as past intelligence, public intelligence, and privileged intelligence.
26
EMH has its origin from Fama (1965) and Fama (1970). The rational expectation based theories
are later quantified by Sharpe (1964) named as Capital Asset Pricing Model (CAPM), Arbitrage
Pricing Theory (APT) by Ross, 1976 and Black Scholes model of option pricing by Black (1972).
Both EMH and CAPM are interlinked as the CAPM is a test of EMH.
2.1.2.1 Empirical support for the efficient market hypothesis.
In 60’s efficient market hypothesis got heavy empirical support relied on serial correlation tests
and other mechanical trading rules. Niederhoffer & Osborne’s (1966) pointed out the monopolistic
access of insider information by a specialist or corporate managers which makes a strong form of
efficiency questionable. The methodology adopted to check efficiency is also criticized later by
Sweenay (1988). Taylor (1982) replaced the testing methodology and argued that the other tests
negated the random behavior of asset prices because traditional methods are not suitable to check
efficiency when there are trends. Instead, the author supported a price trend hypothesis. Studies prior
to 1970’s conclude that the markets are in general weak-form efficient.
According to Fama (1970), ample evidence supports the weak form and semi-strong form
efficiency whereas partial support is found for strong form efficiency with the exception of two papers
(Niederhoffer & Osborne’s, 1966; Schole, 1969). Similarly, Rendleman, Jones, & Latane (1982) find
out the reaction of the stock market to the quarterly earnings announcements. These findings support
the EMH as prices are adjusted back to equilibrium after the arrival of new information.
In modern literature, several studies validate the efficacy of efficient market hypothesis. Palan,
2004 finds efficiency in the valuation of stocks and options. Similarly, Timmermann & Granger (2004)
analyze the EMH from the perspective of a modern forecasting approach. Lo & Hasanhodzic, (2010)
examine the random and unpredictable behavior in Stock. Allen, Brealey & Myers (2011) analyzed
the assets of blue-chip companies and find a weak correlation in consecutive days returns, concluding
the non-predictability of future outcomes. While Wilson & Marashdeh (2007) explain that the co-
27
integrated prices are consistent with EMH in the long run and inconsistent in short run. Similarly, Yen
& Lee (2008) presented a survey article that gives a chronological account of empirical findings and
concludes that evidence in favor of EMH still exists.
2.1.2.2 Critiques of the Efficient Market theory
In 1978 a special issue of Journal of financial economics was published that was designed for
the anomalies related to the efficient market hypothesis. In the editorial note Jensen (1978) elaborates
that although there is strong empirical evidence supporting EMH, there is evidence that is inconsistent
with the theory and cannot be ignored. They also raised question against the joint hypothesis. Ball
(1978) pointed out the inadequacy of asset pricing model which give rise to anomalous evidence.
Similarly, Thompson (1978) discusses that the abnormal returns are attributed to the inadequacy of
asset pricing model. Chiras & Manaster (1978) find the Chicago Board of Exchange (CBOE) market
inefficient due to the presence of abnormal profit for both member and non-members of CBOE.
According to literature, there are several anomalous findings in testing the joint hypothesis of
EMH and asset pricing model in equilibrium. It includes season effect of stock returns (Rozeff &
Kinney, 1976), January effect (Haugen & Lakonishok, 1988).
Later, Fama (1991) provides a second review of market efficiency literature and expands the
efficient market hypothesis by testing returns predictability using variables like dividend-price ratio,
earnings-price ratio, book-to-market ratio and various measures of the interest rates. Semi-strong form
and strong forms of efficiency are tested using event study methodology. With no exception, these
revisions also faced several challenges in the later years (Mishra, 2011).
Recent empirical research efforts are carried out on equity markets for the validity of the theory
in the developed and emerging economies. Empirical studies show that EMH is applicable in the Asian
stock market (Kim, Shamsuddin, & Lim, 2011). Mishra (2011) does not support random walk model
28
for other developed markets (the US, UK, and Germany) and emerging economies (Brazil, India, South
Korea, China, Russia)
During Ball (2009) claims that the cause of the collapse of Lehman Brothers along with other
financial institutions during the global financial crisis was excessive faith in efficient markets that
resulted in market failure. Lee et al. (2010) examine the stationarity of real stock prices for a set of 32
developed and 26 developing economies and find that markets are inefficient during a period from
January 1999 to May 2007. Similarly, Eakins & Mishkin, (2012) investigated the strong form
efficiency and the results indicate market inefficiencies. Fakhry & Richter (2015) found increased
volatility and inefficiency during the recent financial and sovereign debt crises on the US and German
sovereign debt markets. Fakhry, Masood, & Bellalah, (2016) also find mixed efficiency during crisis
in GIPS markets. However, EMH is facing several criticism to date (Yao et al., 2014) and focus is
diverted to behavioral based anomalies (Hirshleifer, 2015).
Behavioral Finance
In reality, fundamental analysis based on efficient market hypothesis is unrealistic as it
completely ignores human behavior (Thaler, 1999). The hypothesis of rational investors and
instantaneous processing of information cannot be defined easily and is unrealistic due to unpredictable
human behavior. In order to overcome difficulties faced by the EMH a new paradigm emerged. In 90’s
a group of researchers with ample evidence against the EMH argues that this paradigm should be
replaced with the “Behavioral finance” approach (Thaler, 1999; Haugen, 1999; Shleifer, 2000). The
presence of anomalies and difficulty of traditional models to deal with the problems stimulates the
development of this approach. Behavioral finance is an emerging field of finance that explain how
psychological factors affect the behavior of an investor and influence their decision-making process in
financial markets that has an ultimate effect on asset prices. It serves as an alternative block of each of
the foundation block of standard finance model (Statman, 2008). According to Shefrin (2000),
29
Behavioral finance is “a rapidly growing area that deals with the influence of psychology on the
behavior of financial practitioners”.
Barber & Odean (1999) declare that “people systemically depart from optimal judgment and
decision making. Behavioral finance enriches economic understanding by incorporating these aspects
of human nature into financial models.”
Glaser et.al, (2004) describes behavioral Finance as a field of study that relates market
phenomena with individual behavior by using the wisdom from both financial theory and
psychological field. Therefore, behavioral finance uses behavioral biases to explain certain anomalies
of standard finance models and provide measures to overcome them.
Behavioral finance literature has two classifications. First is detection of anomalies and their
explanation through behavioral models (Bondt & Thaler, 1985) and second is identification of biases
or individual behavior that classical theories of rational behavior fail to explain. (Odean, 1998).
There are various viewpoints in the literature about behavioral finance. The basis of this
paradigm can be explained by understanding the limits to arbitrage, which argues that in a market less
informed traders cause a disturbance that cannot be easily corrected by the informed traders (Barberis
& Thaler, 2003). Therefore, investor irrationality has a long-term and significant effect on the asset
prices. In order to explain this irrational behavior and inefficiency in asset markets behavioral finance
extract empirical evidence from cognitive psychology, where biases are formed due to the preferences,
beliefs, and means in which decisions are made, (Barberis & Thaler, 2003). Schindler (2007) states
that behavioral finance is an integration of three distinct fields of studies, where we combine
psychology, sociology, and standard finance to come up with the idea of behavioral finance. Where
psychology explain that how people exhibit certain biases systematically while making their
investment decisions based on their beliefs and preferences. Sociology help to explain the fact that a
large number of decisions made by investors is an outcome of social interaction rather than taken in
isolation. It is against the assumption of EMH that decisions are made without any external influence.
30
Figure 2-1: Source: Schindler (2007)
Theories of Human Behavior:
In order to explain irrational investor behavior, few theories were presented by the authors derived
from psychology and sociology. Few of them are prospect theory and heuristics.
2.2.1.1 Prospect Theory:
Kahneman & Tversky (1979) are the founders of prospect theory and Daniel Kahneman was
later awarded Nobel Prize for economics. They are considered fathers of Behavioral Finance. Prospect
theory is based on the idea that people usually don’t always act rationally and under the condition of
uncertainty there are psychological biases due to certain psychological factors that influence their
choices.
The value maximization rule is different from standard finance. According to Prospect theory
gain and losses are considered rather than final wealth position. Schwartz (1998) postulates that instead
of the final state of wealth, investors normally evaluate their outcomes of losses and gains relative to
some reference points. Prospect theory establishes that people usually take riskier decisions if they
Behavioral Finance
Finance
SociologyPsychology
31
face a possibility of losing money with a goal of loss aversion. Therefore, according to prospect theory,
portfolio allocation is based on the highest prospective utility that is obtained by the computation of
potential gains and losses. According to this theory, the investment decision making is influenced by
some psychological factors that diverge them from rationality as supported by the argument of
bounded rationality by Simon’s (1955). These psychological factors are behavioral biases like herding
behavior and can lead to decrease in investment returns (Aggarwal, 2014).
2.2.1.2 Heuristics:
These are the simple efficient rule of thumbs proposed to explain the decision-making process
of investors, especially when available information is incomplete or problems are complex (Park
Sabourian, 2011). Tversky & Kahneman (1981) describe the effect of human heuristics on the
investor’s decisions and argued that heuristic can be simple judgmental operation substitute for
complex problem-solving. They defined it as a strategy that most of the times yield a right solution
when applied to a given set of problems. Brabazon, (2000) explain heuristic as rule of thumbs that can
be applied in an uncertain situation and environment for making decisions.
Behavioral Biases:
Behavioral finance is laid on a foundation that a significant number of investors are affected by
behavioral biases that make their financial decisions irrational. These biases usually applied to
financial problems come from cognitive psychology literature. Shefrin (2007) defined bias as “bias is
nothing else but the predisposition towards error”. Researchers distinguish a long list of behavioral
biases; almost fifty of them are applied in different studies. Few researchers classify biases as
heuristics, while others call them judgment, belief or preferences, but many other classify them along
cognitive or emotional lines. List of these biases is presented in Appendix 1. Out of these biases, the
32
main focus of this research is to investigate herding behavior. A thorough literature in this field of
behavioral finance is discussed in next section.
Herding Behavior:
Investor behavior in the market is often influenced by others. Investors usually adopt the
behavior of other by foregoing their own rational analysis. Herd behavior in investors leads to a
convergence of action (Hirshleifer & Teoh 2003). Christie & Huang (1995) defined herding as
“individuals who suppress their own beliefs and base their investment decisions solely on the collective
actions of the market, even when they disagree with its predictions”.
Herding can be best explained with an example given by Keynes (1936), an investor in the
market behaves like judges in a beauty contest, where the decision is based on others judgment rather
than on they actually think is the most beautiful. Shiller (2006) argues that herding is an irrational
behavior where investors imitate others blindly in the market. This is the most common and dangerous
mistake that is made by investors in the market while making an investment decision. The major
concern is not whether investor imitates others blindly but rather focused on why? To date, several
researchers tried to answer this question by separating true herding(intentional) and spurious herding
(Bikhchandani & Sharma, 2000; Holmes et al., 2013; Gavriilidis et al., 2013; Economou et al., 2015b;
Galariotis et al., 2015)
According to literature, there are two opposite views of herding: Rational and irrational
(Bikhchandani & Sharma, 2001). Rational view is based on the idea that optimal decision-making
process is sometimes altered by incentive issues or information difficulties. This type of herding arises
when investors decision follows Bayes rule by duplicating the decisions on others actions. According
to Badddeley (2010), the results of Bayesian models can be right only if the generated signal
transmitted down by the forerunners follow the right path. Hirshleifer & Teoh (2003) support this fact
by arguing that it is very natural to observe irrational behavior in rational settings. The irrational view
33
is due to psychological bias where investors follow each other blindly while making a decision in the
market. This type of herding arises when investors base their decisions on emotional, sociological or
Psychological factors. Therefore, it can be argued that investor behavior in financial settings can be
irrational rational. Whether this decision is a result of an emotional reaction to the information or due
to “Herd Instinct” in financial decision making.
Welch (2000) discusses several incentives available to the investor for exhibiting herd behavior
in the market. These incentives can be Sanction on deviants, utility networks, positive payoff
externalities, informational externalities, agent-principal payoffs and many. All of these incentives are
theoretically defined and discussed.
Theories of Herding Behavior
Prior research explains herding pattern in three different aspects. First set explain the
psychology related effects of herding, which is related to the contagion of sentiment, i.e., behavioral
aspect of herding. Goldbaum (2008) attributes herding to the psychology of investor where a sense
of security is obtained by following the action of the majority. This type of herding is irrational of
near rational. Pingle (1995) conduct an experiment to figure out reasons of herding, they conclude that
investors herd after a change in decision-making environment, mostly in a competitive environment,
especially when they are taking decision for the first time. Lux (1995) based their decisions entirely
on observations as the available information set is limited and reliance on least available information
can lead to herd formation. The market is dominated by winners and driven by optimistic traders, rest
of the pessimistic investors follow the actions of optimistic ones.
The second set is relying on the effect driven by information set and rely on a model of
informational cascades. The action of more informed investors generate information for the less
informed investors and this information set is more valuable for the followers (Shleifer & Summers,
1990; Calvo & Mendoza, 2000; Chari & Kehoe, 2004; Froot, Scharfstein & Stein, 1992; Avery &
34
Zemsky, 1998). In certain situations, investors largely based their decision on others judgment. It is
assumed that the previous decision taken exhibits private information of agent in the market. Use of
the available information is beneficial because the collection of private information can be time-
consuming and costly. This whole process ends up in herding behavior, where investor solely or to
some extent based their decision on available past information set (Banerjee 1992; Welch, 2000;
Bikhchandani, Hirshleifer, & Welch 1992).
The final set of research is concerned with the reputational concern of manager that cause a
principal-agent problem. According to Morck, Shleifer, & Vishny, (1990), managerial performance in
an industry is evaluated on the basis of firm performance relative to overall industry and a bad
performance can lead to the firing of top management. Hence a manager with reputational concern
blindly follow the evaluation and forecasts of others and show his ability as an efficient agent by
imitating the footsteps of others. This type of herding is rational, utility maximizing, conscious, and
exogenous (Parker & Precter, 2005). Several authors provide evidence on the basis of reputational
concern of managers where they explain different kinds of agents. Scharfstein & Stein (1990); Zwiebel
(1995); Trueman (1994); Graham (1999). More recently, (Blake et al., 2015; Broeders et al. (2016)
investigate this type of herding in buying/selling of stocks, bonds, and other asset classes.
Models of Herding Behavior
In literature, empirical methodologies on herding are classified into two major categories
(Spyrou, 2013). The first type depends on microdata and focuses on a herding of a specific investor
type such as institutional investors. Lakonishok et al. (1992) and Sias (2004) present model to measure
this type of herding. The second type is, herding toward market consensus and is related to investor
type that relies on collective price and market activity data and is focused on the behavioral aspect of
herding. This type of herding is measured by Christie & Huang (1995), Chang et al. (2000) and Hwang
& Salmon (2004).
35
Herding based on microdata is modeled by Lakonishok et al. (1992; LSV hereafter) and Sias
(2004). The basic idea behind LSV measure is that if a money manager can sell (buy) excessive
individual stocks then there exist herding behavior to the extent of that particular stock. The herding
is calculated as the ratio of net holdings (net buyers of the particular stock) to total institutional stock
holdings in that period excluding the adjustment factor due to the rise of a number of active buyers. If
there is herding then there is a cross-sectional increase in expected returns of the above metric, and in
the absence of herding, no expected value remains same.
Sias (2004) is second in the row to extend the model for institutional herding. They argue that
the number of institutional buying in each quarter will vary from one time to the previous time. It
means that institutional holdings will vary among cross-sections. If an investor follows this own
actions by trading in or out of the same assets then herding behavior can be measured by cross-sectional
correlation of demand of assets between times under consideration. This methodology evaluates the
position of institutional investors holding by the fraction of outstanding demand in each quarter. In
each time period position of the holder can be estimated as, If holdings decreases (increases) then the
investor is a seller (buyer).
The difference between two measures is that Lakonishok et al. (1992) measures cross-sectional
position indirectly, while Sias (2004) provides a direct measure of institutional investors herding
behavior among cross sections.
Herding towards market consensus is measured by return dispersion and state space models.
The first dispersion model is developed by Christie & Huang (1995), where they used cross-sectional
standard deviation (CSSD) on daily and monthly data and supports the prediction of asset pricing
model. He further concludes that herding is not an important determinant of asset pricing mechanism.
Future studies based on this model find mix results on the existence of herding behavior in extreme
market condition. Demirer & Kutan (2006) tested Chinese market and find no evidence of herding by
employing the same model. The major drawback of this approach is to find evidence of herding in
36
extreme returns. According to empirical literature, herding behavior is usually observable during stress
periods but it may occur over the entire distribution (Christie & Huang, 1995). Therefore, this model
only captures herding in extreme events and fails to find out herding over time.
The second model of dispersion is presented by Chang et al. (2000). According to this measure,
during extreme market conditions return dispersion and market returns exhibit a nonlinear relationship.
The quadratic factor of returns in the model gives negative and significant results in the presence of
herding behavior. They investigate US, Hong Kong, Japan, Taiwan and South Korean markets and
find partial evidence of herding in Japan and significant herding behavior is observed in two emerging
markets of Korea and Taiwan. In both countries presence of herding is attributed to the presence of
speculative investors, the inefficiency of reliable company information and lots of government
intervention.
The second type of methodology of measuring herding is based on state space model proposed
by Hwang & Salmon (2004). This method focuses on the cross-sectional variability of factor
sensitivities. This particular model is chosen in this study for a number of reasons. First, the method
considers herding as a measure of the behavior of investors who track the performance of specific
factors, styles or macroeconomic signals, and thus proceed on buying or selling individual assets at
the same time disregarding their underlying risk-return relationship. Second, state space models allow
the detection of herding both in the normal and extreme market and provide a comprehensive analysis
of herding over time (Demirer et al., 2010). Finally, the model is free from the impact of idiosyncratic
factors as it focuses only on the variability of factor sensitivities (Hwang & Salmon, 2004). Chen
(2013) investigated herding towards market consensus by employing all three models in global stock
exchanges.
Empirical Evidence on Institutional Investor Herding
Lakonishok et al. (1992) examine positive feedback trading and herding using data of mutual
funds on US market and find no evidence of herding of pension fund managers. Only partial herding
37
is observed in smaller stock holdings. Similarly, Grinblatt et al. (1995) investigate momentum and
herding in mutual fund industry of US market and find evidence against herding but in favor of herding
behavior. Wermers (1999) also find same behavior in US mutual fund industry. Nofsinger & Sias
(1999) investigate herding in the US and find the positive relationship of annual net institutional
holdings on returns from a period of 1977-1996. They conclude herding is present in the market due
to the greater impact of institutional investors on prices than individual investors. Kim & Wei (2002)
find the comparatively higher level of herding in institutional investor than an individual investor and
this effect is more pronounced in foreign investors. Li et al. (2016) investigate the behavior of Chinese
institutional and individual investors and find a high level of herding in informed institutional
investors. Lobão & Serra (2006) find significant institutional herding in Portuguese market using LSV
(1992) model. Similarly, Patro & Kanagaraj (2012) also employed LSV (1992) model in India mutual
fund industry and observe significant herding compare to developed markets.
Sias (2004) find herding as a result of institutional investor imitating behavior in trade of
securities over time and momentum strategies play a role in investor herding behavior. Li & Yung
(2004) find similar behavior in American ADR return. Strong evidence of institutional herding
behavior is observed in Choi & Sias (2009) model. They observe almost 39% correlation in the
institutional holding of one quarter to another quarter. Similarly, Guiterrez & Kelley (2008) find the
higher intensity of herding behavior in investor purchases and conclude that prices destabilize due to
buy herds and revert back to equilibrium due to sell herd formation. Kim & Nofsinger (2007) observe
that Japanese investor exhibit low level of herding than the US institutional investor over the cross
sections. Iihara et al. (2001) found herding of institutional herding in the Japanese market in yearly
interval ownership holding data. Chang & Dong (2006) also observe similar results by relating herding
to an idiosyncratic component of volatility. The presence of institutional herding with industry effect
is also documented well in international markets, (Voronkova & Bohl, 2005; Zhou & Lai, 2009; Choi
38
& Sias, 2009; Gebka & Wohar, 2013; Gavriilidis et al., 2013). Other studies include (Andreu et al.,
2009 & 2015; Chaudhary, 2011; Walter & Weber, 2006).
Empirical Evidence on Aggregate Market Activity
Henker et al. (2006) find no herding in the Australian market by using intraday data. Cajueiro
& Tabak (2009) find out evidence of herding in extreme price movements in Japanese market using
Chang et al. (2000) model. Gleason, Mathur & Peterson (2004) used the model on US Exchange
Traded Funds (ETF) intraday data and find no herding during periods of extreme market movements.
Chiang & Zheng (2010) investigate 18 world markets and observe significant results in advanced and
Asian markets in bullish market trends. They find no herding in Latin American markets. Their results
depict more pronounced herding in a crisis situation and towards US market. Caporale et al. (2008)
find the presence of market-wide informational cascade in Greece. Similarly, Kapusuzoglu (2011)
finds the presence of herding and contagion of herding towards US market in turkey during the crisis.
Economou et al. (2011) investigate Italian, Greek, Portuguese and Spanish markets and observe no
herding in Spanish and Greek markets in the crisis period. They also find herding away from US market
rather than with it. Herding is observed in Greece and specifically in upmarket condition. During this
period correlation across countries is observed confirming the existence of common global component
responsible for herding behavior in the local market.
Caparrelli, D'Arcangelis, & Cassuto, (2004) investigate herding behavior in Italian stock
market. Henker, Henker, & Mitsios (2006) use intraday data and find no evidence of herding in the
Australian market by employing both Christie & Huang (1995) and Chang et al. (2000) model.
Cajueiro & Tabak (2009) investigate Japanese stock market and report herd formation in extreme price
movements using Chang et.al (2000) model. Similarly, Tan et al. (2008) investigate Chinese A and B
shares markets and find significant herding in both up and down market. Whereas, Chiang & Zheng
(2010) investigate same markets but find herding only in down market. Demirer, Kutan, & Chen (2010)
39
investigate Taiwanese market at sectoral level and in the overall market and find significant results
especially during a market downturn. Prosad, Kapoor, & Sengupta (2012) find the presence of herding
in Indian market only in a crisis situation. Javed, Zafar, & Hafeez (2013) find no herding in Pakistani
market. Philippas et al. (2013) identify herding in US real estate investment trust (REIT) by using
CSAD model and find herding behavior due to macro shocks of REIT funding conditions and
deterioration of investor’s sentiment. Demirer et al. (2014) investigate the existence of herding in
American Depository Receipts (ADRs) and infer more evident herding at sector level than country
level. Hsieh, Yang, Yang, & Lee (2011) investigate 12 Asian markets by using return dispersion
models and find significant herding behavior. Garg & Gulati (2013) find no evidence of herding in
Indian stock market by employing Chang et al. (2000) under normal market conditions. Similarly,
Javaira & Hassan (2015) identify no evidence of herding during normal market conditions but
significant herding behavior during a crisis situation in Pakistani stock market.
In the recent years, Chiang, Tan, Li, & Nelling, (2013) estimated the return dispersion model
by using a time-varying approach through Kalman filter estimation and find out dynamic herding in
the US and ten Pacific-Basin markets. All markets except US display significant herding behavior.
Similarly, Yang & Chen (2015) investigate the time variation of herding behavior in Chinese, Hong
Kong, and Taiwanese market and find significant herding in greater China stock markets specifically
during the global financial crisis. They also observe that Chinese and Taiwanese markets exhibit a
higher response to US market factors and exhibit higher herding tendency during a turbulent period.
By using Hwang & Salmon (2004) model, Khan, Hassairi, & Viviani (2011) find the significant impact
of the global financial crisis on the herding tendency of UK, Italian, French, and German markets.
More specifically recent empirical literature following the particular market state has been
reported by several researchers. For example during the period of negative market returns (Zhou &
Lai, 2009; Goodfellow et al., 2009; Demirer et al.,2010; Economou et al., 2011; Gavriilidis et al., 2013;
Holmes et al., 2013) significant herding during positive market returns (Economou et al., 2015a;
40
Economou et al., 2015b) under low volume state (Tan et al., 2008; Economou et al., 2011) and high
volume state (Gavriilidis et al., 2013; Economou et al., 2015b) the effect of declining volatility
(Economou et al., 2011; Holmes et al., 2013; Economou et al., 2015b), and high volatility (Blasco et
al., 2012; Economou et al., 2015b).
The above-mentioned studies are mainly focused on identifying the presence of herding behavior
in different economies. This research is different from the above-mentioned results in many ways, first,
this study tried to examine the presence of herding behavior using industrial level data in a large
dataset. Second, the major focus of this study is not merely the evaluation of herding behavior rather
this study incorporates both irrational component of fundamental factors along with social influences
like culture to provide a comprehensive analysis of investor irrationality. Specifically in a broader set
of global economies. Finally, this study also determines effect of pure contagion caused by the crisis
situation and macroeconomic information that has previously been ignored in the literature.
Culture:
For centuries understanding and defining culture has been of scientific appeal1. According to
Hofstede (1991), everything in the life of a person is affected by the culture of a society. Therefore, when
a study integrates a particular phenomenon in the cross-country analysis, it shall also consider culture. In
literature, there are several definitions of culture. Hofstede (1980) defines culture as “the collective
programming of the mind distinguishing the members of one group or category of people from others”.
Hill (2008) defines culture as “a system of values and norms that are shared among a group of people
and that when taken together constitute a design for living”.
From the definition of culture, it seems difficult to calculate culture in a quantitative manner.
In previous years several attempts are made to measure culture, where scales and dimensions are
1 See Kroeber and Kluckhohn, 1952.
41
constructed to differentiate national culture. They are in the form of large-scale surveys, conducted
and filled by individuals of a society, mean values are identified in order to obtain quantitative cultural
characteristics (Reuter, 2011).
Schwartz (1994), Hofstede & Hofstede (2001) and House et al. (2004) give different
approaches on cultural dimensions. Among these dimensions, Hofstede’s and Schwartz’s are the most
extensively used in literature. Schwartz (1994) finds out three dimensions: egalitarianism vs.
hierarchy, mastery vs. harmony, and conservatism vs. autonomy conducted a survey of teachers and
student in 38 countries. Similarly, House et al. (2004) conducted a survey of 931 different
organizations and to 17300 managers in 62 countries from 1994 to 1997. They found nine dimensions
of culture: future orientation, assertiveness, gender equality, in-group collectivism, human orientation,
institutional collectivism, power distance, performance orientation, and uncertainty avoidance.
Hofstede & Hofstede (2001) used survey analysis during 1965-1971, collected data from IBM
employees in 50 countries and find five cultural dimensions. These dimensions are uncertainty
avoidance, power distance, masculinity, individualism, and long-term orientation.
Hofstede’s cultural dimensions are criticized by several authors. Kirkman et al. (2009) argued
that the dimensions are developed on the assumption that there is homogeneity in the country and did
not consider the effect of subcultures. McSweeny (2002) and Craig & Douglas (2006) state that the
study is conducted in 60’s and 70’s and evolve with the changing culture driven by economic growth,
globalization, or migration and cultures evolve during the time.
After all this criticism Hofstede & Hofstede (2001) repeated the analysis using data from 400
non-IBM employees from 30 countries and find a strong correlation between IBM and non-IBM
employees responses. Therefore, it is easier to interpolate these results to other contexts. Similarly,
House et al. (2004) criticize that the scales are determined only after the results of the survey, this
technique has a problem of biasness and influence the results. They further argue that the problems of
empirically-driven dimensions can be resolved by increasing the sample size and Hofstede & Hofstede
42
(2001) fulfilled this requirement. In spite of all criticism, Hofstede’s dimensions are still used as most
clear simple and applicable measure in empirical analysis. (Kirkman et al., 2009).
Cultural theory has been in discussion since last decades and culture has become a
multidisciplinary approach since last few decades and several streams of research ranges from
sociology (Weber 1962), cognitive and social psychology (Hofstede 1991), anthropology (Malinowski
1961) and history to economics and management science (Meyer & Rowan, 1977; Stulz & Williamson,
2003) are found in literature. This distinct and broad field of research comprises a diversity of
approaches and provide a support for Cross-cultural analysis in fields mentioned above2.
Empirical Studies of Cultural Finance:
This study is an effort to investigate investor behavior in financial markets in the cross-country
analysis. Stulz & Williamson (2003) suggest that there are three main effects of culture in finance:
First, culture modify economic values in a country (e.g. use of alcohol in a country is allowed while is
prohibited in other countries); second, institutions are governed according to culture (difference in
legal systems across countries); third, economic resources are allocated according to culture (few
countries allocate more resources towards weapons and some spend more on infrastructure). Effect of
culture is mainly studied in a context that how it effect the laws and regulations. Anderson, Fedenia,
Hirschey, & Skiba, (2011) argued that culture not only affect investor behavior through legal and regulatory
framework but also has a direct effect. Similarly, Sapienza (2006) argued that due to difficulty in
measurement and ambiguity, the effect of culture is ignored is past.
Using culture in the financial decision-making process is an emergent field. Several authors
investigate the effect of cultural dimension on financial environment. In most of the cross-cultural
research, the authors have opted one dimension only and all dimensions collectively are rarely used to
2 Different cultural dimensions suggested by the authors unambiguously distinguish cultural groups from each other (Hofstede, 1980, 2001; Hall, 1985; Hall and Hall, 1990; or House et al., 2004).
43
investigate their impact on investor behavior. The individualism is the most researched dimension
among all both empirically and theoretically. Chui, Titman, & Wei (2010) investigated the impact of
individualism on momentum profits, and find that investors in these countries and more overconfident.
This overconfidence results in excessive trade and generates momentum profits.
Other dimensions are also investigated like, Stultz & Williamson (2003) study the effect of
culture on financial institutions of a country. De Jong & Semenov (2002) study the effect of higher
level of masculinity and lower level of uncertainty avoidance on the stock market development in
OECD countries. Similarly, Ferris, Jayaraman, & Sabherwal (2013) conclude that overconfidence is
negatively related to long-term orientation and uncertainty avoidance, whereas, it is positively linked
with individualism. Schmeling (2009) uses uncertainty avoidance and individualism as a measure of
overreaction and herding behavior and conclude that stock returns are greatly influenced by investor
sentiments in such markets. Chang & Lin (2015) studies the effect of national culture on herding
behavior of an investor in a set of 50 stock markets and find a significant effect of culture on herding
behavior of investor.
Contagion
The interconnectedness of stock markets is defined as stock market integration. The famous
Markowitz portfolio theory states that for risk diversification investor rather than investing in single asset
choose a proportion of assets to make a portfolio. Henry Markowitz states, ‘Diversifying sufficiently among
uncorrelated risks can reduce portfolio risk toward zero’.3 So an investor can diversify his risk by investing
either in the national or international market. The benefit of this diversification is associated with lowest
and calm connections between the markets. If this market behaves homogeneously benefit of
diversification can be eliminated due to contagion effect. Levy & Sarnat, (1970) stated that investor can
3 Statement given in an interview with Wall street Journal on November 3, 2008
44
get a benefit of international portfolio diversification if only stock markets are not integrated. As Bakeart
& Harvey, (1995) argues that the diversification of global systematic risk is almost impossible as the
markets share common sources of risk, therefore they behave in similar. This effect can be seen in the event
of Asian Financial Crisis of 1997 and Global Financial Crisis of 2007-2009. During this time period, most
of the integrated markets share the same level and source of risk. In the presence of complete market
integration, the expected risk and return of integrated markets is same (Bakeart & Harvey, 1995).
The connections in stock markets can be defined in two ways, one is the increased level of
interdependence among markets near complete integration. This is an extreme level of interdependence in
which repeated shocks become a part of market equilibrium (Forbes & Rigobon, 2002). In this case, before
and after the crisis there shall be no considerable variation followed by interdependence no “contagion”.
Forbes & Rigobon (2002) further elaborate the fact as if markets are highly correlated than both in crisis
and stable condition they will behave in a similar manner, and do not exhibit abnormal shocks, this effect
cannot be defined as contagion but simple interdependence.
The second channel considers contagion out of the market system. Contagion is epidemic and
spread like a disease. In the presence of contagion, the markets are not fully integrated and show significant
signs of shock transmission during turbulent periods (Corsetti et al., 2011). The term contagion into
literature has attracted the attention of economist in mid-1990s when the spread of financial crisis
approaches certain emerging markets that were fundamentally strong and are an example for the
policymakers of fundamentally unhealthy nations.4 In literature, contagion is defined in various ways
but simply contagion is the transmission of shock across the borders. Edward (2000) defines contagion
as the excess co-movement of returns after a change in market fundamentals or common shocks.
Kaminsky & Reinhart (2000) associate the probability of domestic crisis with having a knowledge of
crisis elsewhere. Edward (2000) define economic contagion as “situations where the extent and
4 See Masson (1998) for complete discussion.
45
magnitude to which a shock is transmitted internationally exceeds what was expected ex-ante”. They
further elaborate that contagion is the excess co-movement of returns after a change in market
fundamentals or common shocks. The major reason behind this shock can be financial imbalances
across the border, housing bubbles, and lack of transparency, securitization, and complex financial
instruments. During contagion level of volatility, transmission is very high and further exacerbated
into panic due to irrational investor behavior. Bekaert et al. (2014) define “contagion” as co-
movements of markets and volatility spillover during crisis periods compared to non-crisis periods.
Leung, Schiereck, & Schroeder, (2017) investigate the contagion of crisis between exchange rate and
equity markets and observe the investors behavior leads to irrational phenomena like financial panics
(pure contagion) in excess of that implied by macroeconomic fundamentals (fundamental contagion).
There are three channels where shocks are transmitted to other economies, fundamental-based
contagion, common cause contagion and pure contagion (Moser, 2003)
Fundamental Based Contagion
If the shock from one market to other market transmitted through trade or financial links it is called
fundamental based contagion (Kaminsky et al., 2001). This transmission mechanism can be due to
trade ties or through competitive devaluation. The resultant effect of this interconnectedness is
spillover effect (Gerlach & Smets 1995). In emerging market global financial portfolios diversification
is a major cause of this contagion (Belke & Setzer, 2004). Kaminsky & Reinhart (2000) argue that
financial crisis affect multiple countries if they are financially interdependent. They argue that if the
economies have debts in countries where the impact of the crisis is high, then the probability of
contagion effect increases due to the integrated banking sector.
Common Cause Contagion
The common cause contagion occurs when the common fundamental news is the cause of
reallocation of assets. The basic cause of this type of contagion is an incomplete set of information due
46
to which crisis is transmitted from one market to another (Belke & Setzer, 2004). The crisis in one
country generates a wakeup call in another country, as the market participants re-evaluate their
investment risk with the same fundamentals that are the root cause in the source country. Uncertainty
about the investment motivates the investor to sell the assets of the identified countries thereby shifting
the effect of crisis from one country to other due to increased volatility. Eichengreen et al. (1996)
argue that comparatively trade links are more severe than the fundamental based contagion. In fact,
the crisis around the globe has rekindled the literature on contagion [see Kalbaska & Gatkowski, 2012;
Metieu, 2012; Beirne & Fratzscher, 2013; Mink & De Haan, 2013; Ludwig, 2014; Eichengreen &
Gupta, 2015; Aizenman et al., 2016).
Pure Contagion
During the episodes of recent financial crisis like Asian crisis of 1997 and the Global financial
crisis of 2007 the international transmission of shocks is observed in countries that have negligible
economic linkages. Therefore, the explanation of crisis contagion is not possible with financial and
trade linkages alone and the existence of pure linkage make sense and theoretically this linkage centers
on herding. Herding contagion can be explained through the rational and irrational view of contagion.
This research is focused on the irrational view of herding, therefore, literature concerning this view is
discussed in detail. Bikhchandani & Sharma (2000) attribute major reason of herd behavior to the market
imperfection along with unfair compensation structures and reputation concerns. Thus, this type of herding
is due to Informational cascade, reputation-based herding, and financial linkages.
According to informational cascade, the market participants act sequentially and actions of first
few investors decide the future course of action. The leading investors may have a misleading set of
information that is followed by the rest of investors in the market (Bikhchandani & Sharma, 2000). In
the presence of information cascade, the country with the sound economic system is also not protected
due to speculative attack as observed in crisis period (Belke & Setzer, 2004). Another explanation of
47
information content is the cost of information acquisition. Due to increased globalization and stock
market integration, the demand for information access has reduced in past few years. The stock markets
of emerging markets are highly volatile to rumors that may result in more contagion volatility
spillovers due to portfolio diversification (Calvo & Mendoza, 2000)
Reputation-Based Herding discussed by (Scharfstein & Stein, 1990; Trueman, 1994; Graham,
1999) & Welch, 2000) in this type of herding to maintain their reputation investors make sub-optimal
decisions by following the actions of benchmark against which their performance is measured.
Compensation Based Herding is attached with the external rewards and the investors negate the rational
analysis in order to achieve the average level of performance in order to get incentives and to avoid the risk
of getting nothing (Maug & Naik, 1996). The theories argues that financial crises spread from one
country to another due to reasons not related to or explained by economic fundamentals, such as market
imperfections of herding behavior of international investors (Masson, 1999 or Mondria & Quintana-
Domeque, 2013; Beirne & Fraszter, 2013; Ludwig, 2014; Eichengreen & Gupta, 2015)
48
Theoretical Framework:
Determinants of Herding Behavior
Stock Market Performance
The stock price behavior in the market is usually predicted in theory by the Random walk
hypothesis and is considered to be a salient feature of efficient markets (Fama, 1991). The sharp change
in returns resulting from the crash or uncertain situation causes a sharp decline in returns within a short
period of time that results in a stock market crash and huge losses in shareholders wealth. However,
these extreme variations in stock prices are against the fundamentals of random walk hypothesis and
negate the assumptions of market efficiency that successive prices are not serially correlated and
identically independent (Engle, 2002). This situation leads to market uncertainty and huge risk. Bailey
(2009) blame behavioral factors rather than the fundamental ones as a cause of this situation, they
argued that in spite of rational behavior markets are captivated by irrational behavior caused by some
behavioral linkages like herd behavior, noise trading, and the bandwagon effect. These effects cause
market inefficiency and lead to stock market price distortions, breakdown and eventual collapse
(Barberis & Shleifer, 2003).
Chang, Chen & Jiang (2012) argue that herding behavior during the specific period can push
the stocks’ return down or up. In stock markets, people generally follow two trends either positive
feedback trading (following the pattern of historical prices) (De Long et al., 1990) or herding where a
person follows the actions of others (Bikhchandani & Sharma, 2001). Both effects are a consequence
to each other and it is observed that in practice positive feedback traders usually sell stocks in falling
market and buy them in rising market whereas negative feedback traders behave in an opposite manner
(Sentana & Wadhwani, 1992). It is generally not known which type of feedback strategy do herding
investor follow in the market leading to positive or negative sign on the coefficient on the stock market
49
return. Therefore, it can be hypothesis that that herding behavior is perceived to have correlated effect
with stock market performance (Grinblatt, Titman, & Wermers, 1995)
H1: Stock market performance has a feedback effect on the trading activity of investor thus
significantly affect the herding behavior of an investor.
Volatility of Stock
According to rational expectation models, uninformed trading increases the market price
volatility (Blasco et. al, 2012). The volatility of stock markets have certain markets implications as
correct estimates of correlation and volatility are needed for portfolio optimization, derivative pricing,
hedging and risk management. Under the traditional finance paradigm, where the markets are efficient,
prices instantly adjust to the arrival of new information and volatility is caused by the continuous of
prices to the information arrival. If the prices do not reflect adjustment to new information then it can
be due to certain market conditions or collective actions like herding ((Thaler 1991, Shefrin 2000).
Stock market volatility and its effect on herding behavior are widely tested (Chang et al., 2000, Tan et
al., 2008). It is observed that in extreme events and period of market stress stock market volatility
increases. Montgomery (1991) argues that during uncertain market conditions volatility increases, the
strong pressure in market increase the likelihood of making an error and the impulse to herd becomes
particularly strong. Therefore, stock market volatility can be an important determinant of herding
behavior. Thus we can hypothesize that stock return volatility has significant effect on herding
behavior
H2: Stock return volatility increases herding behavior in the stock market.
Global Volatility
The other main factor is the global market volatility, i.e. the transmission of stock market volatility
(Diebold & Yilmaz, 2009). During the financial crisis, this behavior is more obvious and irrespective
50
of the channel adopted emergence of negative news is soon transmitted to the market participants
(Pericoli & Sbracia, 2003). The impact of Return volatility spillover is observed by Beirne et al. (2000)
from US market to some Pacific- basin market. Liu & Pan (1997) witness similar effect from US
market to Asian stock markets. Strong evidence of cross-market correlation is spotted during the period
of excessive volatility (Forbes & Rigobon, 2002; Butler & Joaquin, 2002). Thus we can hypothesize
that
H3: Global volatility has transmission effect from across the markets, therefore affect herding
behavior of investor in correlated markets.
Herding Behavior and Culture:
Each individual around the world behaves in a unique manner. If it is true then arguments built
by the practitioners of behavioral finance i.e the effect of psychological biases, their perception about
the world as established by Culture will not affect their investment decision in the market (Stultz &
Williamson, 2003). People generally share a collective set of experience that affects their investment
behavior in an emotional and cognitive manner (Statman, 2008). Culture practices can be imitated,
even when the behavior is not directly observable and it is cognitively presented in stories (Bruner,
1990). Both social psychology (Cialdini & Goldstein, 2004) and evolutionary psychology (Henrich &
Gil-white, 2001) confirm the hypothesis that people copy the behavior of likable others and behavior
of higher status. According to Losin, Dapretto & Iacoboni (2009) state that imitation takes place due
to the supply of plausible neural mechanism by mirror neuron system in humans. Therefore, it is an
established fact that the human imitative behavior is greatly affected by the surrounding environment
and especially the culture of a particular society. The purpose of this study is to establish the fact that
culture has an important role to play in the financial decision-making process of an investor specifically
to explain the intensity of herding tendency in a cross-cultural context. In this section, it is intended to
develop a hypothesis based on the effect of each cultural dimension on herding behavior.
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Individualism and Herding Behavior
The empirical literature provides evidence in favor of the existence of psychological
individual-specific factors that affect the individual investment in risky assets (Puri & Robinson, 2007;
Charness & Gneezy, 2010). On the other hand in behavioral finance investor portfolio diversity can
be attributed to psychological factors (Shum & Faig 2006). Hofstede & Hofstede (2001) argued that
individuals in collective societies are linked to each other in a strong and interrelated group, whereas people
in individualistic societies share a weak bond. Therefore, in individualist societies, investors prefer their
own opinion and are less likely to care about public opinion, in a society with a higher level of
individualism. Goodfellow et al. (2009) argue that if an investor is overconfident while making an
investment decision, they rely less on others and prefer their own beliefs. Similarly, Barber & Odean
(2001) state that an individualist is usually overconfident and value their predictions more, therefore
usually overestimate their evaluation about security prices. On contrary, with a lower level of
individualism, investor behavior is more converge towards market consensus and will cause herding
behavior (Chang & Lin, 2015). Similarly, it is observed that people in collectivist culture societies
people give more importance to others opinion and rely less on their private information (Chui et al.,
2010). Finally, Schmeling (2009) argues that if managers follow the market trends, this collectivism
leads to herding behavior. In accordance with cultural theory and the Hofstede framework, it is
hypothesized that culture can matter significantly as Individualism has the predicted sign. The country
high in the individualistic dimension the investors are the less likely to show herding behavior.
Therefore, it is hypothesized that,
H4: Individualist countries tend to be associated with overconfidence, thus decreases the intensity of
herding behavior.
52
Power Distance and Herding Behavior:
Hofstede (1991) says that people are independent in low power distance countries and have own
initiative, while in high power distance societies they rely on others commands. Chui & Kwok (2008)
states that people in high power distance societies stated are also collectivists. In a high power distance
environment, people gain information in unequal conditions and society is unequal. Information is
acquired through the transaction made by others and people usually follow their leads, resulting in
herding behavior (Chang & Lin, 2015).
Conversely in low power distance society’s individuals share values like trust, equality, and
cooperation (Sinke 2012). Mihet (2013) also supported this argument by finding more trust and
harmony in countries with low power distance. House et al. (2004) also observe this fact and argued
that information is shared in countries with low power distance. Therefore, on the basis of
aforementioned literature, power distance leads to more herding but this association is not clearly
supported by the literature.
H5: Power distance has a significant effect on the herd behavior.
Masculinity vs Feminist and Herding Behavior
Masculinity and feminist are attributed to the role of each gender in the society and make a clear
distinction in a specific culture. According to this dimension, men are generally regarded as arbitrary,
strong, and focused on material achievement, while femininity is modest, tender, and focused on life
quality. Estes & Hosseini (1988) state that gender has an important role in defining the confidence
level of investor especially when they make decisions. They further contend that men are more
confident in making decisions. Yao & Hanna (2005) conclude that women tend to be more risk-averse
than men if they have to choose and risky asset with more life expectancy. Chang & Noorbakhsh
(2009) claime that in masculine societies, for getting higher returns people tend to hold more cash so
that they can exploit faster strategic opportunities. Therefore, in masculine societies, men are more
53
ambitious and self-confident tend to be more success oriented and risk takers (Hofstede, 1991). Thus in
countries with high masculinity level, people are more materialistic and more likely to engage in selling
low and buying high in order to be opportunistic. This behavior tends to incline them towards high profits
to satisfy their greed, which results in more herding behavior.
H6: Masculine countries exhibit self-confidence and risk-taking behavior rely more on profits, which
leads to high level of herding.
Uncertainty Avoidance and Herding Behavior:
This dimension explains the comparison of countries on the basis of high and low uncertainty
avoidance when they face an uncertain or threatening situation. The individuals in high uncertainty
societies generally have an overreacting behavior and react more emotionally (Schmeling, 2009). They
find a herd-like behavior in countries with low level of individualism and high Uncertainty avoidance.
Hofstede & Hofstede (2001) state that things uncertain for one person may not be same for another
and risk avoidance is not similar to uncertainty avoidance. Li et al. (2013) examine the effect of the
culture on managerial decision making and conclude the negative relationship between corporate risk-
taking and uncertainty avoidance. This trait restrains them from taking high risk and exploring
innovative investments. People in high uncertainty avoidance culture are characterized with having
defined legal frameworks and safety measure, whereas, in a low uncertainty avoidance culture people
feel more natural and are more absorbent to varying behaviors, and avoid strict regulations (Park &
Lemaire, 2012). According to theory, a higher tracking error (i.e., safety margin) that is used to increase
portfolio risk shall lead, on average and in the longer run, to higher returns compared to the benchmark;
Thus, Uncertainty Avoidance may be a reason for lower returns generated by affected asset managers
(Beckmann et al., 2008). To reduce uncertainty, investors of societies with a higher value in this
dimension usually rely more on information research. The outcome may always be efficient and it will
remain debatable, but at least they try to obtain all relevant information required to make an efficient
54
decision and chances of risk may reduce (Offerman & Hellman 1997). Uncertainty avoidance
commands people to track the same set of rules and avoid risk, thus usually behave in a similar manner
(Hofstede & Hofstede, 2001). Similarly, Sinke (2012) conclude that people with high uncertainty
avoidance exhibit more herding behavior. Therefore, we hypothesize that,
H7: High uncertainty avoidance leads to risk averseness, thus exhibit more herding behavior.
Long-term Orientation and Herding Behavior:
LTO refers to virtues, and especially persistence and frugality, which promote and encourage
the search for the orientation of future returns. Anderson et al. (2011) states that people in long-term
oriented countries are less myopic, which directs them towards more diversification and less herding
behavior. Due to this strong orientation towards fundamentals, one may be not surprised that the
preferred investment strategy is also derived from theory. Inefficient markets, it is rational to rely on
a long-term orientated buy and hold strategy, especially when facing long-term investment horizons,
and thus to refrain from following short-sighted sentiment driven trends and frequent portfolio shifts
that might cause high transaction costs. People with short-term orientation focus on fast outcomes,
therefore invest mostly in most liquid assets or assets like mutual funds. Lobao & Serra (2002) find
that mutual funds are short-term securities and evaluated quarterly, therefore are more inclined to
herding behavior in order to maintain reputation. Chang & Lin (2015) claim that STO culture society
investors prefer current returns, and are more inclined towards market opinion, it leads to and engage
in short-term trading and restrain them from long-term investment, therefore they exhibit more herding
behavior. Matsumoto (2007) argued long-term orientation is related to emotion and temperature of
fear is felt high, but this orientation is positively related to the emotion of joy, shame, and guilt. Park
& Lamaire (2012) argue that long-term oriented societies are more adhered to Confucian culture,
respect of family values, follow of traditions, respect for ancestor’s traits and honor of parents are
major characteristics of these societies and these attributes are more like collectivist traits. A clear
55
direction of relationship cannot be theorized from literature. Thus, we can conclude that due to risk
aversion long-term orientation and collective traits, investor is influenced by the behavior of others.
H8: LTO is risk-averse and more affected by emotions and collective traits, thus exhibit more herding
behavior.
Indulgence versus Restraints
This dimension is a recent addition to the Hofstede dimensions after the work of Minkov (2009).
People in indulge societies satisfy their natural human desires of enjoying life by allowing free
gratification contrary to restraint societies that are more restrictive and regulated by strict norms. This
dimension explains the traits how individuals control their desires based on the way of their upbringing.
The indulge societies are more tilted towards positive emotions than negative emotions. This cultural
dimension has three classifications: “happiness and pleasure in life, the importance of leisure and
friendship, and life control”. This index is positively correlated to the power distance, authority, and
institutions. Highly restraint societies exhibit strict social norms and organized legal frameworks.
Markets in strong regulatory frameworks are usually transparent and less affected by human behavior.
The clear relationship with investor behavior cannot be supported through literature but it can be
argued that Individuals in highly indulged societies exhibit weak control over desires, whereas
societies with a low level of indulgence control their urges strongly. Therefore, it is hypothesized that
H9: IVR exhibits a significant effect on herding behavior.
Effect of Macroeconomic Shocks on Herding
Uncertainty is defined as people able to predict the likelihood of an event to happen (Knight,
1921). An uncertainty shock can be attributed to large dispersions in expected future outcomes. It is a
broad phenomenon: uncertainty can be macro level like GDP growth, micro level like firm growth and
non-economic event specific to war or climate change. The macro uncertainty is due to changes in
56
macroeconomic variables and this volatility increases in a recession. The macroeconomic variables
and stock returns are endogenous variables, therefore their reaction to specific shock can be
simultaneous (Wasserfallen, 1989). Financial prices like exchange rates, bond yields and nonfinancial
measures like GDP and industrial production growth are more volatile in recession (Bloom, 2014). In
financial terms, it is explained in terms of determinants of discount rates or over future profitability,
both are a cause of increased stock volatility. Prieto et al. (2016) identify the impact of shocks to credit
spreads on the lag value of real and nominal variables, and financial variables also react simultaneously
to such disturbance. Gilchrist et al. (2009) identify financial shocks from the corporate bond spreads
that are orthogonal to general measures of economic activity, inflation, real interest rates, and various
financial indicators, as well as to equity returns of firms whose outstanding bonds are used to construct
credit spreads in the bond portfolios. Bachmann, Elstner, & Sims, (2013) found 64% higher Standard
deviation across forecast of IP (industrial production growth) in European economies during the
recession. Similarly, Campbell et al. (2000) investigate the variation in cross-firm stock returns and
50% increase in variation in the recession than boom. Bloom (2008) investigate us to market and
conclude that uncertainty shocks have significant effects on industrial production and employment. On
the other hand, Fernandez et al. (2008) find the persistent effect of these shocks in emerging markets.
Stock market volatility has also been previously used as a proxy for uncertainty at the firm level
(Leahy & Whited (1996) and Bloom, Bond & Van Reenen. (2007). A number of studies document the
impact of unexpected variation in macroeconomic variables or uncertainty shocks like London attack,
9/11 terrorist attacks on stock returns and volatility (Fama, 1981; Wasserfallen, 1989; Gjerde &
Saettem, 1999; Bloom, 2009). Faust (1998) & Uhlig (2005) analyze the impacts of shocks behind
economic uncertainty and changes in financial conditions on the macroeconomy.
Investor behavior is an important determinant of stock market volatility, an uncertain situation
related to market fundamentals can affect investor decision making. Blasco et al. (2012) and Messis &
Zapranis (2014a) investigate Spanish and Greek markets respectively and find a direct linear impact
57
of herding on market volatility and conclude that herding to be an added risk factor. Therefore, this
study is an attempt to empirically investigate the impact of unexpected components of few
macroeconomic variables on the estimated herding measure. Messis & Zapranis (2014b) find this
effect on developed markets, like US, UK France Germany, and China. This fact motivates us to
investigate the impact of macroeconomic shocks on a set of developed and emerging and frontier
markets of Asia, Asia Pacific and Europe. One of the added contributions of this research is the
inclusion of monetary variables like Industrial Production index, interest rates, exchange rate, and
money supply as macroeconomic variables. These variables have previously been used in literature to
find the impact of the macroeconomic shock on stock market volatility (Bloom, 2009) and herding
Behavior (Messis & Zapranis, 2014) in five developed markets. This study empirically contributes to
literature by investigating this impact in a large set of economies. Specifically, developing markets are
more sensitive to shocks as compared to the developed economies (Voronkova & Bohl, 2005) that are
previously ignored in literature. Hence, it is hypothesized that
H10: Macroeconomic uncertainty and crisis shocks have a significant effect on herding behavior.
Herding Behavior and Financial Contagion
Calvo & Reinhart (1996) define contagion as “investors’ reaction towards a country’s asset
return characteristics’ or to mimic the perceived optimal portfolio share assigned to a particular country
by an arbitrary “market” portfolio”. This study explains contagion in two ways, contagion related to
economic fundamentals and contagion related to herding behavior that is investor mentality.
Similarly, Forbes & Rigobon (2002) define contagion through the fundamental channel and
suggest that across the border linkages in stock prices, capital flows and exchange rates increases after
an uncertain shock in a country or group of countries.
Masson (1999) classifies three channels of international financial transmission, spillovers,
monsoonal effect and jumps in equilibrium. Spillover effect can be explained through real linkages
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like trade and other economic activities. The monsoonal effect arises from global environmental
changes and has global impacts. These two types are classified as fundamental ones and the last one
is considered as herding. The jumps are considered as a residual effect, where investor’s changing
expectations causes jumps between multiple equilibriums.
Kodres & Pritsker (2002) investigate financial contagion in a rational expectation model. They
state that contagion happens through cross-market rebalancing: when agents are hit by a shock in one
market, they need to rebalance their portfolio of assets; the presence of asymmetric information
exacerbates the price co-movements resulting from this rebalancing. Yuan (2005) suggests that
asymmetry in information and borrowing constraints are the reason of contagion. Economou et al.
(2011) find the contagion caused by herd behavior in European markets during the financial crisis.
Cross country herding behavior in addition to local market could be due to portfolio rebalancing
(Brunnermeier & Pedersen, 2009), risk premium and liquidity channels (Longstaff, 2010), and cross-
listing effects, (Chandar, Patro, & Yezegel 2009). Similarly, several authors discuss the possible causes
of contagion (Kyle & Xiong, 2001; Mondria, 2006; Kaminsky & Reinhart, 2000; Forbes & Rigobon,
2002). Allen & Gale (2000, 2007) attribute contagion effect to financial system fragility among
financial institutions. In the light of above argument, we develop following hypothesis,
H11: Crisis situation leads to herding behavior that can cause contagion effect.
H12: Contagion of herding is existing across the border due to unexpected shocks in macroeconomic
fundamental.
59
Chapter 3
3. Data and Methodology
Research Design:
Data and Sample:
The dataset in this study consists of industry and Market index returns of Developed, Emerging
and Frontier Markets of Asia, Asia Pacific and Europe. The countries are classified into two main
categories based on MSCI country classification: one category consists of developed markets of the
regions. The second classification includes emerging and frontier markets of Asia, Asia Pacific and
Europe. The three markets of China, Taiwan and Hong Kong under the classification are considered
as greater China markets. For the identification of herding behavior sample time period is selected on
the basis of the time of data availability. Some of the objectives require cross-country analysis,
therefore, data uniformity is required. In order to meet the said objectives the analysis time period is
restricted to a time span of January 2000 to December 2015. All the data for market and industrial
indices are obtained from DataStream international database. Industrial data is based on industrial
Classification Benchmark (ICB) which is a globally recognized standard, operated and managed by
FTSE International for categorizing companies and securities. The list of countries along with sample
size is discussed in table 3.1. Where no of shares represent the total numbers of firms included in data
stream to calculate the industrial indices fulfilling the requirements of industrial classification
benchmark standards for categorizing companies and securities. Dates represent the start time of the
data availability of each sampling country. The list of industries along with a number of sub-sectors is
given in Appendix 2.
To study the impact of macroeconomic shocks on herding measure, this study uses Industrial
Production Index (IP), Money Supply (MS), the Exchange rate and the deposit rate of each country.
60
The macro data is retrieved from DataStream international, IMF database and the Eurostat. For global
volatility, global volatility index VIX is obtained from Chicago Board of exchange website.
To investigate the impact of culture, this study employs national culture indices proposed by
Hofstede & Hofstede (2001) for the five dimensions of national culture: Power distance, individualism,
masculinity, uncertainty avoidance, and long-term orientation, the data for cultural indices are
retrieved from Hofstede website. The list of the variable is given in Appendix 3.
Table 3.1: List of sample countries with starting dates
Developed Markets Emerging and Frontier Markets
Country Code No of shares Start Date Country Code No of shares Start Date
Australia AU 160 11/24/1992 China CA 100 5/12/1998
Austria OE 50 11/12/1992 India IN 100 1/2/1995
Belgium BG 90 4/10/1991 Indonesia ID 50 1/4/2000
Denmark DK 50 8/14/1991 Korea KO 100 8/21/1995
France FR 250 10/20/1997 Malaysia MY 90 12/30/1994
Germany BD 250 1/2/1981 Pakistan PK 50 1/2/1995
Greece GR 50 9/2/1992 Philippines PH 50 9/2/1994
Hong Kong HK 130 6/21/1991 Sri Lanka CY 50 1/3/1995
Italy IT 160 1/1/1998 Taiwan TA 70 1/5/1995
Japan JP 1000 1/5/1981 Thailand TH 50 1/16/1995
The Netherlands NL 130 1/4/1983 Turkey TK 50 3/16/1993
New Zealand NZ 50 12/2/1999
Norway NW 50 11/3/1983
Portugal PT 50 1/4/1993
Singapore SG 100 6/2/1998
Spain ES 120 3/4/1987
Sweden SD 70 10/1/1986
Switzerland SW 150 12/4/1990
The UK The UK 550 12/8/1986
US US 1000 1/1/1981
61
Methodology
Measure of Herding
3.2.1.1 Return Dispersion Models
This study empirically investigates herding by using models developed by Christie & Huang (1995),
Chang et al. (2000) and Chang et al. (2010). Christie & Huang (1995) model is employed to estimate
the average proximity of individual stock returns to the realized market returns. Cross-sectional
standard deviation can be expressed as follows,
1
)(1
2
,,
N
RR
CSSD
N
i
tmti
t (1)
Where, N is the number of stocks in the aggregate market portfolio, Ri,t is the observed return on
industry index i for day t, and Rm,t is cross-sectional average returns of the aggregate market portfolio
for day t. Presence of herding behavior can be gauged by the occurrence of extreme market movements.
In the presence of herd behavior, dispersion between individual and market returns decreases. This
proximity in average returns can be measured through the model proposed by Christie & Huang
(1995):
t
L
t
LU
t
U
t DDCSSD 21 (2)
Where, 1U
tD , when the aggregate market portfolio returns in a given time period stay in the extreme
upper tail, and 0 otherwise. 1L
tD , if the aggregate market portfolio returns in the extreme lower tail
for a given time period and 0 otherwise. Thus, herd formation can be gauged by the presence of
negative, significant β1 and β2 coefficients.
62
The above-given model is further refined by Chang et al. (2000). This study argues that dispersion in
returns is a nonlinear function of market returns. Instead of standard deviation, Chiang et al. (2000)
use cross-sectional absolute deviation (CSAD) which can be modeled as follows,
N
i
tmtit RRN
CSAD1
,, ||1
(3)
This model capture nonlinear effect by using general quadratic-form between CSADt and Rm,t, the
model developed by Chiang et al. (2000) is given as follows,
ttmtmt RRCSAD 2
,3,21 (4)
Where, CSADt measures return dispersion, |Rm,t| is an equally weighted realized return of industry
index on the day, t in its absolute form. The relationship between CSAD and Rm,t capture the presence
of herding. The presence of significant negative coefficient γ3 of the quadratic term of market return
is indicative of herding behavior.
In order to take in to account the asymmetric investor behavior in various market conditions, Chiang
& Zheng (2010) include linear term Rm,t. in the above model. It captures the asymmetry in investor
behavior under different market conditions. In this model + describe the relation between return
dispersion and market return when Rm,t > 0, while - shows the relation when Rm,t ≤ 0. The ratio
of (+ ) / ( ) represents the relative asymmetry between market returns and stock return
dispersion (Duffee, 2001).
ttmtmtmt RRRCSAD 2
,4,3,21 (5)
3.2.1.2 Time-Varying Model of Herding Behavior
3.1.4 Time-Varying Dispersion Models
63
The above-stated model of herding presented by Chang et al. (2000) is static in nature means
it demonstrates the average relation between return dispersion and the square of market returns over
time. The time-varying or dynamic herding can be obtained by using Kalman filter approach. Chiang
et al. (2013) first use this approach on a sample of 10 pacific basin markets and observe the presence
of significant dynamic herding behavior in all markets except the US.
This approach is explained as:
ttmtmt rrCSAD 2
,2,10 (6)
𝑖,𝑡
= 𝑖,𝑡−1
+ 𝑣𝑖,𝑡 , 𝑣𝑖,𝑡 ~ 𝑁(0, 𝜎𝑣,𝑖2 ) , 𝑤ℎ𝑒𝑟𝑒, i=1, 2, 3 (7)
Equation (6) is a measurement equation and [𝛾0, 𝛾1, 𝛾2]′ is a vector of state variables. Equation (7) is
known as transition equation where state variables follow a random walk process. State series t
demonstrate time varying herding behavior in a given market during a specific time period.
3.2.1.3 State Space Models
The third type of model to measure herding is developed by Hwang & Salmon (2004). It measures
herding behavior at a monthly frequency, this model is based on the assumption that the time-varying
alpha and betas are constant within the limited period of one month. Capital asset pricing model in the
presence of rational investors and in equilibrium can be written as:
)()( mtimtitt rErE (8)
Where rit and rmt indicate the excess return of industry i and market return respectively and βit is the
systematic risk. Et(rmt) is the conditional expectation at time t. When herding occurs, Eq. (8) no longer
holds, resulting in the bias of expected return and risk. The model suggests that in the case of herding
towards the market, the following relationship holds:
𝐸𝑡𝑏(𝑟𝑖𝑡)
𝐸𝑡(𝑟𝑚𝑡)= 𝛽𝑖𝑚𝑡
𝑏 = 𝛽𝑖𝑚𝑡 − ℎ𝑚𝑡(𝛽𝑖𝑚𝑡 − 1) (9)
64
where 𝐸𝑡𝑏(𝑟𝑖𝑡) and 𝛽𝑖𝑚𝑡
𝑏 it are the biased expected returns on the industry and its observed beta
respectively. hmt is a latent herding parameter that is time varying. When hmt = 0, shown absence of
herding and the CAPM to holds in equilibrium. When hmt = 1, represents perfect herding towards
market indicating that all industries in the market change in accordance with the movements of the
market portfolio. When 0 < hmt < 1 show the presence of herding to some extent depending on the
intensity of hmt. In adverse herding should hmt < 0. When there is upward trend in the market, for an
industrial index with βit > 1 the CAPM suggests that Et (rit ) > Et (rmt ). There will be a downward
pressure on equity price due to the herding behavior of investors, making 0 < 𝐸𝑡𝑏(𝑟𝑖𝑡) < Et (rit ) For that
reason the industry looks less risky than it should, suggesting that 1 < 𝛽𝑖𝑚𝑡𝑏 < βimt . The same order of
betas holds when the market goes down as investors will buy the equity pushing it upwards. For an
industry with βimt < 1, the inverse process holds and the biased beta increases with the market changes
(i.e. 1 > βb it > βit ) (Wang, 2008). When βit = 1, the equity is neutral to herding, while for adverse
herding (i.e. hmt < 0) an equity with βit > 1 means that Eb t (rit ) > Et (rit ) > Et (rmt ) and for an
industrial index with βit < 1 holds that Eb t (rit ) < Et (rit ) < Et (rmt ). The cross section mean of βbit
(βit) is 1 regardless of whether βit is biased or not. Thus, the cross sectional standard deviation is:
𝑆𝑡𝑑𝑐𝛽𝑖𝑚𝑡𝑏 = √𝐸𝑐((𝛽𝑖𝑚𝑡 − ℎ𝑚𝑡(𝛽𝑖𝑚𝑡 − 1) − 1)2 = √𝐸𝑐((𝛽𝑖𝑚𝑡 − 1)2(1 − ℎ𝑚𝑡) = 𝑆𝑡𝑑𝑐(𝛽𝑖𝑡)(1 − ℎ𝑚𝑡) (10)
Where Ec(.) represents the cross-sectional expectation. If it is assumed that 𝑆𝑡𝑑𝑐𝛽𝑖𝑚𝑡
𝑏 are changing
overtime with the level of herding in the market and by taking logarithm on both sides, so eq (10) can
be written as,
log [𝑆𝑡𝑑𝑐(𝛽𝑖𝑚𝑡𝑏 )] = 𝜇𝑚 + 𝑣𝑚𝑡 (11)
Where 𝜇𝑚 = 𝐸[log [𝑆𝑡𝑑𝑐(𝛽𝑖𝑚𝑡𝑏 )]] and 𝑣𝑚𝑡~𝑖𝑖𝑑(0, 𝜎𝑚𝑣
2 ), state space model 1 is then estimated as
log [𝑆𝑡𝑑𝑐(𝛽𝑖𝑚𝑡𝑏 )] = 𝜇𝑚 + 𝐻𝑚𝑡 + 𝑣𝑚𝑡 (12)
𝐻𝑚𝑡 = ∅𝑚𝐻𝑚𝑡−1 + 𝑚𝑡
(13)
Where Hmt = (1- hmt ) and 𝑚𝑡
~𝑖𝑖𝑑(0, 𝜎𝑚𝑛2 ).Eqs. (12) and (13) are the standard state-space model
estimated by Kalman filter approach follows an AR(1) process
65
. This methodology, focus on the time varying movements in the latent state variable, Hmt. When 𝜎𝑚𝑛2
= 0, it shows the absence of the herding which means that Hmt = 0 for all t. The existence of herding is
interpreted by significant value of 𝜎𝑚𝑛2 and a significant ∅ supports this specific autoregressive
structure.
Model Selection Criteria Applied to Return Dispersion and State Space Model
A number of model selection criteria available for the time series analysis. A model selection criterion
tries to find the best fitting model. These criteria are based on the complexity of model selection and
essentially penalize the likelihood based on the sample size and number of estimated parameters. This
model selection is achieved through the most precise fit with a parsimonious specification. Two
popular information criteria are the Akaike information criterion and the Bayesian information
criterion (or the Schwarz information criterion) used for the selection of dynamic models in various
fields are Akaike information criterion (AIC) and the Bayesian information criterion (BIC) (Penny et
al., 2004). When applying these criteria to different models, the best model is the one with the lowest
value of AIC and BIC.
The Akaike information criterion (AIC), developed by Hirotugu Akaike in 1971 and proposed by
Akaike (1974), is a relative measure of the fitting quality obtained by a statistical model. AIC
combines a measure of model fit with a measure of model complexity, with the assumption that the
smaller value if better. For a given data set and a given model,
AIC = −2 log L + 2p
Where, L is the maximum likelihood of the data, and p is the number of parameters in the model.
Here, −2 log L is a function of the prediction error which should be smaller as the smaller valued give
better estimates. This value describes the fitness of the data.
66
The Bayesian information criterion (BIC), also named as the Schwarz criterion, was developed by
Schwarz (1978) and is a criterion for selecting models based on the posterior probabilities of the
comparing models. For a given dataset model is described as,
BIC = −2 log L + log(n)*p
Where p is the number of parameters in the model and n is the sample size in the model. The criteria
for model selection is same as the AIC model, the smaller value of BIC give better estimates.
Determinants of Herding Behavior
3.2.3.1 Internal Factors
3.2.3.1.1 Stock Market Performance
Stock market performance has a significant effect on investor imitating behavior. It is argued
that change in the price of a stock can greatly affect investor decision-making process (Grinblatt,
Titman, & Wermers, 1995). It is observed that in practice positive feedback traders usually sell stocks
in falling market and buy them in rising market whereas negative feedback traders behave in an
opposite manner (Sentana & Wadhwani, 1992). Therefore, stock market performance can be treated
as an important determinant of herding behavior. This stock market performance can be examined by
adding the stock market index returns
3.2.3.1.2 Volatility
In order to calculate conditional volatility of returns ARCH/GARCH specifications is
commonly used measures. These models are with the limitation of symmetric response even if the
positive news has a different effect from negative news (Black, 1976; Sentena, 1992; Bekaert & Wu,
2000). In certain financial time series, this response is often observed. High volatility is generally in
the presence of negative shocks than the positive shocks of the same intensity. In order to test this
asymmetric effect, the exponential GARCH (EGARCH) model of Nelson (1991) is employed to
67
capture the leverage effects (stock price decline increase the firm debt to equity ratio (financial
leverage) and, consequently, the degree of risk (volatility). The equation of the conditional variance
for the EGARCH model takes the following form:
Where are the estimated coefficient parameters? The coefficient is the estimated GARCH
symmetric effect in the equation. Parameter capture the persistence of volatility in the event of a
certain state in the market. The larger value of is indicative of longer than usual subsistence of
volatility after the crisis period. The coefficient represents the asymmetric effect. If = 0 then model
is symmetric, however > 0 or < 0 represent asymmetry. If > 0 the positive shocks (good news)
generate higher volatility than of negative shock (bad news) of the same magnitude and vice versa.
3.2.3.1.3 Global Market Volatility
In order to assess global volatility, Chicago Board Options Exchange volatility index is used, known
as VIX. The VIX is formed using options implied volatilities and represent next 30 days expected
return volatility of S&P 500 index.
𝐻𝑒𝑟𝑑𝑡 = ∅0 + ∅1𝑅𝑚,𝑡 + ∅2�̂�𝑡2 + ∅3𝑉𝐼𝑋𝑡−1 + 𝜀𝑡 (14)
Where herd is the estimated herd coefficient derives through state space model. Rm,t is market return,
�̂�𝑡2 is the conditional variance based on asymmetric GARCH(1,1) process for each market. Cross herd
is cross market herding correlation and VIXt-1 is the lagged CBOE implied volatility index.
Herding Behavior and Culture
To investigate the relationship between national culture and herding behavior, this study uses six
dimensions of national culture suggested by Hofstede & Hofstede (2001). Index of all dimension of
culture employed by Hofstede & Hofstede (2001) are described in more detail below
1
1
1
12
1
2 loglog
t
t
t
ttt
68
(1) Power distance index (PDI): Power distance index refers PDI which refers to the expectation and
acceptance of the disadvantaged members of the society towards the inequality of authority within
national institutions and organizations (Jong & Semenov, 2002)
(2) Individualism Index (IDV): IDV indicates that how much people of a society are loosely
connected and they look after themselves and their core families relative to society as a whole as in
collectivism. (Chui, Titman & Wei, 2010).
(3) Masculinity Index (MAS): In masculine societies major emphasis is on success and material
achievement, instead of the quality of life and concern for others. In masculine society, individuals are
more open to investment in foreign markets and are usually risk takers (Anderson et al., 2011). This
type of society promote competition and systems are basically market-based and reward the strong
players of the market, gender role is clearly discriminated. On contrary feminist societies are caring,
modest and more focused towards the quality of life.
(4) Uncertainty avoidance index (UAI): UAI refers the societies that are high in this dimension are
usually threatened to uncertainty and ambiguity when they have to deal an uncertain situation. This
trait affects their investment decision, as decision-making process is an outcome of a sense of security
derived from perceiving a threat, not by their achievements and capabilities (Dutta & Mukherjee,
2012). They accept investments with relatively lower risk and domestic investment over cross-border
diversification (Anderson et al., 2011)
(5) Long-term orientation index (LTO): long-term oriented societies focus on future returns and
promote and encourage long-term goals over short-term orientations. They are usually adaptive to the
rule and regulations and let the complexities of life settle at their own pace. Society follows virtue
driven by persistence and personal adaptability is important. They are more focused on relationship
and market positions. They prefer safe investments over the risky ones.
(6) Indulgence index (IVR): This trait has a very short history added following Minkov (2009). This
dimension refers to the situation how individuals have control over their desires. People in indulge
69
societies satisfy their natural human desires of enjoying life by allowing free gratification. Whereas
restraint societies are relatively more restrictive and are more regulated by strict norms. The upbringing
of individuals has an impact on the way they control their impulses and desires. Individuals in highly
indulged societies exhibit weak control over desires, whereas societies with a low level of indulgence
control their urges strongly.
3.2.4.1 Model to Test the Impact of Culture on Herding Behavior.
In order to test and measure the effect of national culture on the presence of herding behavior, this
study employed the methodology proposed by Chiang et al. (2000) and Chiang & Zheng (2010) model
are employed. The selection of these models is based on their nature as they measure herding as a static
phenomenon. Culture is a non-variant variable and changes very slowly over time, therefore,
employment of culture in the time-varying model is almost impossible. After testing the presence of
herding through the above-stated model we include culture variable in the model, culture is treated as
a modulating variable and included as a dummy variable in model proposed by Chiang et al. (2000)
and Chiang & Zheng (2010), for each dimension median value is calculated (using all countries), if
the value lies above median it takes the value of 1, and 0 otherwise.
tti
s
tititititi RmDCRmRmRmCSAD 2
,,4
2
,3,2,1, ][][ (15)
Where CSAD is the cross-sectional absolute deviation of the market on the day for stock market
i, Rmi,t is the market return of that market on that particular day. The relationship between the absolute
value of Rmi,t, squared term of Rmi,t and CSAD represent the existence of herding behavior. The
significant negative value of γ3 indicates the presence of herding behavior. DC is the dummy of each
individual dimension of culture (PDI, IDV, MAS, UAI, LTO, and IVR). Each dimension bear the
value of 1 if that specific country lies above median average and zero otherwise. For 6 cultural variable,
this study runs six regressions of equation (15) by including one dummy of one cultural dimension at
70
a time. This process is done to avoid the problem of dummy trap.5 The analysis is done by ordinary
least square regression used by (Blasco, Corredor, & Ferreruela, 2017). The presence of significant γ4
indicates the effect of each cultural dimension on the intensity of herding behavior. If the value is
negative then herding intensity decreases and vice versa.
The Effects of Macroeconomic Shocks on Herding
The main issue being explored in this section is the effects of macroeconomic shocks on the
estimated herd measures towards the market. However, the first thing needs to be done is to catch
unexpected components of the selected macroeconomic variables. For this purpose, the Box–Jenkins
methodology of an ARIMA (p, d, q) model is used. A fundamental notion behind the selection of this
model is the principle of parsimony. These models are appropriate for forecasting and provide better
results than over parametrized models6. According to Wasserfallen (1989), for the proxy of
unexpected elements in macroeconomic variables, the in-sample residuals from ARIMA models can
be utilized, with an expectation that they are partly rational. However, the fundamental forecast is
based on the historical values of the series itself. But the use of residuals from ARIMA model can
result in consistent parameter estimates but overstated levels of significance (Pagan, 1984). According
to ARIMA (Autoregressive integrated moving average) models future value exhibit a functional
relationship with past points, current points, and white noise error terms. This method can generate
rich hypothesis on the basis of few parameters only and the model can be extended to a random number
of parameters. To begin with, the general form of an ARIMA (p, d, q) model we first need to generate
the AR and MA process. An autoregressive process of order AR(p) can be written as,
𝑥𝑡 = 𝜑1𝑥𝑡−1 + 𝜑2𝑥𝑡−2 + 𝜑3𝑥𝑡−𝑝 + 𝜖𝑡
The moving order process for q order MA can be written as,
5 See, Astriou and Hall, (2007) 6 See Abeysinghe and Rajaguru, (2004).
71
𝑥𝑡 = 𝜖𝑡 + 𝜗1𝑥𝑡−1 + 𝜗2𝑥𝑡−2 + 𝜗3𝑥𝑡−𝑞
The ARMA process with (p,q ) order can be written as
𝑥𝑡 = 𝜑1𝑥𝑡−1 + 𝜑2𝑥𝑡−2 + 𝜑3𝑥𝑡−𝑝 + 𝜖𝑡 + 𝜗1𝑥𝑡−1 + 𝜗2𝑥𝑡−2 + 𝜗3𝑥𝑡−𝑞
Where φ and are estimation parameters and 𝜖 represent the white noise stochastic error terms.
Next step is to generate a non-stationary series and define the first order difference of yt to make a
stationary series.
∆𝑦𝑡 = 𝑦𝑡 − 𝑦𝑡−1
Or by using a Lag operator we can have
∆𝑑𝑦𝑡 = (1 − 𝐿)𝑑𝑦𝑡
In the final form, ARIMA (p,d,q) model can be reported as follows
𝜑𝑝(𝐿)(1 − 𝐿)𝑑𝑦𝑡 = 𝜗𝑞(𝐿)𝜖𝑡 (16)
Where, L is the lag operator, d is the number of differences for becoming the stochastic variable Y
stationary, φp(L) = 1−φ1L−・ ・ ・−φpLp and q(L) = 1−1L−・ ・ ・−qL
q . The residuals of this
model are going to be used for investigating the effects of macroeconomic shocks on herding. These
residuals are then added to the Hwang and Salmon (2004) model.
Herding and Financial Contagion:
Empirical evidence suggests that the economic fundamentals predict the occurrence of a financial crisis
(Kaminsky & Reinhart, 2000), but in reality crisis usually occur when the fundamentals is weak or
may not occur when they are sound. This type of financial crisis is triggered by the herding behavior
of investor, where investors simply imitate the behavior of others.
To examine nature of contagion effect and interrelationship this study first calculate volatility by using
GARCH(1,1) model developed by Bollerslev (1986) and used for the calculation of correlation as,
Yt =M+Et Et ~ N(0, Ht) (17)
72
Where Yt = [𝑦𝑖,𝑡
𝑦𝑘,𝑡]is a (2x1) vector containing the first difference of herding measures, where k is the
originator country and i represent rest of the countries. Conditional mean is represented by (2x1) vector
M=[𝜇𝑦𝑖
𝜇𝑦𝑘] and 2x2 Ht is the conditional covariance matrix.
The conditional variance-covariance equations of a diagonal VECH bivariate GARCH (1,1) can be
written as,
VECH(Ht) = D + EVECH(Et-1 Et-1) + FVECH(Ht-1 H
t-1) (18)
Where D is a 3x1 vector containing intercepts in the conditional variance-covariance equations, E and
F are 3 x 3 diagonal matrices consist of parameters with lagged square disturbance and on the lagged
variance or covariance respectively. The diagonal variance can be written as
ℎ11,𝑡 = 𝑑01 + 𝑒11𝜖1,𝑡−12 + 𝑓11ℎ11,𝑡−1 (19a)
ℎ12,𝑡 = 𝑑02 + 𝑒22𝜖1,𝑡−1𝑔2,𝑡−1 + 𝑓22ℎ12,𝑡−1 (19b)
ℎ22,𝑡 = 𝑑03 + 𝑒33𝜖2,𝑡−12 + 𝑓33ℎ22,𝑡−1 (19c)
The coefficient e11 and e33 represent the ARCH process in residuals from country i and originator
country k respectively. The parameters e22 and e33 are the covariance GARCH parameters between
country i and k respectively.
Whereas conditional correlation is calculated as follows (Alexander, 2001)
�̂�𝑡𝑘𝑖 =
�̂�𝑘𝑖,𝑡�̂�𝑘,𝑡�̂�𝑖,𝑡
⁄ (20)
Where k is the first difference of herd measure of US index and i the estimated herding measures from
the rest countries of the sample.
Certain macroeconomic variable and Crisis dummy are used to develop a model to test the changes in
herding correlation following the approach used by Messis & Zapranis (2014). The changes in the
correlation of herding measures between the sample countries during macroeconomic shocks and crisis
73
period can be tested econometrically by using an OLS regression. This methodology is used by (Chiang
et al., 2007; Coudert & Gex, 2010 and Messis & Zapranis, 2014)
𝜌𝑡𝑘,𝑖 = 𝛼𝑘,𝑖 + 𝛽𝑘𝜌𝑡−1
𝑘,𝑖 + 𝛾1𝑘𝐷𝑐𝑟,𝑡 + 𝛾2
𝑘𝐷𝐸𝑅,𝑡 + 𝛾3𝑘𝐷𝐼𝑃,𝑡 + 𝛾3
𝑘𝐷𝑖,𝑡 + 𝛾4𝑘𝐷𝑀𝑆,𝑡 + 𝑢𝑡
𝑘,𝑖 (20)
Where, t-1, represent the lagged value of correlation measures between the originator countries
and the US. Dcr,t, DER,t, DIP,t, Di,t, DMS,t, are the dummies of crisis and macroeconomic shocks, whereas
k and i represent the respective countries and ut is the residual term. For the crisis dummy Global
financial crisis of 2008 is included. The dummy Dcr,t takes the value of 1 during a period from August
2007 to April 2009 and 0 otherwise. This crisis starts when an announcement is made by BNP Paribas
to cease the activities of three hedge funds. These funds are specialized in the US mortgage debt
(known as subprime mortgage crisis) and peaked with another shock of the collapse of Lehman
Brothers on September 15, 2008, the effects of this crisis concluded in the first quarter of 2009.
The other dummies DER,t, DIP,t, Di, t, DMS,t represent macroeconomic shocks dummies. These
dummies represent extreme shocks that are calculated on the basis of ARIMA residuals represented in
section 3.2.5. These shocks represent the extreme 10 percent of each ARIMA model residuals worst
realized values. The shocks are discussed under the extreme value theory and are generally lies in the
range of 1% to 10% (Longin & Solnik’s, 2001; Klein, 2013). Messis & Zapranis (2014) use the extreme
10% shocks to examine the maximum possible shocks, so this study follows the same approach. The
dummies take the value of 1 on each worst extreme 10 percent observation and zero otherwise.
In the above equation originator countries (k) are the markets of Asia, Asia Pacific and Europe,
whereas i represent the US market. The significance and sign of each dummy represent the effect of
shock in each variable (exchange rate, Industrial production index, interest rates and money supply)
on the correlation coefficient of US herding measure. If the correlation coefficient change due to any
of these factors in any market. Then it can be stated that the contagion of herding exists due to a
macroeconomic shock or crisis.
74
The experimental literature explore the effect of various macroeconomic variables (Belgacem,
and Lahiani, 2013; Messis & Zapranis, 2014; and Blasco 2009). However, the variable that have direct
effect on discount rate or expected cash flows have been found to have more consistent in the influence
(Gong & Dai (2017). Case (2000) and Catte, Girouard, Price, & André (2004) figure out the sensitivity
of asset prices to macroeconomic shocks including, changes in interest rates, industrial production,
and unexpected changes in the money supply. Similarly, Gong & Dai (2017) analyze significant effect
of Interest rate and exchange rates on herding behavior of investors in US market. Therefore, this
study incorporate interest rates, that is directly related with discount rate; exchange rate and growth
rate that influence expected cash flows and finally money supply that have impact on liquidity.
75
Chapter 4
4. Results and Discussion
Return Dispersion Model Based on Christie & Huang (1995)
In the first section of analysis, this study discusses the results based on the return dispersion model of
Christie & Huang (1995).
Descriptive Statistics of Cross-Sectional Standard Deviation
Table 4.1 presents descriptive statistics of cross-sectional standard deviation. Panel A reports
summary statistics of developed markets, whereas panel B gives a summary of emerging and frontier
markets around the globe as per MCSI country classification. When individual returns move in the
same direction as the market returns the cross-sectional standard dispersion is low. When the deviation
of individual and market returns increases, the CSSD also increases.
Among developed markets, the average daily CSSD of Greece is highest 1.575% followed by
Portugal at 1.545%. US exhibit lowest average CSSD of 0.605 among all developed markets. The
average cross-sectional dispersion of Norway (1.302), Hong Kong (1.290), Singapore (1.255),
Denmark (1.274), Sweden (1.130), Belgium (1.125), Austria (1.108) and Netherland (1.033) are above
1%. Whereas, the CSSD for Germany (0.816), Italy (0.995), Spain (0.989), New Zealand (0.961),
Australia (0.915), France (0.889), Switzerland (0.872) Japan (0.827) and UK (0.792) are all below
1%. Similarly, like returns Greece market exhibit highest standard deviation of 1.086% and US
market has the lowest standard deviation of CSSD among all developed markets. The maximum range
of CSSD is also observed in Greece at 8.003% and lowest in US market at 3.022.
76
The minimum CSSD of all countries is approximately equal to zero which may be due to least
dispersion in few days over the entire period. The study used standard dispersion measure followed by
Christie & Huang (1995), therefore, all observations are greater than zero.
Table 4.1: Descriptive statistics of Cross sectional Standard Deviation Panel A: Developed Market
Country Obs Mean St. Dev Max Min Skewness Kurtosis
Australia 6028 0.915 0.470 4.433 0.000 1.651 8.601
Austria 6036 1.108 0.755 6.324 0.000 2.073 10.891
Belgium 6452 1.125 0.665 5.195 0.000 1.447 6.833
Denmark 6362 1.274 0.934 8.159 0.000 1.940 9.798
France 4572 0.889 0.518 3.259 0.000 1.312 5.137
Germany 9130 0.816 0.533 4.710 0.000 1.901 9.095
Greece 6087 1.575 1.086 8.003 0.000 2.006 9.520
Hong Kong 6427 1.290 0.809 5.903 0.000 1.591 7.412
Italy 4696 0.995 0.521 3.542 0.000 1.208 5.478
Japan 9130 0.827 0.541 4.686 0.000 1.638 8.041
Netherland 8608 1.033 0.666 5.185 0.000 1.806 8.046
New Zealand 3913 0.961 0.528 4.050 0.000 1.522 7.346
Norway 8417 1.302 0.763 6.333 0.000 1.691 8.464
Portugal 6000 1.547 1.303 9.715 0.000 2.398 11.097
Singapore 4588 1.255 0.774 5.498 0.000 1.599 7.144
Spain 7523 0.989 0.640 5.631 0.000 2.084 10.267
Sweden 7658 1.130 0.731 5.825 0.000 1.812 8.513
Switzerland 6569 0.872 0.534 4.212 0.000 1.578 7.285
UK 7584 0.792 0.501 4.029 0.000 1.898 8.779
US 9130 0.605 0.369 3.022 0.000 1.957 9.039
Panel B: Emerging and Frontier markets
Country Obs Mean St. Dev Max Min Skewness Kurtosis
China 4628 0.926 0.608 4.296 0.000 1.367 3.223
77
India 5479 0.840 0.505 4.589 0.000 1.665 9.155
Indonesia 4173 1.461 0.861 6.097 0.000 1.386 6.417
Korea 5340 1.461 0.854 6.060 0.000 1.300 6.115
Malaysia 5480 0.881 0.567 4.091 0.000 1.628 7.371
Pakistan 5479 1.317 0.859 7.019 0.000 1.726 8.668
Philippines 5565 1.290 0.830 6.079 0.000 1.481 6.926
Sri Lanka 5456 1.119 0.849 6.077 0.000 1.679 7.706
Taiwan 5478 1.088 0.667 4.029 0.000 0.977 4.222
Thailand 5469 1.319 0.834 5.906 0.000 1.512 6.886
Turkey 5974 1.938 1.221 9.813 0.000 1.467 6.253
Note: statistical properties of Cross-sectional standard deviation calculated through 1
)(1
2
,,
N
RR
CSSD
N
i
tmti
t
Among developing markets the average daily CSSD of turkey is highest 1.938% and India
exhibit lowest average CSSD of 0.84 among all developed markets. The average cross-sectional
dispersion of Turkey (1.938), Indonesia (1.461), Korea(1.461), Thailand(1.319), Pakistan(1.317),
Philippines (1.29), Sri Lanka(1.119) Taiwan(1.088) are above 1%. Whereas, the CSSD for China
(0.926), Malaysia (0.881), India (0.84) are all below 1%. Similarly, like returns, Turkish market
exhibit a highest standard deviation of 1.221% and Indian market has the lowest standard deviation of
CSSD 0.505% among all developed markets. The maximum range of CSSD is also observed in turkey
at 9.813%.
By looking at the mean value of CSSD it is observed that large economies like Australia, UK,
Japan, and US have lower value as compared to other emerging economies and frontier markets in the
data set. A lower mean value means lower cross-sectional dispersion among returns as compared to
other developing economies in the data set. A higher standard deviation may be indicative of unusual
cross-sectional dispersion in returns due to unexpected shock or crisis situation (Chiang & Zheng,
2010). It means that emerging and frontier markets are relatively more sensitive to the unexpected
shocks and returns are more volatile in these markets compared to the developed economies.
78
Estimates of Herding Measure in Extreme Market Conditions
Table 4.2, presents the result of return dispersion model in all developed, emerging and frontier
markets. It is observed that 1 and 2 coefficients are all significant and positive in the markets under
study. All estimations are based on OLS method using Newey- West heteroscedasticity-consistent
standard errors. The period of market stress is classified as the upper and lower 1% and 5% of the
market returns in all distributions. For almost all markets analyzed, this study does not observe the
presence of herding behavior in extreme market events or a period of market stress both in upper and
lower market. For all developed, emerging and frontier markets regression results yield positive and
significant 1 and 2 values. These findings are consistent with Christie & Huang (1995), they observe
similar results in US markets. Chen (2013) also observe similar results in a set of 69 developed,
emerging and frontier markets and observe positive and significant coefficients in both rising and
falling markets during 2000 to 2009. The added contribution of this study is to obtain the similar result
using industrial indices by employing larger data set which starts from the time period of availability
for different economies to 2015.
Table 4.2: Estimates of herding measure in extreme market conditions: CSSD Panel A: Developed Markets
Criterion for extreme = 1% Criterion for extreme = 5%
Country
Australia 0.009*** 0.010*** 0.008*** 0.009*** 0.005*** 0.004***
Austria 0.011*** 0.017*** 0.015*** 0.010*** 0.012*** 0.010***
Belgium 0.011*** 0.011*** 0.013*** 0.010*** 0.007*** 0.008***
Denmark 0.012*** 0.020*** 0.022*** 0.011*** 0.013*** 0.015***
France 0.009*** 0.008*** 0.003*** 0.009*** 0.004*** 0.002***
Germany 0.008*** 0.014*** 0.012*** 0.007*** 0.008*** 0.008***
Greece 0.015*** 0.018*** 0.017*** 0.014*** 0.013*** 0.012***
Hong Kong 0.013*** 0.020*** 0.032*** 0.012*** 0.010*** 0.015***
79
Italy 0.010*** 0.008*** 0.009*** 0.009*** 0.005*** 0.006***
Japan 0.008*** 0.009*** 0.009*** 0.008*** 0.005*** 0.006***
Netherland 0.010*** 0.016*** 0.016*** 0.009*** 0.009*** 0.009***
New Zealand 0.009*** 0.011*** 0.009*** 0.009*** 0.007*** 0.007***
Norway 0.013*** 0.018*** 0.017*** 0.012*** 0.010*** 0.010***
Portugal 0.015*** 0.020*** 0.032*** 0.013*** 0.019*** 0.022***
Singapore 0.012*** 0.012*** 0.014*** 0.012*** 0.008*** 0.011***
Spain 0.010*** 0.013*** 0.012*** 0.009*** 0.008*** 0.008***
Sweden 0.011*** 0.014*** 0.016*** 0.010*** 0.008*** 0.010***
Switzerland 0.009*** 0.011*** 0.016*** 0.008*** 0.007*** 0.008***
UK 0.008*** 0.010*** 0.010*** 0.007*** 0.006*** 0.006***
US 0.006*** 0.008*** 0.008*** 0.006*** 0.004*** 0.004***
Panel B: Emerging and Frontier markets
Criterion for extreme = 1% Criterion for extreme = 5%
Country
China 0.009*** 0.005*** 0.007*** 0.009*** 0.006*** 0.006***
India 0.056*** 0.013*** 0.051*** 0.058*** 0.020*** 0.061***
Indonesia 0.014*** 0.017*** 0.020*** 0.013*** 0.011*** 0.014***
Korea 0.014*** 0.015*** 0.014*** 0.014*** 0.010*** 0.011***
Malaysia 0.014*** 0.015*** 0.014*** 0.014*** 0.010*** 0.011***
Pakistan 0.013*** 0.027*** 0.022*** 0.012*** 0.016*** 0.014***
Philippines 0.013*** 0.011*** 0.015*** 0.012*** 0.009*** 0.011***
Sri Lanka 0.011*** 0.019*** 0.021*** 0.010*** 0.012*** 0.016***
Taiwan 0.011*** 0.014*** 0.017*** 0.010*** 0.012*** 0.012***
Thailand 0.013*** 0.018*** 0.019*** 0.012*** 0.011*** 0.013***
Turkey 0.019*** 0.028*** 0.028*** 0.018*** 0.017*** 0.019***
Note: the results of estimation through Christie and Huang (1995) model t
L
t
LU
t
U
t DDCSSD 21 ***indicate statistical significance at 1%. **Indicate statistical significance at 5%. *Indicate statistical significance at 10%.
These findings are in line with previous research as in developed markets. Chang et al., 2000,
and Demirer, Gubo, & Kutan, A. M. (2007) find no evidence of herding in US market specifically in
80
a period of large price swings. Caparrelli et al. (2004) analyze Christie & Huang (1995) model and
find significant positive coefficients βL and βU that refute the presence of herding behavior for the
period 1988-2001 in extreme market conditions. Gleason et al. (2004) investigate herding in AMEX
stock exchange by employing intraday data for Exchange Traded Funds (ETFs) and observe evidence
against herding in extreme market stress during the period of 1999-2000. Henker et al. (2006) employ
intraday data and investigate herding in Australian equity markets using Christie & Huang (1995)
model. They observe that in extreme market events dispersion of individual returns to market returns
increases supporting the assumption of rational asset pricing model and no evidence in favor of herding
observed during extreme market stress. Blasco & Ferreuela (2008) analyzed the developed markets of
Germany, United Kingdom, United States, Mexico, Japan, Spain, and France but only Spanish market
exhibit aggregate market behavior. Demirer & Kutan (2006) find no evidence of herding during the
period of large price swings in the Chinese market. Similarly, Tan et al. (2008) observe similar findings
in Chinese A & B market shares. Demirer, Kutan, & Chen, (2010) employed CSSD model during a
period of 1995 to 2000 and find no evidence of herding in Taiwanese stock market during extreme
stress in the stock market. Similarly, Huang, Lin, & Yang, (2015) found no evidence of herding during
market stress in the taiwanese stock market. Purba and Faradynawati (2011) examine herding in the
indonesian market during extreme market condition and find no evidence of herding.
It is concluded that during the period of large price swings equity return dispersion increases rather
than decreasing. These findings are consistent with the prediction of CAPM as the rational asset pricing
models state that equity return dispersion increases during the turbulent period.
Return Dispersion Model based on Chiang et al. (2000)
Descriptive Statistics of Cross-Sectional Absolute Deviation of Returns
Table 4.3 reports the results of summary statistics of cross-sectional absolute deviation of
returns. Among developed markets, the average daily CSAD of Greece is highest 1.202% followed by
81
Portugal at 1.036%. US exhibit lowest average CSSD of 0.453 among all developed markets. The
average cross-sectional dispersion of Greece (1.202) and Portugal (1.036), are above 1%. Whereas,
the CSAD for Germany Norway (0.962), Hong Kong (0.928), Denmark(0.927), Singapore(0.91),
Sweden(0.828), Austria(0.811), Belgium(0.767), Netherland (0.763), Italy(0.743), Spain(0.722),
France (0.688), New Zealand (0.688), Australia(0.666), Switzerland(0.648), Japan(0.607),
Germany(0.602), UK(0.585), and US(0.453) are all below 1%. Similarly, like returns Greece market
exhibit highest standard deviation of 0.758% and US market has the lowest standard deviation of
CSAD among all developed markets at 0.275. The maximum range of CSAD is also observed in
Greece at 5.482%.
Among developing markets the average daily CSAD of turkey is highest at 1.43% and China exhibits
lowest average CSAD of 0.689 among all developed markets. The average cross-sectional dispersion
of Turkey (1.43), India (1.112), Korea (1.082), Malaysia (1.082), Indonesia (1.067) are above 1%.
Whereas, the CSAD for Thailand (0.992), Philippines (0.948), Pakistan(0.905), Sri Lanka (0.81),
Taiwan(0.758), China (0.689) are all below 1%. Similarly, like returns Turkish market exhibits
highest standard deviation of 0.888% and Chinese market has the lowest standard deviation of CSAD
0.452% among all emerging and frontier markets.
The minimum CSAD of all countries is approximately equal to zero which may be due to least
dispersion in few days over the entire period. The study used absolute deviation measure followed by
Chiang et al. (2000), therefore, all observations are greater than zero.
It is observed that the average CSAD of developed economies like US, UK, Germany, is lowest
among all developed markets, however in emerging economies Chinese market exhibit lowest mean
value among other markets in the sample. A lower mean value means lower cross-sectional absolute
dispersion among returns as compared to other economies in the data set. A higher standard deviation
may be indicative of unusual cross-sectional dispersion in returns due to unexpected shock or crisis
situation (Chiang & Zheng, 2010). It means that emerging and frontier markets are relatively more
82
sensitive to the unexpected shocks as these markets are characterized by less sophisticated institutional
structures, regulatory frameworks are weak and less protection is available to the investor as markets
are volatile compared to the developed economies (Demirer & Kutan, 2006).
Table 4.3: Descriptive Statistics of Cross-Sectional Absolute Deviation CSAD Country Obs Mean St. Dev Max Min Skewness Kurtosis
Australia 6028 0.666 0.328 2.880 0.000 1.463 7.396
Austria 6036 0.811 0.530 4.198 0.000 1.857 6.487
Belgium 6452 0.767 0.444 3.730 0.000 1.505 7.436
Denmark 6362 0.927 0.646 4.995 0.000 1.548 6.897
France 4572 0.688 0.411 3.156 0.000 1.500 6.300
Germany 9130 0.602 0.381 3.252 0.000 1.852 8.938
Greece 6087 1.202 0.758 5.482 0.000 1.490 6.837
Hong Kong 6427 0.928 0.563 4.491 0.000 1.620 8.212
Italy 4696 0.743 0.386 2.972 0.000 1.245 5.796
Japan 9130 0.607 0.387 2.996 0.000 1.501 7.126
Netherland 8608 0.763 0.489 4.087 0.000 1.873 8.623
New Zealand 3913 0.688 0.344 2.592 0.000 1.182 6.242
Norway 8417 0.962 0.534 4.071 0.000 1.395 6.851
Portugal 6000 1.036 0.777 5.967 0.000 2.088 9.637
Singapore 4588 0.910 0.537 3.976 0.000 1.471 6.666
Spain 7523 0.722 0.443 4.433 0.000 2.005 10.539
Sweden 7658 0.828 0.517 4.563 0.000 1.773 8.741
Switzerland 6569 0.648 0.389 2.889 0.000 1.565 7.270
UK 7584 0.585 0.363 2.918 0.000 1.891 8.755
US 9130 0.453 0.275 2.394 0.000 2.025 9.574
Panel B: emerging and Frontier markets
Country Obs Mean St. Dev Max Min Skewness Kurtosis
China 4628 0.689 0.452 4.046 0.000 1.552 4.846
India 5479 1.112 0.645 5.360 0.000 1.208 5.745
83
Indonesia 4173 1.067 0.618 4.592 0.000 1.346 6.597
Korea 5340 1.082 0.611 4.092 0.000 1.158 5.558
Malaysia 5340 1.082 0.611 4.092 0.000 1.158 5.558
Pakistan 5479 0.905 0.565 4.645 0.000 1.546 7.719
Philippines 5565 0.948 0.582 3.984 0.000 1.243 5.814
Sri Lanka 5456 0.810 0.616 4.992 0.000 1.756 8.643
Taiwan 5478 0.758 0.456 3.474 0.000 0.986 4.608
Thailand 5469 0.992 0.624 4.863 0.000 1.574 7.471
Turkey 5974 1.430 0.888 6.665 0.000 1.464 6.138
Note: statistical properties of measure cross-sectional absolute deviation calculated through
N
i
tmtit RRN
CSAD1
,, ||1
Estimates of Herding Measure Based on Constant Coefficient Model
Table 4.4 reports the results of return dispersion model based on Chang et al. (2000). They observe
herding behavior as a nonlinear function of market returns and cross-sectional absolute deviation. If
the nonlinear term in the model is negative and significant then herding is present in the market and
insignificant term reports the absence of herding and presence of rationality in the market.
Table 4.4: Estimates of herding measure based on constant coefficient model: CSAD Panel A: Developed Markets
Country R2
Australia 0.005*** 0.291*** 1.001 0.22
Austria 0.005*** 0.542*** -3.464*** 0.35
Belgium 0.005*** 0.419*** -1.227 0.32
Denmark 0.005*** 0.693*** -5.084*** 0.40
France 0.005*** 0.193*** -0.449 0.17
Germany 0.004*** 0.394*** -1.597*** 0.40
Greece 0.007*** 0.480*** -2.382*** 0.33
Hong Kong 0.006*** 0.385*** -1.832*** 0.31
Italy 0.005*** 0.238*** -1.076*** 0.21
84
Japan 0.004*** 0.252*** -1.204*** 0.20
Malaysia 0.008*** 0.334*** -1.729*** 0.26
Netherland 0.005*** 0.396*** -1.230*** 0.35
New Zealand 0.005*** 0.538*** -5.061*** 0.29
Norway 0.006*** 0.421*** -2.149*** 0.32
Portugal 0.005*** 1.017*** -9.552*** 0.44
Singapore 0.006*** 0.469*** -3.654*** 0.27
Spain 0.005*** 0.362*** -1.586*** 0.30
Sweden 0.005*** 0.415*** -2.177*** 0.32
Switzerland 0.004*** 0.389*** -1.993*** 0.34
UK 0.004*** 0.279*** -1.045*** 0.23
US 0.003*** 0.185*** -0.399*** 0.21
Panel B: Emerging and Frontier Markets
Country R2
China 0.004*** 0.289*** -2.854*** 0.19
India 0.006*** 0.294*** -1.064*** 0.26
Indonesia 0.006*** 0.678*** -5.429*** 0.41
Korea 0.008*** 0.334*** -1.729*** 0.26
Malaysia 0.008*** 0.334*** -1.729*** 0.26
Pakistan 0.006*** 0.406*** -1.777*** 0.35
Philippines 0.006*** 0.433*** -2.815*** 0.28
Sri Lanka 0.004*** 0.819*** -5.672*** 0.56
Taiwan 0.004*** 0.479*** -4.834*** 0.38
Thailand 0.006*** 0.415*** -1.940*** 0.33
Turkey 0.008*** 0.462*** -1.548*** 0.39
Note: the results of estimation through Chang et al. (2000) model ttmtmt RRCSAD 2
,3,21
***indicate statistical significance at 1%. **Indicate statistical significance at 5%. *Indicate statistical significance at 10%.
From the Panel A and B of table 4.4, it is observed that herding is present in all the developed,
emerging and frontier markets except Australia, Belgium, and France. These results are consistent with
85
Henker et al. (2006) and Chang & Zheng (2010) as they observe insignificant herding behavior in
Australian and French markets. It means that that information is communicated efficiently to investors
in Australian, Belgium and French equity market. Herding is more likely to be present in emerging
and developing markets due to their market characteristics including weak regulatory system,
immature financial system, control of the market in the hands of few institutional investors, the
existence of speculators, and transmission of highly volatile capital flows from international markets
(Economou et al., 2000). The negative significant sign of coefficient reports presence of herding
behavior in rest of the markets at 1% significance level.
The empirical literature provide support to Chang et al. (2000) model in both developed and
developing markets. Caparrelli et al. (2004) employ Chang et al. (2000) model and observe herding
under normal condition, using the same model they also observe an asymmetry in herding behavior
which is more pronounced during up market condition than down market. Economou, Kostakis, &
Philippas, (2011) examine four markets of Europe during 1998 to 2008 namely Spanish, Italian,
Portuguese and Greek and find strong evidence of herding in Italian and Greek stock market. Whereas
they Portuguese market shows unclear results depending on the choice of calculation of portfolios.
Spanish market provides insignificant results and rejects the hypothesis of the presence of
herding. Lao & Singh (2011), observe significant herding behavior in the stock market of China and
India. Tan et al. (2008) use Chang et al. (2000) methodology and analyze herding behavior in
asymmetric patterns of returns, trading volume and level of volatility in Chinese stock market Demirer
et al. (2014) investigate the existence of herding in American Depository Receipts (ADRs) and infer
more evident herding at sector level than country level. Hsieh, Yang, Yang, & Lee (2011) investigate
12 Asian markets by using return dispersion models and find significant herding behavior. Garg &
Gulati (2013) find no evidence of herding in Indian stock market by employing Chang et al. (2000)
under normal market conditions. Similarly, Javaira & Hassan (2015) identify no evidence of herding
86
during normal market conditions but significant herding behavior during a crisis situation in Pakistani
stock market.
Mobarek, Mollah, & Keasey (2014) examine herding behavior in a large set of European
markets using sector level data and find no evidence of herding behavior find no evidence of herding
behavior under normal market condition in PIIGS (Portugal, Italy, Ireland, Greece, Spain),
Nordic(Finland, Norway Sweden Denmark) and Continental Europe (France Germany) with the
exception of Finland and Germany. Henker et al. (2006) find a nonlinear relationship in returns and
cross-sectional dispersion using daily data in Australian marker, but they observe insignificant result
when intraday data is employed. Zhou & Anderson (2013) employ quantile regression to the Chang et
al. (2000) methodology in US equity REIT for a period of 1980 to 2010. They find herding in bear
markets and normal market conditions only, and in high quantile of return dispersion.
It is observed that all developed markets except Australia, Belgium, and France exhibit herding
behavior. However, the presence of herding is observed in all emerging and frontier markets. These
outcomes are consistent with previous literature as herding is more likely to be present in emerging
and developing markets than in developed markets (Economou et al., 2000).
Estimates of Herding based on Chang and Zheng (2010)
Table 4.5 reports the results based on the return dispersion with asymmetric effect proposed by
Chang & Zheng (2010). Similar to the Chang et al. (2000) model all the developed, emerging and
frontier markets exhibit similar results. Herding is absent in Australia and Belgium but French market
exhibit significant herding behavior at 10% level of significance.
The underlying assumption of adding the term≠ 0, is to observe the relative asymmetry in
market returns and cross-sectional dispersion. As this coefficient is significant and positive, therefore
+ captures the relationship between return dispersion and market returns here the market returns
have a greater effect on cross-sectional returns return dispersion as Rm > 0. The effect of absolute
87
market return is larger when the market returns are positive (+ ) than when the market returns are
negative (- . These results are calculated through the ratio (γ2+ γ3) / (γ3- γ2).
For most of the markets is significant and positive, however, almost all markets except Australia
and Belgium report significant herding behavior.
Table 4.5: Estimates of asymmetric herding behavior based on constant coefficient model: CSAD Panel A: Developed Markets
Country R2
Australia 0.005*** 0.018* 0.286*** 1.275 0.22
Austria 0.005*** 0.004 0.543*** -3.442*** 0.35
Belgium 0.005*** 0.014** 0.418*** -1.155 0.32
Denmark 0.005*** 0.024*** 0.689*** -4.912*** 0.40
France 0.005*** 0.010* 0.195*** -0.493* 0.17
Germany 0.004*** 0.010** 0.394*** -1.552*** 0.40
Greece 0.006*** 0.037*** 0.387*** -1.811*** 0.31
Hong Kong 0.006*** 0.015*** 0.293*** -1.021*** 0.26
Italy 0.005*** 0.009 0.239*** -1.060*** 0.22
Japan 0.004*** 0.027*** 0.250*** -1.099*** 0.20
Netherland 0.005*** 0.010* 0.395*** -1.188*** 0.35
New Zealand 0.005*** 0.013*** 0.536*** -4.848*** 0.29
Norway 0.006*** 0.022*** 0.417*** -1.977*** 0.32
Portugal 0.005*** 0.031** 1.021*** -9.571*** 0.45
Singapore 0.006*** 0.031*** 0.464*** -3.448*** 0.28
Spain 0.005*** 0.002*** 0.362*** -1.587*** 0.30
Sweden 0.005*** 0.019*** 0.416*** -2.181*** 0.32
Switzerland 0.004*** 0.012** 0.389*** -1.928*** 0.35
UK 0.004*** 0.007 0.278*** -0.997*** 0.23
US 0.003*** 0.001 0.185*** -0.394*** 0.21
88
Panel B: Emerging and Frontier Markets
Country R2
China 0.004*** 0.001 0.289*** -2.849*** 0.17
India 0.006*** 0.015*** 0.293*** -1.021*** 0.26
Indonesia 0.006*** 0.045*** 0.666*** -4.967*** 0.42
Korea 0.008*** 0.013** 0.334*** -1.724*** 0.26
Malaysia 0.008*** 0.013 0.334*** -1.724*** 0.26
Pakistan 0.006*** 0.010 0.408*** -1.789*** 0.35
Philippines 0.006*** 0.030*** 0.440*** -2.938*** 0.29
Sri Lanka 0.004*** 0.041*** 0.811*** -5.542*** 0.56
Taiwan 0.004*** 0.007 0.478*** -4.794*** 0.38
Thailand 0.006*** 0.014 0.415*** -1.948*** 0.33
Turkey 0.008*** 0.016*** 0.460*** -1.523*** 0.39
Note: Properties of the estimates calculated through model ttmtmtmt RRRCSAD 2
,4,3,21
***indicate statistical significance at 1%. **Indicate statistical significance at 5%. *Indicate statistical significance at 10%.
Chang & Lin (2015) employ this model and observe herding behavior in a set of 49 developed,
emerging and frontier markets and observe significant herding behavior in most of the markets.
Similarly, Demirer, Kutan, & Zhang (2014) employ this model to test herding in ADR issues in 19
markets within country and sector-based portfolios.
It is observed that all developed markets except Australia, and Belgium exhibit herding behavior. By
setting French market also exhibit herding behavior. Similar to Chang et al. (2000), the presence
of herding is observed in all emerging and frontier markets. These findings are consistent with previous
studies as herding is more likely to be present in emerging and developing markets than in developed
markets (Economou et al., 2000).
89
Time-Varying Model of Herding Behavior
Descriptive Statistics of Time-Varying Herding Parameter
Table 4.6 shows the descriptive statistics of the dynamic herding measure 2,t for all developed,
emerging and frontier markets. Panel A of the table 4.6 report summary statistics of time-varying
herding estimates of the developed markets. Except for Australia, Belgium, and Portugal all countries
exhibit a negative mean value of the herding measure. These signs are supported in most of the
countries with negative skewness, it means that herding is widely present in most of the developed
markets. These results are in contradiction with Chiang et al. (2013), they observe positive coefficient
of herding estimates in US market during the sample period and the negative mean coefficient in the
Australian market. However, the results of Chinese, Hong Kong, and Singaporean markets are in line
with this study. According to Garbade & Watchtel (1978), the coefficient estimates of OLS estimation
are not average of the time-varying coefficients.
It is observed that the mean value of AU, OE, BG, DK, BD, GR, HK, IT, NL, NZ, NW, PT,
SG, ES, SD, SW, UK, and US, are 1.00%, -5.40%, 11.60%, -0.60%, -2.50%, -3.40%, -5.40%, -1.20%,
-1.10%, -3.30%, 1.10%, -4.80%, -2.70%, -2.90%, -1.40%, and -1.30% respectively as compared to
the slope value of coefficient 1.00%, -3.46%, -1.23%, -5.08%, -1.60%, -2.38%, -1.83%, -1.08%, -
1.23%, -5.06%, -2.15%, -9.55%, -3.65%, -1.59%, -2.18%, -1.99%, -1.05%, and -0.40%. Except
Australian market, all markets exhibit large variations from the slope of the constant coefficient model.
All the negative time-varying coefficients are consistent with the earlier findings. The time-varying
coefficient validate the presence of herding. The maximum and minimum value of Singapore market
exhibit negative value of herding coefficient which means that the herding coefficient is present
throughout the sampling period. These findings are in contradiction with the results of Yang & Chen
(2015). They find no evidence of herding in Hong Kong and American markets. Time-varying herding
90
behavior is absent in Japanese and French market over the entire sample period, therefore not included
in the results.
Table 4.6: Descriptive Statistics of herding coefficient determine by Dynamic Model Panel A: Developed Markets
Country Variable Obs Mean Std. Dev Maximum Minimum Skewness Kurtosis
Australia 2,t 6028 0.010 0.063 0.486 -0.144 1.304 7.745
Austria 2,t 6036 -0.054 0.088 0.293 -0.281 0.669 3.751
Belgium 2,t 6452 0.116 0.070 0.320 -0.022 0.813 2.735
Denmark 2,t 6362 -0.006 0.098 0.265 -0.253 0.202 2.539
Germany 2,t 9130 -0.025 0.063 0.419 -0.214 1.555 9.751
Greece 2,t 6087 -0.034 0.029 0.001 -0.114 -1.095 3.470
Hong Kong 2,t 6400 -0.054 0.035 0.053 -0.146 -0.475 3.098
Italy 2,t 4696 -0.012 0.028 0.078 -0.102 0.140 4.484
Netherland 2,t 8608 -0.011 0.061 0.256 -0.192 0.333 4.690
Norway 2,t 8391 -0.033 0.036 0.144 -0.124 1.618 8.217
Portugal 2,t 5999 0.011 0.194 0.698 -0.443 0.558 2.859
Singapore 2,t 4588 -0.048 0.008 -0.031 -0.067 -0.050 2.743
Spain 2,t 7522 -0.027 0.033 0.046 -0.155 -0.638 3.957
Sweden 2,t 7658 -0.029 0.029 0.071 -0.111 0.295 2.979
UK 2,t 7584 -0.014 0.033 0.100 -0.117 -0.074 2.762
US 2,t 9130 -0.013 0.022 0.054 -0.079 -0.310 2.910
Panel B: Emerging and Frontier Markets
Country Variable Obs Mean Std. Dev Maximum Minimum Skewness Kurtosis
China 2,t 4602 -0.025 0.006 -0.013 -0.042 -0.450 3.465
India 2,t 5479 -0.048 0.102 0.035 -0.111 0.894 4.432
Indonesia 2,t 4173 -0.043 0.046 0.099 -0.165 -0.137 3.424
Korea 2,t 5314 -0.059 0.026 0.000 -0.129 -0.405 2.460
Malaysia 2,t 5480 -0.091 0.059 0.043 -0.263 -0.523 2.635
91
Pakistan 2,t 5479 -0.052 0.048 0.059 -0.182 -0.392 2.656
Philippines 2,t 5565 -0.058 0.030 -0.010 -0.136 -0.826 3.299
Sri Lanka 2,t 5455 -0.091 0.117 0.302 -0.424 0.528 3.898
Taiwan 2,t 5476 -0.036 0.015 -0.011 -0.067 -0.062 2.135
Thailand 2,t 5469 -0.037 0.021 0.026 -0.085 0.512 3.137
Turkey 2,t 5974 -0.003 0.017 0.024 -0.062 -1.440 5.940
Properties of the estimates calculated through state space model applied on ttmtmt rrCSAD 2
,2,10
Panel B of table 4.6 presents the results for the emerging and frontier markets. It is observed
that the mean values of CA, IN, ID, KO, MY, PK, PH, CY, TA, TH, and TK markets are -2.50%, -
4.30%, -4.30%, -5.90%, -9.10%, -5.20%, -5.80%, -9.10%, -3.60%, -3.70%, and -0.30% respectively
as compare to the slope value of coefficient -2.85%, -1.06%, -5.43%, 1.73%, -1.73%, -1.78%, -2.82%,
-5.67%, -4.83%, -1.94%, and -1.55%. All the mean value of the time-varying models are different
from the slope value of the constant coefficient model. Most of the coefficient in the time-varying
models are negatively skewed which means that during the sample period emerging markets show
persistent herding behavior. These findings are consistent with the Yang & Chan (2015), they find
significant time-varying herding in Taiwan and Chinese stock market. These findings are also
consistent with Chiang et al. (2013) as they observe significant time-varying herding behavior in a set
of emerging markets including China, Indonesia, Malaysia, Korea, Thailand and Taiwan. All the
markets show negative and significant mean value of herding coefficient in both studies. The
minimum-maximum range and the value of st. deviation in herding series indicate that among
emerging markets CY, MY, IN, ID, PK, and PH are higher and consistent than all other markets. The
correlation of herding measure is given in appendix 4.
92
The Time Series of Herding Coefficient
4.3.2.1 Developed Markets
Figure 4.1 and 4.2 present the plot of time series estimates of herding coefficient 3,t calculated by
state space model using Kalman filter estimation along with the confidence interval of 95%. Figure
4.1 provides the results from the herding series estimates of 20 developed markets over time. The
sampling period for all series is different depending upon the data limitations. It is observed that
herding series in some developed markets do not display time variation. Belgium and Australian
markets show positive values over the entire period with the exception of few negative shocks in the
Australian market. These results are consistent with the constant coefficient model as the stated model
predict no herding in these two markets. These findings contradict to Chiang et al. (2013) as they
observe significant time-varying herding behavior in Australian market from the period of 1997 to
2008. Similarly, French market also provides evidence against herding behavior. These findings are in
line with a constant coefficient model where we observe no evidence of herding. Therefore it can be
concluded that French market exhibit no herding on average and over the time. The results of US
market also contradict with Chiang et al. (2013) as they find no evidence of herding in US market.
This study observe time-varying herding even the presence of mean value of -1.3% and negative
skewness also support the existence of herd behavior in US market.
In most of the time period Austrian market exhibits negative herding behavior and time-varying
value mostly prevailing in the negative region of the graph. The mean value of -5.4% also supports the
evidence. Denmark market exhibits mixed evidence. There are certain periods in which herding
coefficient lies in the negative regions and several positive coefficient periods are also observed. On
average this study can conclude that in general herding prevail in DK market.
German market shows significant herding behavior as most of the times herding coefficient
remains negative with a mean value of -2.5%. German market exhibits similar results with a constant
93
coefficient model of Chang et al. (2000). In Greek market, strong evidence of herding behavior is
observed. The time series of herding coefficient remains in the negative region throughout the sample
period. The herding series exhibit negative mean value of -3.4% along with negative skewness and
max-min range of -11.4%. Therefore, GK market exhibits strong herding behavior as similar results is
obtained through the constant coefficient model.
Similar findings are observed in Hong Kong market. All of the coefficients of time series
observed to remain in the negative region of the graph with a mean value of -5.4% and negative
skewness. These findings are supported by the constant coefficient model. These findings are in line
with the Chiang et al. (2013) model but are in contradiction with Yang & Chen (2015) findings as they
observe the absence of time-varying herding behavior in HK market.
Italian market exhibits mixed time periods of increased and decreased dispersion. In several
time periods time-varying herding behavior is observed. This is confirmed by the presence of negative
mean value of -1.2% but evidence of intense herding is weakened by the presence of positive skewness.
Japanese market provides evidence against time-varying herding behavior. The constant coefficient
model reports strong evidence of herding, therefore, it can be said that on average the investors of
Japanese market exhibit herding behavior but it is non time varying in nature.
Netherland and Norwegian markets exhibit mixed evidence of time-varying herding behavior.
Several episodes of negative coefficient time periods with few periods of positive trends are witnessed.
However, New Zealand market provides results similar to Japan. In this market constant coefficient
model predict herding behavior but no evidence of this behavior is observed over time.
Singapore market exhibits strong evidence of herding behavior. Over the entire sample, period
herding behavior is consistent and negative. These results are supported by the findings of Chiang et
al. (2013). They also report strong evidence of time-varying herding behavior in Singapore. It is the
only market in the sample of the developed market where max-min range of -0.067 to -0.031 lies in
94
the negative region of the market with a mean value of -4.8% and negative skewness. Thus SG market
displays strong evidence of herding and time-varying herding among all developed markets.
Spanish, Swedish and UK markets exhibit evidence like most of the markets. Mixed evidence
of time-varying herding behavior is observed. Several episodes of negative coefficient time periods
with few periods of positive trends. In several periods time-varying herding behavior is observed. This
is confirmed by the presence of negative mean value of -2.7%, -2.9% and -1.4% but evidence of intense
herding is weakened by the presence of positive skewness in Swedish market. However, Spanish and
UK market exhibit evidence stronger than Sweden due to the presence of negative skewness in both
markets. The presence of herding behavior in all three markets is also confirmed by the presence of
negative coefficient in the constant coefficient model of herding behavior.
The Swiss market exhibits surprising results like many other markets with the absence of time-
varying herding behavior. Like Japan and New Zealand time-varying nature of herding behavior is not
present in the Swiss market but the presence of constant negative coefficient in Chang et al. (2000)
model supports the presence of herding behavior in this particular market.
Therefore it can be concluded that a market exhibiting herding as a constant parameter does not display
same behavior over time. Chiang et al. (2013) argued that if a certain shock hits the economy during a
specific period it can be explained through linkages other than trade or fundamental.
This study observes time variation of herding behavior in all emerging markets. Few developed
markets like Belgium, France, New Zealand, and Switzerland exhibit time-invariant herding behavior.
Therefore, ample evidence of time variation of herding behavior in developed and developing markets
provide support to the hypothesis that herding behavior is a time-variant phenomenon, not a short-term
disequilibrium.
95
-.4
-.2
.0
.2
.4
.6
199
21
99
3
199
4
199
5
199
6
199
7
199
8
199
9
200
0
200
1
200
2
200
3
200
4
200
5
200
6
200
7
200
8
200
9
201
0
201
1
201
2
201
3
201
4
201
5
Herd Coefficient ± 2 RMSE
Australia
-.6
-.4
-.2
.0
.2
.4
.6
199
21
99
3
199
4
199
5
199
6
199
7
199
8
199
9
200
0
200
1
200
2
200
3
200
4
200
5
200
6
200
7
200
8
200
9
201
0
201
1
201
2
201
3
201
4
201
5
Herd Coefficinet ± 2 RMSE
Austria
-.1
.0
.1
.2
.3
.4
199
1
199
2
199
3
199
4
199
5
199
6
199
7
199
8
199
9
200
0
200
1
200
2
200
3
200
4
200
5
200
6
200
7
200
8
200
9
201
0
201
1
201
2
201
3
201
4
201
5
Herd coefficient ± 2 RMSE
Belgium
-.4
-.3
-.2
-.1
.0
.1
.2
.3
.4
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Herd Coefficient ± 2 RMSE
Denmark
-1E+119
0E+00
1E+119
2E+119
3E+119
4E+119
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Herd Coefficient ± 2 RMSE
France
-.4
-.2
.0
.2
.4
.6
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Herd Coefficient ± 2 RMSE
Germany
-.20
-.16
-.12
-.08
-.04
.00
.04
.08
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Herd Coefficient ± 2 RMSE
Greece
-.3
-.2
-.1
.0
.1
.2
199
1
199
2
199
3
199
4
199
5
199
6
199
7
199
8
199
9
200
0
200
1
200
2
200
3
200
4
200
5
200
6
200
7
200
8
200
9
201
0
201
1
201
2
201
3
201
4
201
5
Herd Coefficient ± 2 RMSE
Hong Kong
-.20
-.15
-.10
-.05
.00
.05
.10
.15
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Herd coefficient ± 2 RMSE
Italy
-.3
-.2
-.1
.0
.1
.2
.3
198
11
98
21
98
31
98
41
98
51
98
61
98
71
98
81
98
91
99
01
99
11
99
21
99
31
99
41
99
51
99
61
99
71
99
81
99
92
00
02
00
12
00
22
00
32
00
42
00
52
00
62
00
72
00
82
00
92
01
02
01
12
01
22
01
32
01
42
01
5
Herd Coefficient ± 2 RMSE
Japan
Figure 4-1: Time series of 2,t in developed markets(continued)
96
-.4
-.3
-.2
-.1
.0
.1
.2
.3
.4
.5
198
31
98
41
98
51
98
61
98
71
98
81
98
91
99
01
99
11
99
21
99
31
99
41
99
51
99
61
99
71
99
81
99
92
00
02
00
12
00
22
00
32
00
42
00
52
00
62
00
72
00
82
00
92
01
02
01
12
01
22
01
32
01
42
01
5Herd Coefficient ± 2 RMSE
Netherland
-.016
-.012
-.008
-.004
.000
.004
.008
.012
.016
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Herd Coefficient ± 2 RMSE
Newzealand
-.3
-.2
-.1
.0
.1
.2
.3
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Herd Coefficient ± 2 RMSE
Norway
-1.2
-0.8
-0.4
0.0
0.4
0.8
1.2
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Herd Coefficient ± 2 RMSE
Portugal
-.10
-.08
-.06
-.04
-.02
.00
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Herd Coefficient ± 2 RMSE
Singapore
-.3
-.2
-.1
.0
.1
.2
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
199
7
199
8
199
9
200
0
200
1
200
2
200
3
200
4
200
5
200
6
200
7
200
8
200
9
201
0
201
1
201
2
201
3
201
4
201
5
Herd coefficient ± 2 RMSE
Spain
-.20
-.16
-.12
-.08
-.04
.00
.04
.08
.12
.16
198
61
98
7
198
8
198
9
199
0
199
1
199
2
199
3
199
4
199
5
199
6
199
7
199
8
199
9
200
0
200
1
200
2
200
3
200
4
200
5
200
6
200
7
200
8
200
9
201
0
201
1
201
2
201
3
201
4
201
5
Herd Coefficient ± 2 RMSE
Sweden
-.00006
-.00004
-.00002
.00000
.00002
.00004
.00006
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Herd Coefficient ± 2 RMSE
Switzerland
-.3
-.2
-.1
.0
.1
.2
198
61
98
7
198
8
198
9
199
0
199
1
199
2
199
3
199
4
199
5
199
6
199
7
199
8
199
9
200
0
200
1
200
2
200
3
200
4
200
5
200
6
200
7
200
8
200
9
201
0
201
1
201
2
201
3
201
4
201
5
Herd Coefficient ± 2 RMSE
UK
-.15
-.10
-.05
.00
.05
.10
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Herd Coefficient ± 2 RMSE
US
Figure 4.1: Time series of 2,t in Developed market
97
-.06
-.05
-.04
-.03
-.02
-.01
.00
.01
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Herd Coefficient ± 2 RMSE
China
-.3
-.2
-.1
.0
.1
.2
199
5
199
6
199
7
199
8
199
9
200
0
200
1
200
2
200
3
200
4
200
5
200
6
200
7
200
8
200
9
201
0
201
1
201
2
201
3
201
4
201
5
Herd Coefficient ± 2 RMSE
India
-.3
-.2
-.1
.0
.1
.2
.3
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Herd Coefficient ± 2 RMSE
Indonesia
-.25
-.20
-.15
-.10
-.05
.00
.05
199
5
199
6
199
7
199
8
199
9
200
0
200
1
200
2
200
3
200
4
200
5
200
6
200
7
200
8
200
9
201
0
201
1
201
2
201
3
201
4
201
5
Herd coefficient ± 2 RMSE
Korea
-.5
-.4
-.3
-.2
-.1
.0
.1
.2
.3
199
5
199
6
199
7
199
8
199
9
200
0
200
1
200
2
200
3
200
4
200
5
200
6
200
7
200
8
200
9
201
0
201
1
201
2
201
3
201
4
201
5
Herd Coefficient ± 2 RMSE
Malaysia
-.3
-.2
-.1
.0
.1
.2
199
5
199
6
199
7
199
8
199
9
200
0
200
1
200
2
200
3
200
4
200
5
200
6
200
7
200
8
200
9
201
0
201
1
201
2
201
3
201
4
201
5
Herd Coefficient ± 2 RMSE
Pakistan
-.20
-.15
-.10
-.05
.00
.05
199
4
199
5
199
6
199
7
199
8
199
9
200
0
200
1
200
2
200
3
200
4
200
5
200
6
200
7
200
8
200
9
201
0
201
1
201
2
201
3
201
4
201
5
Herd Coefficient ± 2 RMSE
Philippines
-.8
-.6
-.4
-.2
.0
.2
.4
.6
199
5
199
6
199
7
199
8
199
9
200
0
200
1
200
2
200
3
200
4
200
5
200
6
200
7
200
8
200
9
201
0
201
1
201
2
201
3
201
4
201
5
Herd Coefficient ± 2 RMSE
Sri Lanka
-.10
-.08
-.06
-.04
-.02
.00
.02
.04
199
5
199
6
199
7
199
8
199
9
200
0
200
1
200
2
200
3
200
4
200
5
200
6
200
7
200
8
200
9
201
0
201
1
201
2
201
3
201
4
201
5
Herd Coefficient ± 2 RMSE
Taiwan
-.16
-.12
-.08
-.04
.00
.04
.08
199
5
199
6
199
7
199
8
199
9
200
0
200
1
200
2
200
3
200
4
200
5
200
6
200
7
200
8
200
9
201
0
201
1
201
2
201
3
201
4
201
5
Herd Coefficient ± 2 RMSE
Thailand
-.100
-.075
-.050
-.025
.000
.025
.050
.075
.100
199
3
199
4
199
5
199
6
199
7
199
8
199
9
200
0
200
1
200
2
200
3
200
4
200
5
200
6
200
7
200
8
200
9
201
0
201
1
201
2
201
3
201
4
201
5
Herd Coefficient ± 2 RMSE
Turkey
Figure 4-2: Time series of 2,t in emerging and frontier market
98
4.3.2.2 Emerging and Frontier Markets
Figure 4.2 reports the result of Time-varying herding estimates of emerging and Frontier
markets. This study observes that the evidence of time-varying herding behavior is stronger in
developing markets than developed markets. It is observed that Chinese market exhibit strong
evidence of herding behavior. Over the entire sample, herding behavior is consistent and negative.
These results are supported by the findings of Chiang et al. (2013) and Yang & Chen (2015). They
also observe strong evidence of time-varying herding behavior in the Chinese market. It can be
confirmed by the presence of max-min range -0.042 to -0.013 in the negative region of the figure
with a mean value of -2.5% and negative skewness. Similar outcomes are observed in case of
Korean, Philippines, and Taiwanese markets. All the mean values are negative -5.9%, -5.8%, and
-3.6% respectively with all series exhibiting negative skewness. However, in all the three markets,
the extreme value remained in the negative region of the graph.
All other markets except Sri Lanka and Thailand exhibit strong evidence of herding. In both
these markets, several episodes of increased and decreased dispersion are observed. On average
market exhibit herding behavior during the entire period as the negative mean value of coefficient
predict (-9.1% and -3.7%) but the value of Skewness is positive in both markets. However, Indian,
Indonesian, Malaysian and Pakistani markets exhibit strong evidence of herding with a very few
episode of increase dispersion in these markets. All four markets exhibit a negative mean value of
-4.8%, -4.3%, -9.1% and -5.2% and for Pakistan with negative skewness. All these markets support
the findings of the constant coefficient model where there exists strong support of herding
behavior. Turkish market exhibit time-varying herding behavior but the evidence is weak as the
mean value of the coefficient is 0.3% and max-min range of 0.024 to -0.062. The skewness is
although negative but the value nearer to zero is observed over the entire distribution.
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Therefore, it can be concluded that the time-varying herding behavior is more pronounced in
emerging markets than in developed markets. Voronkova & Bohl, (2005) argue that capital market
and especially the emerging markets exhibit more herding tendency as compared to the well-
developed markets (Wermer, 1999). The investor in these markets is normally irrational due to the
absence of information set needed to make reliable investments. Markets are fragile, the presence
of weak regulatory systems, the concentration of wealth in few hands and the strong influence of
speculators make the markets more volatile. Therefore, in order to make investment decisions
either they rely on the information set available in the market or follow the footsteps of large
institutional investors.
Determinants of Herding Behavior
In table 4.7 this study reports the effect of two domestic and one cross country factor of
herding behavior. Market returns as a proxy for market performance and volatility of domestic
market calculated through the asymmetric GARCH model are used as domestic factors. Whereas,
global market volatility is measured through CBOE implied volatility index VIX.
Panel A of the table 4.7 presents the results for the developed markets. It is observed that
stock market performance affect the herding behavior in Denmark and Portugal markets only and
this effect is positive. These findings are in contradiction with Chiang et al. (2013) as they observe
the negative significant effect of stock returns on herding behavior. An increase in stock returns
will increase herding behavior, these findings are similar to the findings of Tan et al. (2008) and
Mobarek et al. (2014). When there are positive returns in the stock market, investors tend to follow
the market consensus by discarding their own set of information and affirms that herding is more
prevalent in turbulent periods, when the market is facing extreme returns (Christie & Huang,
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1995). These findings are consistent with Duffee (2001), they find a higher aggregation of trading
volumes in days when the stock market is rising.
Except for Norway, all developed markets exhibit a significant relationship with increased
volatility at 1% significance level. Herding is positively related to the stock market volatility. The
herding coefficient has a negative sign, the negative sign of volatility in results can be interpreted
into increased dispersion. It means that stock return volatility has a significant effect on the herding
behavior of an investor. In more volatile periods investors set aside the information set available
with them and follow the market consensus. In few markets, effect is negative whereas for others
it is positive.
Table 4.7: Domestic and Cross-Market Determinants of Herding Behavior Panel A: Developed Markets
Country ∅0 ∅1 ∅2 ∅3 R2
Australia -0.1894*** 0.1434 -0.0190*** 0.0004 0.024
Austria -0.2991*** -0.1052 -0.0249*** 0.0003 0.034
Denmark -0.4303*** 0.2079** -0.0450*** -0.0003 0.216
Germany -0.1083*** 0.0050 -0.0129*** 0.0010*** 0.010
Greece 0.0070 -0.0214 0.0063*** 0.0006*** 0.083
Hong Kong 0.0977*** -0.0001 0.0173*** 0.0001 0.116
Italy 0.0897*** 0.0006 0.0096*** -0.0007*** 0.029
Netherland -0.3124*** -0.0639 -0.0280*** 0.0020*** 0.066
Norway -0.0456** 0.0087 -0.0021 -0.0004** 0.011
Portugal 0.4997*** 0.4594** 0.0402*** -0.0053*** 0.043
Singapore -0.0178** 0.0007 0.0027*** -0.0003*** 0.040
Spain -0.0023 0.0164 0.0027*** 0.0000 0.002
Sweden -0.1167*** -0.0094 -0.0069*** 0.0013*** 0.059
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UK 0.0884*** 0.0189 0.0100*** -0.0003* 0.031
US -0.0053* -0.0043 0.0072*** -0.0002* 0.031
Panel B: Emerging and Frontier Markets
Country ∅0 ∅1 ∅2 ∅3 R2
China -0.0307*** 0.0004 -0.0010*** -0.0001*** 0.077
India -0.0226* 0.0047 0.0040* -0.0004** 0.009
Indonesia -0.1426*** 0.0302 -0.0120*** -0.0005** 0.048
Korea 0.0320*** 0.0091 0.0130*** 0.0010*** 0.484
Malaysia 0.1256*** -0.0826 0.0209*** -0.0005** 0.101
Pakistan -0.0419*** 0.0946** 0.0022** 0.0005*** 0.008
Philippines -0.0698*** -0.0726** -0.0013 0.0000 0.001
Sri Lanka -0.1225*** 0.0105*** -0.0001 0.0013*** 0.014
Taiwan -0.0792*** -0.0131 -0.0058*** -0.0005*** 0.181
Thailand -0.0230** 0.0125 0.0037*** 0.0009*** 0.159
Turkey -0.0995*** -0.02 -0.0106*** 0.0005*** 0.305
Note: the results are obtained through OLS estimated of equation 𝐻𝑒𝑟𝑑𝑡 = ∅0 + ∅1𝑅𝑚,𝑡 + ∅2�̂�𝑡2 + ∅3𝑉𝐼𝑋𝑡−1 + 𝜀𝑡
***indicate statistical significance at 1%. **Indicate statistical significance at 5%. *Indicate statistical significance at 10%.
The episodes of the recent financial crisis validate the effect interdependence of stock
market and transmission of volatility from one market to another (Diebold & Yilmaz, 2009).
Analysis into the causes of the recent crisis in financial markets indicate whenever the negative
news is generated in one market it is quickly absorbed by the participants of other markets,
irrespective of the channel through which this news is transmitted (Pericoli & Sbracia, 2003).
Most of the markets in the sample exhibit a significant effect on dynamic herding behavior
in developed markets. Ng (2000) observes a significant effect of volatility spillover from the US
towards different Pacific Basin markets. The global volatility index VIX is used as a proxy of
global volatility. Whaley (2009) argues that during market turmoil period VIX increases and the
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stock price will fall as a reaction to increased volatility, as a result, investors risk premium
increases. Hakkio & Keeton (2009) states that VIX capture both types of uncertainty arising from
fundamental channels or the investor sentiment effects. It is observed that except Australia,
Austria, Denmark, Hong Kong and Spain, all other market report a significant effect of global
volatility on herding behavior of local investor. The lagged value exhibit similar results as the local
volatility have on herding behavior. These findings are supported by the findings of Chang &
Zheng (2010) as they find a strong correlation between international stock markets.
Panel B of table 4.7 present results of the emerging and frontier markets, similar to
developed markets very few markets exhibit significant relationship of herding behavior to the
stock market performance. Only Pakistan, Philippine, and Sri Lankan markets exhibit the
significant effect of stock returns on herding behavior. However, except Philippine, all other
markets exhibit a significant relationship with Domestic and cross-market volatility on herding
behavior. These findings are supported by the findings of Tan et al. (2008), they state that herding
is more observable during period of increased volatility and trading volumes, similarly Gleason et
al. (2004) argue that generally, investors find comfort in mimicking the actions of others when
information flow in market is abnormal and volatility is high.
The role of stock market performance, domestic and global volatility cannot be neglected
in the determination of time variation of herding behavior and fulfill the purpose of the study.
Herding behavior is more pronounced in periods of high volatility and lagged value of global
volatility provide similar results as domestic volatility. These findings support the hypothesis that
herding behavior is affected by market performance, domestic and global volatility specifically
when market undergoes stress and are inefficient. These findings provide a support to ABC theory
that in the presence of social influence the emotions of the investors are affected by the market
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dynamics and they make cognitive errors in their decision-making process that leads to irrational
behavior of investors in the market.
Culture and Herding Behavior
Table 4.8 reports the results of the impact of culture on herding behavior. The results provide that
culture has a major contribution in investor’s decision-making process, specifically on herding
behavior. It is observed that except uncertainty avoidance and power distance all variables
including individualism, long-term orientation, masculinity, and indulgence have a significant
impact.
Power distance and Herding Behavior
This study shows that power distance index has no effect on herding behavior. According
to literature, there are no clear findings on the effect of power distance and herding behavior. This
study also concludes that there is no relationship in herding behavior and power distance, these
findings support the findings of Chang & Lin (2015) as they also do not find any support of the
relationship between high or low power distance and Herding behavior. These findings are
supportive of the fact given by Sinke (2012), they argue that in high power distance countries
institutional structure provides more investor protection and welfare. Thus these markets have
institutions with better quality and information flow is high (Chui et al., 2010). This argument
negates the presence of herding in countries with strong institutional setups.
Individualism and Herding Behavior
The dimension of individualism provides significant result when added as a moderating
variable in herding regression. The value is negative and significant at 10% level. It means that the
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dispersion in cross-sectional standard dispersion decrease by -3.630 percent if the society is high
in individualism. These findings are supported theoretically and empirically as the study
hypothesizes that individualism has negative significant effect on herding behavior. Increase in
individualism reduces herding behavior in a society therefore, reject the hypothesis of no effect on
10 percent significance level. These findings are supportive of the fact that collectivist societies
exhibit more herding behavior whereas individualist societies exhibit less herding behavior. These
results are consistent with Chang & Lin (2015) and Blsaco et al. (2017). In individualist societies,
people rely more on their own decision and take less effect from others and they are able to succeed
with their own capabilities. Thus they accept risks and are usually overconfident, act individually
and take less effect from the group. Schmeling (2009) studies the effect of individualism along
with uncertainty avoidance as a measure of overreaction and herding behavior and conclude that
investor sentiment is greatly affected by these virtues.
Masculinity and Herding Behavior
The results show that the dimension of masculinity has a significant effect when added as
a modulating variable in herding regression. The value is negative and significant at 1% level. It
means that the dispersion in cross-sectional standard dispersion decrease by -4.801 if there is one
unit increase in the dimension of masculinity thus exhibiting more herding. These findings have
theoretical and empirical support as the study hypothesize the negative significant effect of
masculinity on herding behavior. In masculine societies, individuals are ambitious and more self-
confident and the societies are driven by the attributes of competition and victory (Hofstede 1991).
These potentials make investor care for material things more, in order to overcome competition
they follow the strategies of others to maintain their reputation and engage in momentum profit,
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Table 4.8: Estimation of Individual Cultural Dimensions on Herding Behavior Power Distance Index (PDI)
C Rmi,t |Rmi,t| Rm2i,t PDI*[Rm2
i,t] 𝑅2̅̅̅̅ F-test
CSADi,t
0.003
(12.818)***
0.030
(1.090)
0.531
(7.378)***
-7.671
(-3.865)***
-0.377
(-0.116)
0.26 44.167***
Individualism Index (IDV)
C Rmi,t |Rmi,t| Rm2i,t IDV*[Rm2
i,t] 𝑅2̅̅̅̅ F-test
CSADi,t
0.003
(13.115)***
0.034
(1.264)
0.533
(7.741)***
-6.445
(-3.117)***
-3.630
(-1.677)*
0.29 46.199***
Masculinity index(MAS)
C Rmi,t |Rmi,t| Rm2i,t MAS*[Rm2
i,t] 𝑅2̅̅̅̅ F-test
CSADi,t
0.003
(13.206)***
0.027
(1.023)
0.521
(7.628)***
-3.879
(-1.667)*
-4.801
(-2.799)***
0.27 47.166***
Uncertainty avoidance Index(UAI)
C Rmi,t |Rmi,t| Rm2i,t UAI*[Rm2
i,t] 𝑅2̅̅̅̅ F-test
CSADi,t
0.003
(12.951)***
0.037
(1.452)
0.537
(7.687)***
-9.523
(-3.450)***
2.809
(1.476)
0.26 45.373***
Long term orientation Index(LTO)
C Rmi,t |Rmi,t| Rm2i,t LTO*[Rm2
i,t] 𝑅2̅̅̅̅ F-test
CSADi,t
0.004
(13.229)***
0.001
(0.041)
0.508
(7.037)***
1.821
(0.339)
-10.229
(-2.235)**
0.29 52.090***
Indulgence Index(IVR)
C Rmi,t |Rmi,t| Rm2i,t IVR*[Rm2
i,t] 𝑅2̅̅̅̅ F-test
CSADi,t
0.003
(13.159)***
0.015
(0.528)
0.515
(7.520)***
-7.928
(-3.852)***
4.910
(1.691)*
0.27 45.972***
Note: Estimation results of pool regression estimated through model tti
s
tititititi RmDCRmRmRmCSAD 2
,,4
2
,3,2,1, ][][
***indicate statistical significance at 1%. **Indicate statistical significance at 5%. *Indicate statistical significance at 10%.
106
thus exhibiting more herding behavior. These findings are in line with Chiang and Lin (2015) &
Blasco et al. (2017). These findings are supported in a way that in masculine societies institutions
are controlled by the strong individuals of the society and rest of the individuals follows their traits
in order to increase their profit and avoid risk.
Uncertainty Avoidance and Herding Behavior
There is the insignificant effect of uncertainty avoidance on investor herding behavior,
witnessed by the presence of positive insignificant coefficient in table 4.8. In high uncertainty
avoidance societies, the investor usually feels comfort in following the consensus opinion.
Similarly, Sinke (2012) concludes that people with high uncertainty avoidance exhibit more
herding behavior. These results are in contradiction with Blasco et al. (2017) they found a decrease
in herding in high uncertainty avoidance societies. These findings are in line with Chang & Lin
(2015) as they found the insignificant effect of uncertainty avoidance on herding behavior. These
findings are in line with the theory where investors, in order to avoid risk, rely more on information
generate fewer returns on average and exhibit less herding behavior but against the hypothesis that
low-risk aversion leads to higher level of herding. This cultural dimension does not meet to fulfill
the objective as it has no modulating effect on the herding behavior of investors in the economies
under consideration.
Long-Term Orientation and Herding Behavior
The results in the presence of long-term orientation (LTO) exhibit a significant negative
relationship with cross-sectional absolute dispersion of returns. In the presence of significant LTO
dimension, the squared value of market return turned out to be negative. It means that when the
societies are LTO the herding behavior is absent. The markets in these societies are more
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regularized and individuals in these markets prefer safe investments over risky investments.
Investor is usually adaptive to the rules and regulations and lets the complexities of life settle at
their own pace. The decrease in dispersion due to interactive term can be explained through the
traits of long-term orientation. These societies follow the virtues driven by persistence and
personal adaptability, focused on relationship and market positions and prefer safe investments
over risky ones. Therefore, they follow the market directions. Virtues of long-term oriented itself
can be a possible reason for decrease in dispersion or market inefficiency. The value of moderating
term shows 10.229 Percent decrease with one unit increase in the level of long-term orientation.
These findings are in contradictions with Chang & Lin (2015) because they find insignificant effect
of long-term orientation on herding behavior.
These results can provide an explanation in favor of the asset pricing theory, due to this
strong orientation towards fundamentals, it is usually perceived that preferred investment strategy is
usually drawn from theory. Inefficient markets, large investors usually rely on a long-term oriented
strategies, especially when investment horizon is long-term and usually avoid short-sighted trends
based on sentiments that can result in frequent portfolio shifts (Beckmann, et al., 2008). The results
of the study support the hypothesis that long-term orientated societies are usually risk-aversed and
exhibit less herding behavior as they refrain from taking short-term sentiment based decisions.
Indulgence and Herding Behavior
The dimension of indulgence report a positive significant relationship with herding behavior of an
invetsor. The presence of indulgence decreases the dispersion of cross-sectional absolute returns,
it means that indulgence weakens the relationship and in its presence herding behavior decreases.
This effect can be seen by the increase in dispersion by 4.910 percent by one unit increase in
indulgence. It means that highly indulged societies exhibit less herding behavior. These
108
observations are against the hypothesis that in the presence of more indulgence herding behavior
increases.
It is observed that power distance and uncertainty avoidance has no impact on herding behavior of
investor, however individualism, masculinity, and indulgence influence the relationship between
nonlinear returns and market dispersion. In the presence of long-term orientation, herding
coefficient becomes insignificant, slope dummy of interacting term decreases the dispersion of
returns.
Estimation of State Space Model:
Properties of Cross-Sectional Standard Deviation of betas
The first step is the estimation of betas through rational asset pricing model. They are
required to calculate the cross-sectional deviation of betas in order to identify dispersion in the
model. This study uses daily data to estimate betas and follows the approach of Hwang & Salmon
(2004). They used one month window to calculate OLS estimates of beta in order to avoid
overlapping intervals and statistical difficulties. These estimated betas are further used to calculate
the cross-sectional standard deviation of betas.
Table 4.9 provides some statistical properties of betas on the market portfolio. The results
of developed markets are reported in Panel A of Table 4.9. This study consists of 192 monthly
observations selected on the basis of active market participants from a period of 2000- 2015. Mean
value of all series are different from zero and positively skewed like all other volatility series. For
developed markets, the cross-sectional standard deviation of beta varies between a range from a
0.202 for Japan to 0.446 for Norway and the average mean value is 0.337. Similarly, the standard
deviation of the series varies from 0.027 for Japan to 0.188 for Denmark, with a mean value of to
109
0.136. The Denmark market exhibit maximum value of 1.316, while Greek market exhibit lowest
value of 0.008 for cross-sectional standard deviation of beta. According to the Jarque Bera
statistics, all of the series for developed markets are not normal which can be justified by
significant positive skewness and excess kurtosis in all sample countries. The resultant Jarque Bera
statistics due to the deviation of skewness and Kurtosis from zero ranges from 541.040 for Italy to
27.546 for Sweden. These findings are similar to Hwang & Salmon (2004).
Table 4.9: Descriptive statistics of Cross-Sectional Standard Deviation of betas Panel A: Developed Markets
Country Obs Mean Std. Dev. Maxi Min Skewness Kurtosis JB
Australia 192 0.338 0.122 0.781 0.100 0.891 3.625 28.527***
Austria 192 0.252 0.145 0.916 0.053 1.797 7.222 245.984***
Belgium 192 0.400 0.095 0.806 0.163 0.774 4.582 39.185***
Denmark 192 0.531 0.188 1.316 0.181 1.384 5.783 123.220***
France 192 0.276 0.147 0.954 0.076 1.801 6.648 210.267***
Germany 192 0.282 0.120 0.680 0.124 1.327 4.303 69.900***
Greece 192 0.273 0.151 1.094 0.008 1.892 8.851 388.479***
Hong Kong 192 0.394 0.143 1.114 0.148 0.991 5.421 78.325***
Italy 192 0.313 0.107 0.880 0.154 2.066 10.111 541.040***
Japan 192 0.202 0.027 0.313 0.158 1.372 5.513 110.761***
Netherland 192 0.253 0.145 0.892 0.067 1.528 5.738 134.708***
New Zealand 192 0.381 0.175 1.082 0.098 1.560 6.168 158.227***
Norway 192 0.446 0.149 0.994 0.215 1.367 5.109 95.395***
Portugal 192 0.374 0.163 0.997 0.081 0.904 4.002 34.209***
Singapore 192 0.283 0.131 0.836 0.063 1.275 4.645 73.651***
Spain 192 0.327 0.130 0.965 0.145 1.962 8.419 358.106***
Sweden 192 0.368 0.176 0.947 0.109 0.918 3.272 27.546***
Switzerland 192 0.377 0.109 1.017 0.154 1.713 9.117 393.295***
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UK 192 0.388 0.170 0.984 0.081 1.215 4.515 65.563***
US 192 0.291 0.134 0.911 0.090 1.583 6.175 160.808***
Panel B: Emerging and Frontier Markets
Country Obs Mean Std. Dev. Max Min Skewness Kurtosis Jarque-Bera
China 192 0.239 0.097 0.589 0.072 0.909 4.097 36.082***
India 192 0.348 0.138 1.027 0.114 1.738 7.744 276.625***
Indonesia 192 0.377 0.261 1.715 0.089 2.245 8.891 438.965***
Korea 192 0.398 0.130 0.964 0.159 1.566 6.695 187.719***
Malaysia 192 0.495 0.161 1.133 0.184 1.449 5.484 116.600***
Pakistan 192 0.430 0.128 0.826 0.172 0.528 3.404 10.233***
Philippines 192 0.259 0.151 0.985 0.056 2.003 8.310 353.949***
Sri Lanka 192 0.370 0.192 0.982 0.056 0.911 3.575 29.191***
Taiwan 192 0.368 0.084 0.615 0.139 -0.010 2.742 0.537
Thailand 192 0.407 0.123 0.888 0.180 0.970 4.556 49.479***
Turkey 192 0.387 0.068 0.627 0.207 0.279 3.056 2.525
Note: Betas are calculates using OLS estimates on capital asset pricing Model. Daily data is utilized to calculate
monthly betas on the factor. These betas are further utilized to calculate Cross-sectional dispersion of
betas 𝑆𝑡𝑑𝑐(𝛽𝑖𝑚𝑡𝑏 ).
Panel B reports summary statistics of emerging and Frontier markets, this study observes that
Malaysian markets exhibit a highest mean value of dispersion at 0.495 whereas Chinese markets
have lowest mean value among all, with a mean value of 0.371. The standard deviation of cross-
sectional dispersion of beta varies from 0.068 for turkey to 0.261 for Japan, with a mean value of
0.139. The Indonesian market exhibit maximum value of 1.715, while Philippines market exhibits
the lowest value of 0.056 for cross-sectional standard deviation of beta. Small dispersion from
mean represents a higher degree of herding behavior. According to the Jarque Bera statistics, most
of the series is not normal except Taiwan and Turkey which can be justified by significant positive
111
skewness and excess kurtosis in most of the countries in the sample. The findings are similar to
the findings of Hwang & Salmon (2004).
Properties of Log of Cross-Sectional Standard Deviation
Table 4.10 represents the descriptive statistics of logarithmic cross-sectional dispersion of betas
from the mean value of equally weighted dispersion of betas. Betas are calculated using OLS
estimates on the market model. From a daily data over a period of 2000 to 2015 total 192 monthly
observations of betas are estimated which is further used in the calculation of logarithmic cross-
sectional dispersion of betas. This transformation is required for the application of Kalman filter
estimation.
The logarithmic cross-section deviation statistics reveal the insignificant value of Jarque Bera
statistics. The null hypothesis of normality is not rejected in these cases for most of the countries
of developed, emerging and frontier markets. Therefore, this study can employ Kalman filter for
the estimation of herding behavior in these markets.
Table 4.10: Descriptive statistics of Log of cross-sectional standard deviation of betas Panel A: Developed Markets
Country Obs Mean Std. Dev. Max Min Skewness Kurtosis JB
Australia 192 -0.575 0.181 -0.040 -1.048 0.279 3.114 2.598
Austria 192 -0.498 0.154 -0.107 -0.999 -0.055 2.998 0.099
Belgium 192 -0.657 0.236 0.083 -1.273 0.115 3.492 2.363
Denmark 192 -0.405 0.117 0.131 -0.788 0.683 6.905 136.884**
France 192 -0.299 0.145 0.119 -0.742 0.092 3.668 3.843
Germany 192 -0.607 0.199 -0.021 -1.119 0.497 3.151 8.087*
Greece 192 -0.591 0.168 -0.174 -0.911 0.409 2.748 5.868
Hong Kong 192 -0.625 0.243 0.039 -2.077 -1.045 8.793 303.402
112
Italy 192 -0.432 0.158 0.047 -0.831 -0.167 2.825 1.132
Japan 192 -0.522 0.138 0.135 -0.814 1.009 5.749 93.078**
Netherland 192 -0.698 0.054 -0.505 -0.801 0.940 4.052 37.111**
New Zealand 192 -0.659 0.230 -0.050 -1.177 0.199 2.597 2.563
Norway 192 -0.457 0.193 0.034 -1.011 0.052 3.103 0.171
Portugal 192 -0.365 0.147 0.291 -0.667 0.910 4.720 50.175**
Singapore 192 -0.451 0.232 0.419 -1.093 0.427 4.694 28.782**
Spain 192 -0.591 0.191 -0.078 -1.203 0.049 3.167 0.301
Sweden 192 -0.513 0.148 -0.016 -0.839 0.642 3.607 16.128**
Switzerland 192 -0.483 0.203 -0.024 -0.961 0.095 2.302 4.185
UK 192 -0.440 0.116 0.007 -0.811 0.333 4.123 13.644**
US 192 -0.450 0.183 -0.007 -1.093 -0.041 3.400 1.333
Panel B: Emerging and Frontier Markets
Obs Mean Std. Dev. Max Min Skewness Kurtosis JB
China 192 -0.658 0.180 -0.230 -1.143 -0.296 3.079 2.861
India 192 -0.487 0.155 0.012 -0.943 0.317 3.543 5.567
Indonesia 192 -0.497 0.242 0.234 -1.049 0.568 3.273 10.920**
Korea 192 -0.417 0.137 0.154 -0.798 0.564 4.782 35.579**
Malaysia 192 -0.326 0.129 0.054 -0.734 0.390 3.792 9.876**
Pakistan 192 -0.376 0.157 0.391 -0.765 0.717 6.044 90.578**
Philippines 192 -0.645 0.222 -0.007 -1.252 0.198 3.348 2.221
Sri Lanka 192 -0.480 0.260 0.477 -1.253 0.068 3.640 3.426
Taiwan 192 -0.446 0.106 -0.211 -0.858 -0.694 3.634 18.636**
Thailand 192 -0.409 0.128 -0.052 -0.745 -0.005 3.127 0.131
Turkey 192 -0.420 0.077 -0.203 -0.685 -0.246 3.136 2.078
Note: Betas are calculates using OLS estimates on capital asset pricing Model. Daily data is utilized to calculate
monthly betas on the factor. These betas are further utilized to calculate Cross-sectional dispersion of betas which are
further transformed to their Logarithmic form log(𝑆𝑡𝑑𝑐(𝛽𝑖𝑚𝑡𝑏 ) to test the state space model.
113
Panel A reports summary statistics of developed countries for the log standard dispersion
of betas. Mean value of Log(𝑆𝑡𝑑𝑐(𝛽𝑖𝑚𝑡𝑏 )) varies from -0.698 of Netherlands to -0.299 of France,
with a mean value of -0.516. The standard deviation of the series ranges between 0.243 for Hong
Kong to 0.054 for Netherland, with a mean value of 0.172. The Sri Lankan market exhibits highest
logarithmic standard dispersion, where as Hong Kong market exhibits lowest dispersion of -2.077.
In case of skewness Kurtosis and Jarque Bera statistics, most of the series exhibit state of normality
because of insignificant value of statistics. Therefore, this study can use Kalman filter for testing
herd formation in these markets.
Panel B of the table 4.10 reports summary statistics of the emerging and frontier markets.
Mean value of Log(𝑆𝑡𝑑𝑐(𝛽𝑖𝑚𝑡𝑏 )) varies from -0.658 for China to -0.326 for Malaysia with a mean
value of -0.469. Standard deviation of Log(𝑆𝑡𝑑𝑐(𝛽𝑖𝑚𝑡𝑏 )) ranges from 0.077 for Turkey to 0.260
for Sri Lanka, with a mean value of 0.163. Most of the series show insignificant value of Jarque-
Bera statistics and normality of data. Therefore, Kalman filter can be applied to test state space
model proposed by Hwang & Salmon (2004).
114
Table 4.11: Estimates of Herding Measure and State Space Model Panel A: Developed Countries
Country 𝝁𝒎 𝝈𝒗𝒎 ∅𝒎 𝝈𝒏𝒎 𝝈𝒏𝒎/𝐒𝐃[𝐥𝐨𝐠 [𝑺𝒕𝒅𝒄(𝜷𝒊𝒎𝒕𝒃 )]] Log Likelihood AIC BIC
Australia -0.494(0.042)*** 0.132(0.117)*** 0.974(0.022)*** 0.017(0.864)*** 0.093 105.271 -1.055 -0.987
Austria -0.652(0.076)*** 0.196(0.110)*** 0.977(0.022)*** 0.028(0.779)*** 0.18 28.645 -0.257 -0.189
Belgium -0.406(0.019)*** 0.103(0.107)*** 0.886(0.074)*** 0.025(0.824)*** 0.106 148.013 -1.5 -1.432
Denmark -0.302(0.022)*** 0.116(0.195)*** 0.838(0.104)*** 0.048(0.703)*** 0.266 112.102 -1.126 -1.058
France -0.578(0.130)*** 0.121(0.135)*** 0.983(0.015)*** 0.037(0.450)*** 0.255 104.823 -1.05 -0.982
Germany -0.591(0.050)*** 0.020(1.190)*** 0.896(0.037)*** 0.075(0.175)*** 0.374 214.384 -2.192 -2.124
Greece -0.634(0.058)*** 0.205(0.094)*** 0.936(0.048)*** 0.045(0.838)*** 0.271 15.277 -0.118 -0.05
Hong Kong -0.427(0.072)*** 0.117(0.127)*** 0.981(0.018)*** 0.022(0.658)*** 0.089 122.235 -1.232 -1.164
Italy -0.518(0.027)*** 0.117(0.098)*** 0.901(0.065)*** 0.032(0.786)*** 0.133 120.275 -1.211 -1.143
Japan -0.697(0.010)*** 0.000(2.16E+08) 0.661(0.049)*** 0.040(0.078)*** 0.292 344.516 -3.547 -3.479
Netherland -0.644(0.055)*** 0.181(0.121)*** 0.936(0.042)*** 0.050(0.597)*** 0.389 33.511 -0.307 -0.24
New Zealand -0.461(0.034)*** 0.169(0.099)*** 0.947(0.048)*** 0.023(0.926)*** 0.101 59.396 -0.577 -0.509
Norway -0.328(0.076)*** 0.119(0.070)*** 0.988(0.012)*** 0.015(0.616)*** 0.079 125.07 -1.261 -1.193
Portugal -0.460(0.038)*** 0.217(0.104)*** 0.934(0.084)*** 0.029(1.512)*** 0.131 12.207 -0.085 -0.018
Singapore -0.591(0.028)*** 0.170(0.133)*** 0.892(0.085)*** 0.039(0.921)*** 0.167 53.046 -0.511 -0.443
Spain -0.484(0.067)*** 0.093(0.113)*** 0.973(0.018)*** 0.031(0.445)*** 0.163 153.819 -1.561 -1.493
Sweden -0.514(0.142)*** 0.116(0.128)*** 0.983(0.019)*** 0.037(0.481)*** 0.143 110.692 -1.111 -1.044
Switzerland -0.439(0.027)*** 0.097(0.119)*** 0.940(0.043)*** 0.022(0.762)*** 0.106 158.203 -1.606 -1.538
UK -0.445(0.060)*** 0.120(0.130)*** 0.949(0.030)*** 0.044(0.476)*** 0.564 103.381 -1.035 -0.967
115
US -0.578(0.048)*** 0.108(0.183)*** 0.908(0.045)*** 0.060(0.398)*** 0.33 107.859 -1.082 -1.014
Panel B: Emerging and frontier Markets
Country 𝝁𝒎 𝝈𝒗𝒎 ∅𝒎 𝝈𝒏𝒎 𝝈𝒏𝒎/𝐒𝐃[𝐥𝐨𝐠 [𝑺𝒕𝒅𝒄(𝜷𝒊𝒎𝒕𝒃 )]] Log Likelihood AIC BIC
China -0.657(0.027)*** 0.133(0.155)*** 0.777(0.103)*** 0.076(0.563)*** 0.651 73.537 -0.724 -0.656
India -0.489(0.038)*** 0.104(0.163)*** 0.905(0.050)*** 0.049(0.468)*** 0.311 124.015 -1.25 -1.182
Indonesia -0.503(0.040)*** 0.199(0.140)*** 0.863(0.073)*** 0.069(0.601)*** 0.447 13.276 -0.097 -0.029
Korea -0.413(0.030)*** 0.123(0.093)*** 0.956(0.035)*** 0.017(0.825)*** 0.324 119.388 -1.202 -1.134
Malaysia -0.325(0.016)*** 0.083(0.421)*** 0.574(0.144)*** 0.081(0.534)*** 0.589 133.544 -1.349 -1.282
Pakistan -0.380(0.052)*** 0.171(0.031)*** 0.950(0.052)*** 0.026(0.811)*** 0.178 55.348 -0.535 -0.467
Philippines -0.646(0.048)*** 0.187(0.119)*** 0.936(0.050)*** 0.041(0.770)*** 0.261 33.051 -0.303 -0.235
Sri Lanka -0.480(0.026)*** 0.161(1.142)*** 0.410(0.284) 0.185(0.992)*** 1.246 -6.65 0.111 0.179
Taiwan -0.445(0.021)*** 0.073(0.197)*** 0.822(0.084)*** 0.044(0.405)*** 0.38 183.656 -1.871 -1.804
Thailand -0.409(0.014)*** 0.108(0.242)*** 0.697(0.200)*** 0.049(1.044)*** 0.46 127.425 -1.286 -1.218
Turkey -0.420(0.008)*** 0.069(0.163)*** 0.739(0.232)*** 0.023(1.313)*** 0.178 222.221 -2.273 -2.205
Note: results of the estimates of state space model of Hwang & Salmon (2004) model log [𝑆𝑡𝑑𝑐(𝛽𝑖𝑚𝑡𝑏 )] = 𝜇𝑚 + 𝐻𝑚𝑡 + 𝑣𝑚𝑡, where, 𝐻𝑚𝑡 = ∅𝑚𝐻𝑚𝑡−1 +
𝑚𝑡
***indicate statistical significance at 1%. **Indicate statistical significance at 5%. *Indicate statistical significance at 10%.
116
Estimates of Herding Measure and State Space Model
Table 4.11 represents estimation results for the state space model presented in eq 12 & 13.
The results indicate strong evidence of herding in almost all markets, as the coefficient Hmt is
highly persistent and large and statistically significant value of AR(1) coefficient ∅m with the
exception of Sri Lanka. Furthermore, the value of coefficient ∅m is less than one without
exploding. When there is no herding in the market, the coefficient σnm is equal to zero. The results
indicate that the coefficient is non zero and significant for all countries indicating the presence of
herding behavior.
Panel A represents herding towards a market portfolio in developed markets. It can be
observed that Hmt indicate the highly persistent significant value of coefficient ∅m. The results show
that Norway has the highest coefficient value followed by France, Sweden, Hong Kong, Austria,
Australia, Spain, UK, New Zealand, Switzerland, Greece, Netherland, Portugal, US, Italy,
Germany, Singapore, Belgium, Denmark and Japan at, 0.988, 0.983, 0.983, 0.981, 0.977, 0.974,
0.973, 0.949, 0.947, 0.940, 0.936, 0.936, 0.934, 0.908, 0.901, 0.896, 0.892, 0.886, 0.838, and 0.661
respectively at 1% level of significance. All the values of the coefficient are less than 1 without
exploding which means that all series of herding behavior follows a stationary process. These
findings are consistent with Hwang & Salmon (2004), even the coefficient value for most of the
countries are higher than compared to their findings.
According to equation 13, when there is no herding towards portfolio in the market, the coefficient
σnm is equal to zero, however in all developed markets σnm the error term, is different from zero and
highly significant at 1% level of significance. When looking at the proportion of variability
explained by herding, this study observe highest variability in cross sectional dispersion of returns
117
which can be explained through herding behavior in the market of UK (56.40%), followed
by Netherland (38.90%), Germany (37.40%), US (33.00%), Japan (29.20%), Greece (27.10%),
Denmark (26.60%), France (25.50%), Austria (18.00%), Singapore (16.70%), Spain
(16.30%), Sweden (14.30%), Italy (13.30%), Portugal (13.10%), Belgium (10.60%), Switzerland
(10.60%), New Zealand (10.10%), Australia (9.30%), Hong Kong (8.90%), and lowest in Norway
(7.90%). These findings are similar to the findings of Khan et al. (2011), they observe herding
using HS model in France, UK, Italy and German and find herding in extreme market returns.
Panel B of the table 4.11 reports the results herding towards a market portfolio in emerging
and developed markets. It can be observed that Hmt is highly persistent and the value of coefficient
∅m is highly significant for all countries except Sri Lanka. The results indicates that Korea has the
highest coefficient value followed by Pakistan, Philippines, India, Indonesia, Taiwan, China,
Turkey, Thailand, and Malaysia at 0.956, 0.950, 0.936, 0.905, 0.863, 0.822, 0.777, 0.739, 0.697
and 0.574 respectively at 1% level of significance. All the values of the coefficient are less than 1
without exploding which means that all series of herding behavior follow a stationary process.
These findings are consistent with Hwang & Salmon (2004), even the coefficient values for most
of the countries are higher than compared to their findings.
According to equation 13, when there is no herding towards portfolio in the market, if the
coefficient σnm is equal to zero. However, in emerging and frontier markets σnm the error term, is
different from zero and highly significant at 1% level of significance. When looking at the
proportion of variability explain by herding, this study observe that highest variability in cross-
sectional dispersion of returns is explained through herding behavior in the market of China
(65.10%) followed by Malaysia (58.90%), Thailand (46.00%), Indonesia (44.70%), Taiwan
(38.00%), Korea (32.40%), India (31.10%), Philippines (26.10%), and lowest in Turkey and
118
Pakistan (17.80%). These findings are in line with Demirer et al. (2010) they investigate herding
across industrial sectors using HS (2004) model in Taiwanese market and find herd formation
during a period from 1995-2006. Similarly, Solakoglu & Nihat (2014) find significant herding
behavior of uninformed investors towards market portfolio during a period of 2000 to 2013 in
Turkish stock markets but insignificant herding behavior in informed traders.
All countries in the sample except Sri Lanka exhibit herding behavior around market
portfolio and particularly in stress. These findings are similar to Hwang & Salmon (2004) as the
main purpose of this research is to investigate the irrational herding. Information plays a crucial
role in investor’s decision-making process. Therefore, it can be concluded that herding is more
obvious if there is information asymmetry in the market.
Properties of Herding Measure
Table 4.12 presents the summary statistics of herding evolution hmt over the entire period.
Panel B of the table 4.12 reports the results for developed markets. The Norway market exhibits
lowest mean value of -0.037, and Sweden exhibits highest value of 0.033 of monthly herding index
with a mean value of -0.0046. The mean value is around 0 which means that on average due to
several cycles of adjustment from herding to adverse herding difference markets exhibit minimal
herding. The standard deviation of herding measure varies around 0.041% for Australia and
0.174% for Germany with a mean value of 0.10. The herding measure ranges from a maximum
of 0.407 for Germany to a minimum of -0.547 for Japan. According to JB statistics, herding series
for all developed markets are not normal.
Table 4.12: Descriptive statistics of Herding measure, Hmt Panel A: Developed Markets
119
Obs Mean Maximum Minimum Std. Dev. Skewness Kurtosis JB- stats
hmt_ Australia 192 -0.004 0.101 -0.128 0.052 -0.213 2.093 8.025**
hmt_ Austria 192 -0.005 0.203 -0.172 0.121 0.205 1.508 19.150***
hmt_ Belgium 192 0.002 0.127 -0.056 0.041 1.008 3.611 35.519***
hmt_ Denmark 192 0.002 0.16 -0.224 0.069 -0.58 4.247 11.524***
hmt_ France 192 -0.029 0.384 -0.257 0.151 1.348 4.323 23.219***
hmt_ Germany 192 -0.001 0.407 -0.312 0.166 0.444 2.745 72.141***
hmt_ Greece 192 0.009 0.174 -0.267 0.112 -0.718 2.897 6.830**
hmt_ Hong Kong 192 -0.005 0.138 -0.147 0.099 0.152 1.314 16.586***
hmt_ Italy 192 -0.003 0.186 -0.111 0.058 1.076 4.682 23.482***
hmt_ Japan 192 -0.019 0.282 -0.547 0.174 -0.976 3.425 59.702***
hmt_ Netherland 192 -0.015 0.267 -0.256 0.123 0.53 2.635 31.925***
hmt_ New Zealand 192 -0.001 0.15 -0.08 0.06 0.924 3.068 10.065***
hmt_ Norway 192 -0.037 0.158 -0.137 0.081 0.987 3.039 27.366***
hmt_ Portugal 192 0.010 0.107 -0.108 0.059 -0.334 1.898 31.204***
hmt_ Singapore 192 0.001 0.146 -0.109 0.066 0.089 1.958 13.271***
hmt_ Spain 192 -0.028 0.307 -0.172 0.109 1.467 4.557 8.934***
hmt_ Sweden 192 0.033 0.298 -0.327 0.160 0.307 1.89 88.278***
hmt_ Switzerland 192 0.000 0.117 -0.105 0.053 0.19 2.024 12.869***
hmt_ UK 192 -0.005 0.320 -0.226 0.128 0.83 3.138 8.782**
hmt_ US 192 0.003 0.395 -0.218 0.133 0.983 3.711 22.197***
Panel B: Emerging and Frontier markets.
Obs Mean Maximum Minimum Std. Dev. Skewness Kurtosis JB- stats
hmt_ China 192 -0.001 0.227 -0.327 0.098 -0.442 3.741 10.651***
hmt_ India 192 0.002 0.269 -0.248 0.103 0.442 2.911 6.300**
hmt_ Indonesia 192 0.006 0.299 -0.173 0.11 0.664 2.717 14.743***
hmt_ Korea 192 0.000 0.496 -0.335 0.118 0.548 4.831 36.422***
120
hmt_ Malaysia 192 0.000 0.217 -0.235 0.08 0.411 3.368 6.499***
hmt_ Pakistan 192 -0.003 0.117 -0.118 0.07 -0.156 1.586 16.772***
hmt_ Philippines 192 0.001 0.227 -0.212 0.101 0.092 2.483 2.409
hmt_ Sri Lanka 192 -0.001 0.535 -0.513 0.163 -0.096 3.366 1.366
hmt_ Taiwan 192 0.001 0.122 -0.208 0.055 -0.677 3.567 17.250***
hmt_ Thailand 192 -0.409 -0.291 -0.534 0.046 -0.206 2.71 2.031
hmt_ Turkey 192 0.000 0.048 -0.049 0.021 -0.179 2.6 2.308
Note: properties of herding series calculates through hmt = 1-Hmt, where 𝐻𝑚𝑡 = ∅𝑚𝐻𝑚𝑡−1 + 𝑚𝑡
,
***indicate statistical significance at 1%. **Indicate statistical significance at 5%. *Indicate statistical significance at 10%.
Table 4.12 present the summary statistics of herding evolution hmt over the entire period.
Panel B of the table reports the results for developed markets. The Indonesian market exhibits
highest mean value of 0.006, and Thai market exhibit lowest mean value of -0.409 of monthly
herding index with a mean value of 0.037. The mean value is around 0 which means that on average
due to several cycles of adjustment from herding to adverse herding difference markets exhibit
minimal herding. The standard deviation of herding measure varies around to 0.021% for Turkey
to 0.163% for Sri Lanka with a mean value of 0.08. The herding measure ranges from a maximum
of 0.535 for Sri Lanka to a minimum of -0.049 for Turkey. According to properties of JB statistics,
herding series for all emerging and frontier market is not normally distributed. The herding series
of Philippines, Sri Lanka, Thailand, and Turkey display a normal distribution.
Further insight into the herding evolution has been discussed in next section
Evolution of Herding Measures over Time
Fig. 4.3 to Fig. 4.32 shows the evolution of herding measure hmt =1-exp(Hmt) in developed,
emerging and frontier markets. For most of the countries value of hmt is far less than one. It means
that during the sample period, most of the countries at no time display an extreme level of herding.
121
From the period 1995 to 2015 this study observes several cycles of herding and adverse herding
towards the market value as the herding parameter hmt moves around its long-term zero average.
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AUSTRALIA
(a) Market Index
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AUSTRALIA
(b) Hmt----- Herding Series
Figure 4-3: Herding evolution in Australian Market
Figure 4.3 reports the result of the Australian market. This study analyzes that over the
entire period from 2000 to 2015. Australian market does not exhibit an extreme level of herding
as the value is far away from 1 and is observed within a range of -0.128 to 0.10. It is also verified
by the signal to noise ratio of 0.093. The value of hmt fluctuates around mean value zero and several
cycles herding and adverse herding are observed. The period from 2004 to 2008 is relatively
smooth and four attempts of adjustments to fundamentals are observed after that one peak of
adverse herding in 2009 with one adjustment is observed. Adverse herding is observed during
2000 to 2004, this time period is the aftermaths of the Dotcom Bubble burst that greatly affected
the Australian market. Another extreme peak of adverse herding is observed during the early phase
of 2009. This downfall started with the start of Global financial crisis in 2008, The stock market
decline by 17% by the end of January 2008 and further 41% in June 2009 from its level of Nov
2007, creating huge losses for investors, and large pension fund sector and provide several
concerns about the regulatory structure of the securities market.
122
The Australian market quickly recovered from the turbulent period in mid-2009 better than many
developed markets 7, the situation is not without risks. Afterwards, positive value of herding is
observed from 2010 to 2015. This study observes a similar positive growth during a period of 2003
to 2007 and a sharp decline in the market index during the earlier phase of 2009. This time period
can be referred to the Global crisis that impacted the markets around the globe.
0
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AUSTRIA
(a) Market Index
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AUSTRIA
(b) Hmt---Herding Series
Figure 4-4: Herding evolution in Australian Market
Figure 4-4 reports the results of Austrian stock market. Similar to Austrian market, there
is never an extreme level of herding in the market as the value is far away from 1 and the value is
observed within a range of -0.172 to 0.203 over the entire period. This is also substantiated by the
signal to noise ratio of 0.18. Several cycles of herding and adverse herding are observed. It is
observed that during a period of 2000 to 2008 Austrian market remained stable and grew at a
steady rate. Due to the strong institutional framework, the market is not destabilized by
macroeconomic imbalances and external shocks. The impact of the Global crisis remains moderate
but not negligible. The unique structure of the market helped it to avoid extreme macroeconomic
shocks that intensified the impact of the crisis. However, financial sector stability is the major
7 Gruen (2009) provide a comprehensive analysis of the triggering factors for the Global Financial Crisis and the reason as to how Australia is least affected than elsewhere.
123
challenges faced by the Austrian authorities throughout the crisis period. External expansion and
Internal Stagnation of the Banking Sector exposed the Austrian market to financial contagion just
before the Global Financial Crisis that can be observed in part (b) of Figure 4.4. This effect is
further exacerbated by the Euro Crisis and Ukrainian/Russian crisis due to sanctions imposed by
the EU on Russia. Despite all these effects Austrian market is able to maintain low unemployment
and high living standards.
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BELGIUM
(a) Market Index
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BELGIUM
(b) Hmt----Herding Series
Figure 4-5: Herding Evolution in Belgium Market
Figure 4-5 reports the results of Belgian stock market. Similar to other markets, there is
never an extreme level of herding in the market as the value is far away from 1 and the value is
observed within a range of -0.056 to 0.127 over the entire period. This is also substantiated by the
signal to noise ratio of 0.106 which implies that 10.6 percent change in dispersion of portfolio beta
is due to Herding. The value of hmt fluctuates around mean value zero and several cycles of herding
and adverse herding are observed. The period from 2000 to 2001 is relatively smooth and shows
a low level of herding as the value remained close to zero. From 2002 to 2004 Belgian market
exhibits adverse herding. Belgian market faced almost six peaks of adverse herding during the
sample period with four adjustments. The time period of Global financial crisis is seen to have
least impact on the investor behavior as in most of the time period observe value in a positive
124
region far away from 1. Adverse herding is observed during 2002 to 2004, similarly, another
episode of adverse herding is observed from the last quarter of 2005 to first quarter of 2008. In this
time period market activity of Belgian market slowed down due to the effect of the global financial
crisis. The financial sector of the market was greatly hit as the market faced a 12.5 % increase in
bankruptcies. At the end of third quarter of 2009, Belgium starts slow recovery in the
fundamentals. It can be observed in figure (a) of the graph. It is observed that from 2010 to 2015
with the exception of a small adjustment and positive herding from the last quarter of 2010 to 2012
the market faced an adverse herding. These findings can be related to the fact that herding behavior
of an investor is not affected by the fundamental driven factors but might be due to liquidity
withdrawal which may result into costly liquidation due to the banking crisis and as a result asset
prices collapse (Chang and Velasco, 2001).
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CHINA
(a) Market Index
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CHINA
(b) Hmt---Herding Series
Figure 4-6: Herding Evolution in Chinese Market
Figure 4-6 reports the result of the Chinese market. This study analyzes that over the entire period
from 2000 to 2015 Chinese market does not exhibit an extreme level of herding as the value is far
away from 1 and the value is observed within a range of -0.327 to 0.227. From table 4.12, it is
verified that the signal to noise ratio is 0.651 which implies that 65 percent change in dispersion
of portfolio beta is due to Herding. It means that Chinese market is one of the markets that are
125
highly affected by the herding behavior. The value of hmt fluctuates around mean value zero and
several cycles of herding and adverse herding are observed.
From 2006 to 2009 Chinese market experienced a sharp increase followed by a sudden downfall.
In 2006 china reliance on debts increased to 170%. The simultaneously financial global crisis hit
the global market and that has an effect on the Chinese market as well witnessed by a sharp
downturn in both stock index and herding evolution. During this period the volume of debt
increased to almost 286% of GDP. According to Kaminsky & Reinhart (2000) countries with
strong financial interdependence are more likely to be affected by the same crisis. From 2008 to
2014 several cycles of herding observed in the positive range of the graph. During this period
Chinese market become less reliant on the export-based model as exports reduce from 35% to 29%
of GDP. Similarly, import reliance also reduces from 29% to 19% due to increased industrial
production as Chinese market exposure to foreign market capital flows decline.
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DENMARK
(a) Market Index
-.3
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2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
DENMARK
(b) Hmt----Herding Series
Figure 4-7: Herding Evolution in Danish Market
Figure 4.7 reports the result of Denmark market. This study analyzes that over the entire period
from 2000 to 2015 Denmark market does not exhibit an extreme level of herding as the value is
far away from 1 and the value is observed within a range of -0.224 to 0.16 from table 4.12. It is
also verified by the signal to noise ratio of 0.266 which implies that 26.6 percent change in
126
dispersion of portfolio beta is due to Herding. The value of hmt fluctuates around mean value zero
and several cycles of herding and adverse herding are observed.
Figure 4.8 reports the results of French stock market. Similar to other markets, there is
never an extreme level of herding in the market as the value is far away from 1 and the value is
observed within a range of -0.257 to 0.384 over the entire period. It is also verified by the signal
to noise ratio of 0.255 which implies that 25.5 percent change in dispersion of portfolio beta is due
to Herding. The value of hmt fluctuates around mean value zero and several cycles of herding and
adverse herding are observed. French market exhibit adverse herding from 2003 to 2015 with an
adjustment in the end of 2008 that lasts until the mid of 2009.
From 2001 to 2003 French market face a sharp decline, this decline was caused by a shock
that was generated due to the adoption of Euro as a single currency. The French market settled
with Euro but as a result, inflation was created and consumers lost their confidence and changed
their consumption behavior, cut spending that resulted in reduced demand. The GDP growth
declines to 3.9% from 2001 to 1.1% in 2003. The GDP started to increase in 2004 and then again
a decline is observed in 2005.
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7,000
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2011
2012
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2014
2015
FRANCE
(a) Market Index
-.3
-.2
-.1
.0
.1
.2
.3
.4
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2010
2011
2012
2013
2014
2015
FRANCE
(b) Hmt----Herding Series
Figure 4-8: Herding Evolution in French Market
127
Global Financial crisis significantly affects the French market as it leads to several bank
failure, the collapse of financial markets, reduced household consumption and a panic in overall
sectors of the market. This crisis leads to the financial crisis as it caused the wealth to be drowned
into the stock markets that result in reduced investment. As a result, government cuts spending to
rescue the French financial system. The GDP remains positive in 2007 but declined due to
decreased consumption. GDP further decline in 2008 but 2009 shows a recovery, this trend can be
observed in both parts of the graphs. This uprise in financial system during 2009 can be a result of
financial allocation by the Government that results in increased savings. Even the French market
is badly hurt by the crisis, in the last quarter of 2009 market recover from the recession. This study
observes an adjustment and a positive trend in investor herding behavior.
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2011
2012
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2014
2015
GERMANY
(a) Market Index
-.4
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.0
.1
.2
.3
.4
.5
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2010
2011
2012
2013
2014
2015
GERMANY
(b) Hmt---Herding Series
Figure 4-9: Herding Evolution in German Market
Figure 4.9 reports the result of the German market. This study analyzes that over the entire
period from 2000 to 2015 Denmark market does not exhibit an extreme level of herding as the
value is far away from 1 and the value is observed within a range of -0.312 to 0.407 from table
4.12. It is also verified by the signal to noise ratio of 0.374 which implies that 37.4 percent change
in dispersion of portfolio beta is due to Herding. The value of hmt fluctuates around mean value
zero and several cycles herding and adverse herding are observed. The period of 2000 to 2006
128
German market exhibit positive herding. From 2006 to 2015 several cycles of adverse herding are
observed with five adjustments in the different time periods. From 2007-2009 the time periods can
be classified as the Global financial crisis. During the global financial crisis, Germany underwent
a decline in GDP in last quarter of 2008 that continued to become negative in early 2009, the
recovery started in the third quarter and positive growth is observed in 2010. By the mid of 2011,
Germany is well on its way to recovery and Euro debt crisis had minimal effect on the German
economy and the growth cycle continued as one can observe it in the first part of the graph.
0
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2014
2015
GREECE
(a) Market Index
-.3
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-.1
.0
.1
.2
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2012
2013
2014
2015
GREECE
(b) Hmt---Herding Series
Figure 4-10: Herding evolution in Greece Market
Figure 4.10 reports the result of the Greek market. This study analyzes that over the entire period
from 2000 to 2015 Greek market does not exhibit an extreme level of herding as the value is far
away from 1 and the value is observed within a range of -0.267 to 0.174 from table 4.12. It is also
verified by the signal to noise ratio of 0.271 which implies that 27.1 percent change in dispersion
of portfolio beta is due to Herding. The value of hmt fluctuates around mean value zero and several
cycles herding and adverse herding are observed. From 2001 to 2007greek market observe herding
in the positive region this period is classified as a period of economic growth. This time started
shortly after joining the single currency. A sudden shock is observed with the tumbling down
position in 2008. This period is classified as the period of the financial crash of 2008. Like many
129
other European markets, Greece is seriously affected. In late 2011 and 2012 government
implement several structural reforms to strengthen the economy that can be observed by the regain
of investor confidence in part (b) of the graph.
Figure 4.11 reports the result of Hong Kong market. This study analyzes that over the entire
period from 2000 to 2015 Hong Kong market does not exhibit an extreme level of herding as the
value is far away from 1 and the value is observed within a range of -0.147 to 0.138 from table
4.12. It is also verified by the signal to noise ratio of 0.089 which implies that 8.9 percent change
in dispersion of portfolio beta is due to Herding. The value of hmt fluctuates around mean value
zero and several cycles herding and adverse herding are observed. Honk Kong economy observes
a downturn in 2008 following the Global financial crisis. Hong Kong economy is the trade-oriented
economy and hit hard by the crisis in terms of GDP, employment, and exports. The economic
growth declined by 6.4% in 2007 to 2.5% in 2008 which further declined to -2.5 in 2009. Exports
also declined by 21.8% in 2009 followed by the sharp decline in Hang Seng Index by 48% in these
years.
8,000
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24,000
28,000
32,000
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
HONGKONG
(a) Market Index
-.15
-.10
-.05
.00
.05
.10
.15
2000
2001
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2007
2008
2009
2010
2011
2012
2013
2014
2015
HONGKONG
(b) Hmt--- Herding Series
Figure 4-11: Herding evolution in Hong Kong Market
These time periods can be observed easily in part b of the graph. The herding evolution remains
in the adverse region after the introduction of the crisis situation. Hong Kong’s open economy left
130
it exposed to the global economic slowdown, its integration with Chinese economy has reduced
the effect of the crisis. A recovery began in the third quarter of 2009, and the economy grew nearly
6.8% in 2010.
0
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8,000
2000
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2011
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2014
2015
INDIA
(a) Market Index
-.3
-.2
-.1
.0
.1
.2
.3
2000
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2003
2004
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2009
2010
2011
2012
2013
2014
2015
INDIA
(b) Hmt----Herding Series
Figure 4-12: Herding Evolution in Indian Market
Figure 4.12 reports the result of the Indian market. This study analyzes that over the entire period
from 2000 to 2015 Indian market does not exhibit an extreme level of herding as the value is far
away from 1 and the value is observed within a range of -0.248 to 0.269 from table 4.12. It is also
verified by the signal to noise ratio of 0.313 which implies that 31.3 percent change in dispersion
of portfolio beta is due to Herding. The value of hmt fluctuates around mean value zero and several
cycles of herding and adverse herding are observed. The Indian economy is greatly affected by
the dotcom collapse (2000) and global recession of 2001 and also affected by the global uncertainty
caused by the invasion of Iraq in 2003. From 1997-2003 Indian economy faced a decline in GDP
growth to a level of 5.7 percent. The Indian economy performed well in coming years from 2003-
2008, and touch the peak of 9% increase in GDP per year for consecutive three years 2005-2008.
The Global financial crisis is observed to have minimal effect on investor herding behavior
as adverse herding is observed closer to 1 from 2007-2008. Adverse herding is observed during
2010–11 due to the widespread charges of corruption, which led to paralysis in decision making
131
and huge bank losses the slowdown of the economy and lost investor confidence regained after
the defeat of the Congress Party. New political system tackle some of the serious problems and an
economic boost and investor confidence is observed after 2014 and growth increases to estimated
7.5 percent in 2015.
0
1,000
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4,000
5,000
6,000
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
INDONESIA
(a) Market Index
-.2
-.1
.0
.1
.2
.3
.4
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
INDONESIA
(b) Hmt----Herding Series
Figure 4-13: Herding Evolution in Indonesian Market
Figure 4.13 reports the result of the Indonesian market. This study analyzes that over the
entire period from 2000 to 2015 Indonesian market does not exhibit an extreme level of herding
as the value is far away from 1 and the value is observed within a range of -0.173 to 0.299 from
table 4.12. It is also verified by the signal to noise ratio of 0.304 which implies that 30.4 percent
change in dispersion of portfolio beta is due to Herding. The value of hmt fluctuates around mean
value zero and several cycles of herding and adverse herding are observed.
In early 2000, Indonesian economy is seen struggling to come out of the aftermaths of Asian
financial crisis of 1997 and is also going through the political instability that ended up the Suharto
regime. During this period Indonesian economy went through persistent capital outflow, exchange
rate volatility and inflationary pressures which led to fragile investor sentiment throughout a
decade (Jayasuriya & Leu, 2017). This phenomenon can be observed in Figure 4.13, from 2000 to
2009 Indonesian economy observes adverse herding with two peaks in 2006 and 2008. Former can
132
be attributed to the political instability due to the firing of high profile government officials and
later is connected to the Global financial crisis which led to currency depreciation due to risk
perception as a result of declining export prospects. From mid-2009 Indonesian economy observe
large capital inflows which led to the recovery of the economic system as that can be observed in
figure 4.13, part b. Another extreme adverse herding shock observes in 2011 that can be an
outcome of European Debt crisis that generated a huge turmoil and reversal of cash flow, resulting
in increased risk perception and investors shifted their funds abroad. The Indonesian economy
quickly resumes in coming years with the exception of a most recent global shock that hits the
economy in May 2013.
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20,000
25,000
30,000
35,000
40,000
45,000
50,000
2000
2001
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2008
2009
2010
2011
2012
2013
2014
2015
ITALY
(a) Market Index
-.12
-.08
-.04
.00
.04
.08
.12
.16
.20
2000
2001
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2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
ITALY
(b) Hmt---Herding Series
Figure 4-14: Herding Evolution in Italian Market
Figure 4.14 reports the result of the Italian market. This study analyzes that over the entire
period from 2000 to 2015 Italian market does not exhibit an extreme level of herding as the value
is far away from 1 and the value is observed within a range of -0.11 to 0.186 from table 4.12. It is
also verified by the signal to noise ratio of 0.133 which implies that 13.3 percent change in
dispersion of portfolio beta is due to Herding. The value of hmt fluctuates around mean value zero
and several cycles of herding and adverse herding are observed. During the global financial crisis,
Italian investor and banks suffered little in the early phase of the crisis after the collapse of the
133
Lehman Brothers collapse in September 2008 (Quirico, 2010). This effect can be easily observed
in the time period from 2007 to the start of 2009. Italian economy observes an adverse herding
peak in 2010to 2013. This time period can be related to the Eurozone crisis that adversely affected
the Italian economy during these years. The economy contracted by around 10% since 2007
followed by a recession. The output is at its lowest, unemployment declined to 12%-13%,
followed by a cut in consumption and investment. Smaller enterprises suffer a decline in sales,
profitability, and lack of financing. The banking system turmoil exacerbates the volatility situation
and investor confidence as well.
6,000
8,000
10,000
12,000
14,000
16,000
18,000
20,000
22,000
2000
2001
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2003
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2007
2008
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2010
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2014
2015
JAPAN
(a) Market Index
-.6
-.5
-.4
-.3
-.2
-.1
.0
.1
.2
.3
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
JAPAN
(b) Hmt---Herding Series
Figure 4-15: Herding Evolution in Japanese Market
Figure 4.15 reports the result of the Japanese market. This study analyzes that over the
entire period from 2000 to 2015 Japanese market does not exhibit an extreme level of herding as
the value is far away from 1 and the value is observed within a range of -0.547 to 0.282 from table
4.12. It is also verified by the signal to noise ratio of 0.292 which implies that 29.2 percent change
in dispersion of portfolio beta is due to Herding. The value of hmt fluctuates around mean value
zero and several cycles of herding and adverse herding are observed. Japanese economy faced a
financial recession since 1990. A significant rise is observed after 2013 followed by a substantial
increase in money supply. The adverse herding peaks in the graph can be attached to 2000 US
134
bubble and economic shutdown, the 2007 subprime mortgage crisis, and the 2008 Lehman
Brothers bankruptcy (a slight peak in adverse herding), and 2011 Eurozone crisis.
Figure 4.16 reports the result of Korea market. This study analyzes that over the entire
period from 2000 to 2015 Korea market does not exhibit an extreme level of herding as the value
is far away from 1 and the value is observed within a range of -0.335 to 0.496 from table 4.12. It
is also verified by the signal to noise ratio of 0.324 which implies that 32.4 percent change in
dispersion of portfolio beta is due to Herding. The value of hmt fluctuates around mean value zero
and several cycles of herding and adverse herding are observed. Highly fluctuating herding
behavior is observed in Korean market with several episodes of adverse and normal herding with
a number of adjustments.
50
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300
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2001
2002
2003
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2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
KOREA
(a) Market Index
-.4
-.2
.0
.2
.4
.6
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
KOREA
(b) Hmt---Herding Series
Figure 4-16: Herding Evolution in Korean market
Figure 4.17 reports the result of Malaysian market. This study analyzes that over the entire period
from 2000 to 2015 Malaysian market does not exhibit an extreme level of herding as the value is
far away from 1 and the value is observed within a range of -0.235 to 0.217 from table 4.12. It is
also verified by the signal to noise ratio of 0.589 which implies that 58.9 percent change in
dispersion of portfolio beta is due to Herding. The value of hmt fluctuates around mean value zero
and several cycles of herding and adverse herding are observed. During the global financial crisis,
135
the share prices fell to 20% from 2007 to 2009. There is a massive outflow of short-term capital
flows in 2009. These shocks were rapidly absorbed due to sound financial and banking system.
There are several episodes of crisis but Malaysian economy managed it well due to strong banking
and reserve systems. The impact of the financial crisis of 2008 would have been much more severe
and shaken the investor confidence if it is not backed by the sound macroeconomic discipline and
timely actions taken by the Malaysian authorities by the Asian financial crisis.
400
600
800
1,000
1,200
1,400
1,600
1,800
2,000
2000
2001
2002
2003
2004
2005
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2009
2010
2011
2012
2013
2014
2015
MALAYSIA
(a) Market Index
-.3
-.2
-.1
.0
.1
.2
.3
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
MALAYSIA
(b) Hmt---Herding Series
Figure 4-17: Herding Evolution in Malaysian Market
Figure 4.18 reports the result of Netherlands market. This study analyzes that over the entire period
from 2000 to 2015 Netherlands market does not exhibit an extreme level of herding as the value
is far away from 1 and the value is observed within a range of -0.256 to 0.267 from table 4.12. It
is also verified by the signal to noise ratio of 0.389 which implies that 38.9 percent change in
dispersion of portfolio beta is due to Herding. The value of hmt fluctuates around mean value zero
and several cycles of herding and adverse herding are observed. Two episodes of adverse herding
are observed in Netherlands market. One is witnessed in 2003 and another one is followed by the
Global financial crisis. The adverse herding in the first phase is witnesses after the first quarter of
2003 the time of largest downturn in the history of the stock market of Netherlands. Second adverse
136
phase can be witnessed as the effect of Global Financial crisis. Both the Graphs simultaneously
exhibit downtrend. It means that investor is highly reactive to the stock market functioning.
200
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2008
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2010
2011
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2015
NETHERLAND
(a) Market Index
-.3
-.2
-.1
.0
.1
.2
.3
2000
2001
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2007
2008
2009
2010
2011
2012
2013
2014
2015
NETHERLAND
(b) Hmt---Herding Series
Figure 4-18: Herding Evolution in Netherlands Market
Figure 4.19 reports the result of New Zealand market. This study analyzes that over the
entire period from 2000 to 2015 New Zealand market does not exhibit an extreme level of herding
as the value is far away from 1 and the value is observed within a range of -0.08 to 0.15 from table
4.12. It is also verified by the signal to noise ratio of 0.101 which implies that 10.1 percent change
in dispersion of portfolio beta is due to Herding. The value of hmt fluctuates around mean value
zero and several cycles of herding and adverse herding are observed. In New Zealand, market
investor herding behavior is not much intense as the values are far away from 1. From 2000 to
2009 adverse herding is observed and from 2010 onward New Zealand investor exhibit normal
herding behavior towards market portfolio. Like other OECD markets, New Zealand also went
through economic slow-down after the global financial crisis in September 2008. The consumer
and business confidence decline but the economy recovered mainly through exports, strong
Government accounts and sound banking system proved to be the stable base. These policy boosts
led to an increase in the level of GDP in 2010 and 2011. The New Zealand stock market has
137
recovered strongly from the global financial crisis from 1 April 2009 to 30 June 2015 that can be
seen in both Graphs
1,600
1,800
2,000
2,200
2,400
2,600
2,800
3,000
3,200
3,400
2000
2001
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2007
2008
2009
2010
2011
2012
2013
2014
2015
NEWZEALAND
(a) Market Index
-.10
-.05
.00
.05
.10
.15
.20
2000
2001
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2003
2004
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2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
NEWZEALAND
(b) Hmt---Herding Series
Figure 4-19: Herding Evolution in New Zealand Market
Figure 4.20 reports the result of Norwegian market. This study analyzes that over the entire
period from 2000 to 2015 Norwegian market does not exhibit an extreme level of herding as the
value is far away from 1 and the value is observed within a range of -0.137 to 0.158 from table
4.12. It is also verified by the signal to noise ratio of 0.079 which implies that 7.9% percent change
in dispersion of portfolio beta is due to Herding. The value of hmt fluctuates around mean value
zero and several cycles of herding and adverse herding are observed. Norwegian economy
experience a herding behavior from 2000 to 2003 after that period adverse herding is observed.
The initial decline in the stock market is attached to the dotcom bubble effect that is not adverse
for the Norwegian economy. The global financial crisis has significant effect followed by a drop
in market index beginning of 2008, as a result of the global financial countries. In 2009 the stock
markets increase and fall again during 2010.
138
100
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700
800
2000
2001
2002
2003
2004
2005
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2007
2008
2009
2010
2011
2012
2013
2014
2015
NORWAY
(a) Market Index
-.15
-.10
-.05
.00
.05
.10
.15
.20
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
NORWAY
(b) Hmt---Herding Series
Figure 4-20: Herding Evolution in Norway Market
Figure 4.21 reports the result of Pakistani market. This study analyzes that over the entire
period from 2000 to 2015 Pakistani market does not exhibit an extreme level of herding as the
value is far away from 1 and the value is observed within a range of -0.118 to 0.117 from table
4.12. It is also verified by the signal to noise ratio of 0.178 which implies that 17.8% percent
change in dispersion of portfolio beta is due to Herding. The value of hmt fluctuates around mean
value zero and several cycles of herding and adverse herding are observed. From figure 4.21, it is
observed that from 2005-2010 Pakistani investor exhibit adverse herding behavior. The herding
appears to be stationary and time-varying. This indicates that in most of the time period Pakistan
economy exhibit significant herding activities especially during and post-crisis periods it remains
negative. The time period of crisis is attributed global financial crisis of 2008. Before the global
financial crisis, three local crisis hits the economy. The domestic crisis period is stock market crash
of 2005, 2006 and 2008. During all three domestic crisis, the stock market shows persistent herding
behavior, as the herding coefficient remain negative during the crisis and the post-crisis period.
139
0
5,000
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15,000
20,000
25,000
30,000
35,000
40,000
2000
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2003
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2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
PAKISTAN
(a) Market Index
-.15
-.10
-.05
.00
.05
.10
.15
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
PAKISTAN
(b) Hmt---Herding Series
Figure 4-21: Herding Evolution in Pakistani Market
Figure 4.22 reports the result of Philippines market. This study analyzes that over the entire
period from 2000 to 2015 Philippines market does not exhibit an extreme level of herding as the
value is far away from 1 and the value is observed within a range of -0.212 to 0.227 from table
4.12. It is also verified by the signal to noise ratio of 0.261 which implies that 26.1% percent
change in dispersion of portfolio beta is due to Herding.
0
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8,000
2000
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2008
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2010
2011
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2013
2014
2015
PHILPINES
(a) Market Index
-.3
-.2
-.1
.0
.1
.2
.3
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
PHILIPPINES
(b) Hmt---Herding Series
Figure 4-22: Herding Evolution in Philippines Market
The value of hmt fluctuates around mean value zero and several cycles of herding and
adverse herding are observed. The Philippine economy faced a mild recession in 2003 and growth
rates are lower than 2 percent. Soon it recovered but the major increase in this unemployment is
observed aftermaths of the Lehman Brothers breakdown in late 2008.
140
Figure 4.23 reports the result of Portuguese market. This study analyzes that over the entire period
from 2000 to 2015 Philippines market does not exhibit an extreme level of herding as the value is
far away from 1 and the value is observed within a range of -0.108 to 0.107 from table 4.12. It is
also verified by the signal to noise ratio of 0.131 which implies that 13.1% percent change in
dispersion of portfolio beta is due to Herding.
4,000
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8,000
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16,000
2000
2001
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2008
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2014
2015
PORUGAL
(a) Market Index
-.12
-.08
-.04
.00
.04
.08
.12
2000
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2006
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2008
2009
2010
2011
2012
2013
2014
2015
PORTUGAL
(b) Hmt---Herding Series
Figure 4-23: Herding Evolution in Portugal Market
The value of hmt fluctuates around mean value zero and several cycles of herding and adverse
herding are observed. Several cycles of herding and adverse herding are observed. The Portuguese
economy is one of the best markets during 2002-2007 and continued to grow positively during
most of 2008. The impact of the global financial crisis is felt in last quarter of 2008 but it recovered
through timely reforms including growth in exports. Low exposure of the financial system to
banking system keep the country secure from turmoil. It can be easily observed in both panels of
the graph.
Figure 4.24 reports the result of Singapore market. This study analyzes that over the entire period
from 2000 to 2015 Singapore market does not exhibit an extreme level of herding as the value is
far away from 1 and the value is observed within a range of -0.109 to 0.146 from table 4.12. It is
141
also verified by the signal to noise ratio of 0.167 which implies that 16.7% percent change in
dispersion of portfolio beta is due to Herding.
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SINGAPORE
(a) Market Index
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SINGAPORE
(b) Hmt---Herding Series
Figure 4-24: Herding Evolution in Singapore market
The value of hmt fluctuates around mean value zero and several cycles of herding and
adverse herding are observed. Despite increased employment in 2007 and 2008 and averaging a
growth rate of nearly 10 percent from 2004 to 2007. Singapore is the first and worst hit economy
in the East Asian country by the global economic crisis in July 2008. Till 2009 situation continued
after 2010 the growth rate increased at a rate of 3 to 5 % on average. After this period economy
recovered at a rapid pace.
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SPAIN
(a) Market Index
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SPAIN
(b) Hmt---Herding Series
Figure 4-25: Herding Evolution in Spanish Market
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Figure 4.25 reports the result of the Spanish market. This study analyzes that over the entire
period from 2000 to 2015 Spanish market does not exhibit an extreme level of herding as the value
is far away from 1 and the value is observed within a range of -0.172 to 0.307 from table 4.12. It
is also verified by the signal to noise ratio of 0.163 which implies that 16.3% percent change in
dispersion of portfolio beta is due to Herding. The value of hmt fluctuates around mean value zero
and several cycles of herding and adverse herding are observed. During the 1990s and early 2000s,
Spain enjoyed rapid economic growth. Spain is now the 5th largest EU economy. The economy
hits badly by the global financial crisis. Overall Spanish economy faced adverse herding from
2002 to 2014, with very few adjustments.
Figure 4.26 reports the result of Sri Lankan market. This study analyzes that over the entire
period from 2000 to 2015 Sri Lankan market exhibit extreme level of herding as the value is greater
than 0.5 in most of the regions and the value is observed within a range of -0.513 to 0.535 from
table 4.12. It is also verified by the signal to noise ratio of 1.246 which implies that 124.6 % percent
change in dispersion of portfolio beta is due to Herding. The value of hmt fluctuates around mean
value zero and several cycles of herding and adverse herding are observed.
0
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SRILANKA
(a) Market Index
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SRILANKA
(b) Hmt--Herding Series
Figure 4-26: Herding Evolution in Sri Lankan Market
143
The Singapore economy exhibits an intense level of herding during the entire period. The
global financial crisis is no surprise as the economy in the whole time period faced several cycles
of herding and adverse herding.
Figure 4.27 reports the result of Swedish market. This study analyzes that over the entire
period from 2000 to 2015 Swedish market does not exhibit an extreme level of herding as the value
is far away from 1 and the value is observed within a range of -0.327 to 0.298 from table 4.12. It
is also verified by the signal to noise ratio of 0.143 which implies that 14.3% percent change in
dispersion of portfolio beta is due to Herding. The value of hmt fluctuates around mean value zero
and several cycles of herding and adverse herding are observed. In early years Swedish economy
observes positive herding behavior from 2005 onward several episodes of adverse herding are
observed, Sweden is traditionally highly dependent on exports, leaving the economy at the mercy
of the fluctuations in global markets. During the time the Krona appreciated that has worsened the
situation during the global financial crisis and the economy struggles through several structural
breaks.
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SWEDEN
(a) Market Index
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SWEDEN
(b) Hmt---Herding Series
Figure 4-27: Herding Evolution in Swedish Market
Figure 4.28 reports the result of Swiss market. This study analyzes that over the entire
period from 2000 to 2015 Swiss market does not exhibit an extreme level of herding as the value
144
is far away from 1 and the value is observed within a range of -0.105 to 0.117 from table 4.12. It
is also verified by the signal to noise ratio of 0.106 which implies that 10.6 percent change in
dispersion of portfolio beta is due to Herding. The value of hmt fluctuates around mean value zero
and several cycles of herding and adverse herding are observed.
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SWITZERLAND
(a) Market Index
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SWITZERLAND
(b) Hmt---Herding Series
Figure 4-28: Herding Evolution in Swiss Market
The Swiss economy exhibits a major spike in 2011, the global financial crisis has little
effect on herding behavior but Eurozone crisis increases the intensity of herding as this time period
Switzerland, has been especially affected, as its major trading partner is the European Union.
Almost 60% of all its merchandise exports in 2010 went to the EU. The euro crisis hit the Swiss
economy through trade effects.
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THAILAND
(a) Market Index
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THAILAND
(b) Hmt---Herding Series
Figure 4-29: Herding Evolution in Thai Market
145
Figure 4.29 reports the result of Thailand market. This study analyzes that over the entire
period from 2000 to 2015 Thailand market exhibit extreme level of herding as the value is around
0.5 throughout the period and value is observed within a range of -0.534 to -0.291 from table 4.12.
It is also verified by the signal to noise ratio of 0.46 which implies that 46 percent change in
dispersion of portfolio beta is due to Herding. The value of hmt fluctuates around mean value of 0.5
and several cycles of adverse herding are observed. Thailand is the economy where this study
observe a continuous period of adverse herding as a dynamic process. This is due to the intense
role of the institutional investor in the market, the presence of information asymmetry and the high
cost of acquiring information leads the Thai investor towards intense herding behavior.
0
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TURKEY
(a) Market Index
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TURKEY
(b) Hmt---Herding Series
Figure 4-30: Herding Evolution in Turkish Market
Figure 4.30 reports the result of the Turkish market. This study analyzes that over the entire
period from 2000 to 2015 Turkish market does not exhibit an extreme level of herding as the value
is far away from 1 and the value is observed within a range of -0.049 to 0.048 from table 4.12. It
is also verified by the signal to noise ratio of 0.178 which implies that 17.8 percent change in
dispersion of portfolio beta is due to Herding. The value of hmt fluctuates around mean value zero
and several cycles of herding and adverse herding are observed. Herding behavior in Turkish
market is between the ranges of very nominal values. And global crisis and Euro crisis fail to put
146
strong pressure on the economy. Turkish economy followed a fast pace of growth in last years and
perform wonderfully since the global financial crisis. Therefore herding behavior has minimal
effect on the Turkish investors.
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UK
(a) Market Index
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UK
(b) Hmt---Herding Series
Figure 4-31: Herding Evolution in UK market
Figure 4.31 reports the result of UK market. This study analyzes that over the entire period
from 2000 to 2015 UK market does not exhibit an extreme level of herding as the value is far away
from 1 and the value is observed within a range of -0.226 to 0.320 from table 4.12. It is also verified
by the signal to noise ratio of 0.564 which implies that 56.4 percent change in dispersion of
portfolio beta is due to Herding. The value of hmt fluctuates around mean value zero and several
cycles of herding and adverse herding are observed. From the graph, one can observe that none of
the Global and Eurozone crisis affects the Herding behavior of UK investors. During both time
periods herding measure either remained in the positive region or value vary below -0.1.
Figure 4.32 reports the result of US market. This study analyzes that over the entire period
from 2000 to 2015 US market does not exhibit an extreme level of herding as the value is far away
from 1 and the value is observed within a range of -0.218 to 0.395 from table 4.12. It is also verified
by the signal to noise ratio of 0.33 which implies that 33 percent change in dispersion of portfolio
beta is due to Herding.
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US
(a) Market Index
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US
(b) Hmt---Herding Series
Figure 4-32: Herding Evolution in US market
The value of hmt fluctuates around mean value zero and several cycles of herding and
adverse herding are observed. Surprisingly during and after the global financial crisis, the US
investor exhibit positive herding and the value of herding remained in the positive region. The
intense level of herding had never been observed during the period.
Selection of best Model based on AIC and BIC
Table 4.13 reports the comparison of return dispersion models and state space models based on
the AIC and BIC. For model selection in classical settings, the exact likelihood value is needed to
compute model selection criteria such as the AIC or BIC (de Valpine 2008). According to the AIC
and BIC values, it is observed that the state space model presented by Hwang & Salmon (2004)
outperform as compared to the Return dispersion model proposed by the (Chiang et al., 2013).
This conclusion is based on the smaller values of AIC and BIC in state space model as compared
to the dynamic return dispersion model in all countries in a sample.
Table 4.13: Selection of model based on AIC and BIC Panel A: Developed Market
Country Return Dispersion Model State Space Model
AIC BIC AIC BIC
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Australia 0.213 0.218 -1.055 -0.987
Austria 0.928 0.933 -0.257 -0.189
Belgium -0.194 -0.189 -1.5 -1.432
Denmark -7.85 -7.844 -1.126 -1.058
France 0.38 0.386 -1.05 -0.982
Germany 0.375 0.381 -2.192 -2.124
Greece 1.627 1.631 -0.118 -0.05
Hong Kong 1.168 1.172 -1.341 -1.287
Italy 0.423 0.429 -1.211 -1.143
Netherland 0.621 0.624 -0.307 -0.24
New Zealand ----- ----- -0.577 -0.509
Norway 0.982 0.986 -1.261 -1.193
Portugal 1.541 1.546 -0.085 -0.018
Singapore 1.062 1.068 -0.511 -0.443
Spain 0.458 0.462 -1.561 -1.493
Sweden 0.803 0.807 -1.111 -1.044
Switzerland ----- ----- -1.606 -1.538
UK 0.093 0.097 -1.035 -0.967
US -0.441 -0.437 -1.082 -1.014
Panel B: Emerging and Frontier markets
Country Return Dispersion Model State Space Model
AIC BIC AIC BIC
China -8.413 -8.408 -1.125 -1.078
India 0.899 0.904 -1.25 -1.182
Indonesia 1.215 1.221 -0.097 -0.029
Korea 1.402 1.407 -1.202 -1.134
Malaysia 0.198 0.203 -1.22 -1.152
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Pakistan 1.06 1.065 -0.535 -0.467
Philippines 1.253 1.258 -0.303 -0.235
Sri Lanka 0.884 0.889 0.111 0.179
Taiwan 0.567 0.572 ------ -----
Thailand 1.184 1.189 -1.286 -1.218
Turkey 1.752 1.757 -2.273 -2.205
The superiority of the Hwang & Salmon (2004) model can be due to the fact that these models
incorporate the risk and return characteristics of the market and incorporate the effect of
fundamental information and also incorporate the effect of time series volatility. The return
dispersion model fails to capture this characteristic of the market. This is the reason, out of all
three measures this model has strong theoretical properties and provide better empirical results
which are further validated through the selection based criteria’s. These results meet the objective
of best model selection and have implication for future studies and markets.
Estimation of Macroeconomic Shocks on Herding Behavior
The extraordinary or infrequently occurring event has an important effect on modeling
financial and macroeconomic time series. These are classified as outliers or a specific shock. These
shocks have relative importance in behavioral finance literature and can be detected through
several methods based on intervention analysis as originally proposed by Box & Tiao (1975).
Another commonly used method is that of Tsay (1988). This study utilizes Box-Jenkins ARIMA
model for the detection of shocks that have an effect on the herding behavior of international markets.
Table 4.14 presents the results of the structure of estimated ARIMA model for the selected
macroeconomic variables. The basic idea behind the selection of in-sample residuals from the
estimated ARIMA-models as a proxy for the unexpected shocks is that the information is
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considered to be at least partly rational as the residuals have a zero mean value and observed to be
serially uncorrelated. The forecast is based on the past values of the series itself.
All variables except interest rates are transformed into the natural logarithmic. The first
step in the estimation of ARIMA model is to check the stationarity of data. The stochastic process
is assumed to be stationary if its mean and variance remain unchanged over time and covariance
depend on the lags between the time periods. In order to check stationarity of exchange rates,
industrial production index, Interest rates and money supply, correlogram of ACF and PACF and
unit root test (Augmented Dickey-Fuller) unit root test is applied. Unit root test is used to detect
the order of integration. At level, all series are found to be non-stationary, whereas, with a first
difference, the null hypothesis of unit root test is rejected. It means that all series are integrated to
order 1.
To reach the Parsimony each series is checked with different p and q orders. The residuals
obtained from these structures are further used to check the impact of macroeconomic shocks on
herding behavior and contagion of herding. Panel A of Table 4.14 presents results for the
developed markets. All series are integrated to order one and p and q orders vary significantly from
variable to variable. The ARIMA structures, with Schwartz Bayesian Criteria (SBC), along with
their R2 value is given. Highest p and q orders are observed in case of money supply in developed
markets. It is observed that in developed markets Interest rate appear to exhibit highest R-value
among all, whereas exchange rates exhibit lowest value R2 among other variables. In general, the
explanatory value of ARIMA residuals varies among countries.
Table 4.14: The structures of estimated ARIMA models with the order (p,d,q) of all
macroeconomic variables. Panel A: Developed Market
Exchange Rates Industrial Production Index Interest Rates Money Supply
151
ARIMA SBC R2 ARIMA SBC R2 ARIMA SBC R2 ARIMA SBC R2
Australia (1,1,2) -3.61 0.16 (2,1,1) 1.45 0.74 (4,1,2) -5.09 0.14 (5,1,2) 4.04 0.07
Austria (1,1,2) 0.35 0.12 (1,1,2) 3.66 0.07 (3,1,2) -6.50 0.22 (2,1,2) 2.92 0.09
Belgium (1,1,2) 2.50 0.12 (2,1,3) 5.07 0.34 (3,1,1) -1.44 0.88 (4,1,5) 3.01 0.17
Denmark (3,1,1) -0.86 0.97 (3,1,3) 6.62 0.52 (2,1,2) -5.59 0.12 (2,1,3) 4.14 0.55
France (2,1,4) -0.84 0.09 (2,1,1) 3.44 0.08 (3,1,2) -6.47 0.55 (5,1,2) 3.15 0.33
Germany (3,1,2) -3.63 0.14 (2,1,2) 3.66 0.10 (1,1,1) -0.95 0.32 (4,1,3) 3.98 0.29
Greece (3,1,2) 6.80 0.14 (3,1,0) 4.95 0.37 (4,1,2) -1.84 0.27 (2,1,3) 4.00 0.55
Hong Kong (3,1,1) 2.35 0.30 (4,1,4) 5.10 0.19 (3,1,3) -3.77 0.10 (2,1,2) 3.93 0.05
Italy (1,1,2) 10.24 0.12 (2,1,3) 5.55 0.47 (3,1,2) -6.47 0.56 (2,1,4) 3.62 0.30
Japan (2,1,4) 4.73 0.1 (1,1,0) 4.59 0.03 (2,1,2) -9.09 0.12 (4,1,2) 1.02 0.42
Netherland (3,1,2) -3.28 0.14 (1,1,1) 4.50 0.10 (5,1,4) -5.26 0.27 (3,1,3) 3.75 0.10
New Zealand (3,1,2) -2.52 0.07 (4,1,4) 3.89 0.12 (3,1,2) -4.70 0.20 (2,1,2) 4.40 0.08
Norway (2,1,2) -0.07 0.10 (3,1,3) 4.94 0.28 (2,1,4) -4.72 0.21 (5,1,5) 3.10 0.76
Portugal (5,1,4) 6.16 0.19 (3,1,3) 6.39 0.73 (2,1,0) -4.12 0.24 (3,1,3) 3.18 0.09
Singapore (2,1,3) 2.82 0.45 (2,1,3) 7.18 0.47 (4,1,5) -4.48 0.189 (1,1,1) 2.77 0.087
Spain (3,1,2) 5.37 0.14 (2,1,2) 3.53 0.12 (2,1,2) -5.080 0.07 (2,1,3) 3.27 0.22
Sweden (2,1,2) 0.19 0.08 (3,1,2) 4.64 0.16 (3,1,2) -5.98 0.4 (2,1,4) 3.41 0.30
Switzerland (1,1,2) -4.12 0.06 (2,1,5) 5.22 0.50 (1,1,1) -5.40 0.07 (1,1,2) 3.11 0.35
UK (2,1,1) -3.83 0.11 (1,1,2) 2.79 0.06 (1,1,2) -5.60 0.13 (3,1,3) 0.89 0.22
US (2,1,1) -3.83 0.10 (2,1,2) 2.04 0.30 (1,1,2) -6.28 0.53 (2,1,3) 1.01 0.12
Panel B: Emerging and Frontier Market
Exchange Rates Industrial Production Index Interest Rates Money Supply
ARIMA SBC R2 ARIMA SBC R2 ARIMA SBC R2 ARIMA SBC R2
China (2,1,2) 0.88 0.29 (3,1,0) 4.67 0.33 (3,1,2) -3.32 0.09 (2,1,2) 2.87 0.28
India (2,1,3) 2.56 0.18 (4,1,5) 5.50 0.64 (2,1,0) -3.38 0.05 (3,1,4) 3.95 0.27
Indonesia (3,1,3) 5.52 0.10 (2,1,2) 6.13 0.75 (1,1,2) -0.32 0.37 (1,1,1) 3.68 0.03
Korea (3,1,3) 3.45 0.38 (1,1,1) 3.12 0.16 (3,1,2) -5.04 0.22 (2,1,2) -4.51 0.07
Malaysia (3,1,3) -2.65 0.12 (2,1,0) 5.25 0.28 (3,1,2) -7.42 0.13 (2,1,2) 2.98 0.12
Pakistan (3,1,2) 3.06 0.10 (3,1,2) 3.06 0.10 (4,1,4) -2.21 0.41 (4,1,4) -2.21 0.41
152
Philippines (3,1,3) 2.58 0.18 (3,1,3) 6.58 0.23 (2,1,2) -3.01 0.23 (2,1,2) 4.58 0.22
Sri Lanka (4,1,4) 3.97 0.13 (2,1,1) 6.86 0.17 (1,1,3) -2.80 0.14 (2,1,2) 2.48 0.08
Taiwan (3,1,2) 0.83 0.29 (2,1,3) 4.75 0.15 (5,1,2) -2.91 0.2 (4,1,3) 1.80 0.23
Thailand (3,1,3) 2.01 0.14 (3,1,3) 6.74 0.37 (1,1,2) -4.54 0.17 (5,1,5) 4.64 0.76
Turkey (3,1,2) -2.44 0.08 (3,1,3) 6.56 0.39 (1,1,1) 4.73 0.47 (4,1,5) 5.80 0.13
Panel B of the table 4.14 present results for the emerging and frontier markets. All series are
integrated to order 1 and the p and q orders vary significantly. The ARIMA structures, with
Schwartz Bayesian Criteria (SBC), along with their R2 value is given. Like Developed markets,
Money supply exhibit highest AR and MA orders. It is observed that Industrial production index
exhibit highest R2 value among all, whereas exchange rate has the lowest R2 value in almost all
countries.
Table 4.15 reports the results of the effect of macroeconomic shocks on herding. The
estimated residuals from ARIMA models are included as an explanatory variable in the Hwang &
Salmon (2004) model. The findings show that in most of the countries Macroeconomic shocks
have an insignificant effect on investors herding behavior but in few cases, one or two of the four
variables have a significant effect on herding behavior of an investor. In some cases, the inclusion
of Macroeconomic shocks affects the intensity of herding behavior that can be observed through
the changed value of herding Coefficient.
Table 4.15 presents the results of the effect of macroeconomic shocks on investor herding
behavior. Panel A reports the results of developed markets. In general, it is concluded that none of
these shocks except few have a significant effect on the imitating behavior of investor. According
to Hwang & Salmon (2004) if the coefficient Hmt becomes insignificant then change in the
dispersion is due to macroeconomic fundamental not due to Herding. These findings are similar to
153
Hwang & Salmon (2004) except few almost all macroeconomic shocks exhibit an insignificant
effect on investor herding behavior as the coefficient Hmt remains significant. In some countries,
it can be observed that exchange rate shocks(Austria and Portugal), Interest rate shocks(France)
and money supply shocks (Japan) have a significant effect on the return dispersion and in the
presence of these shocks the coefficient m is significant and high and nm is different from zero.
These findings are in line with Messis & Zapranis (2014) they examine four series of
macroeconomic shock and observe similar results.
From the table 4.15, it can be observed that Exchange rates shock significantly affect the
herding behavior in the Austrian market. The decrease in the dispersion can be interpreted into the
less correlated behavior of investors in the market and results in decrease herding behavior towards
market portfolio. These findings are similar to Gang & Dai (2017), they find a significant effect of
CNY depreciation on herding intensity. The Austrian economy in 1995 joined the European Union
and adopted Euro as the single currency in 1999. Euro as a single currency among EU can be
considered equivalent to an irrevocably fixed exchange rate that can be controlled through
adjustments in the European Union internally. The most recent crisis like US subprime, euro as a
common currency insulated the Euro area from the dollar depreciation and financial panic. But the
2010 Greek and Irish crisis and more specifically Euro debt crisis destabilize the ambitious
dominance of European integration process. Especially, the Austrian economy depends upon
international Sovereign debts as an external Financing, large unemployment and inflation result in
decrease investor confidence on unanimous currency. The decrease in Euro value over time can
be attributed to lesser consumer confidence and more correlated behavior.
Portugal market also exhibits similar results, there exists a negative relationship between
exchange rate shocks and cross-sectional standard deviation of portfolio betas. These results are
154
supported as Cho, Choi, Kim, & Kim (2016) discuss that the investor reaction to change in
currency value is different in developed markets than in emerging markets, the flow of capital from
the market or in the market depends upon the Global market conditions and investors respond
accordingly. When there is a downward trend in the global market, emerging market investors
shift their funds to the developed markets, which results in a negative correlation of currency and
stock returns in developed, whereas the positive correlation between stock returns and currency
value in emerging markets. Same correlation is observed when global markets are in boom.
Therefore, the results are in line with the above-stated argument as the markets faced recession
there exists a negative relation between, stock return and currency value.
With increased trade and international diversification and investment, the role of interest
rates and exchange rates is getting more attention gradually Tsai (2012). The interest rate shock is
found to have a positive impact on the standard deviation of portfolio beta in the French market.
It implies that a positive shock in interest rates increase the magnitude of herding in the French
market. These findings are similar to Gang & Dai (2017), they find a significant effect of an
increase in interest rate on Chinese investor herding behavior. These findings are in line with Lee,
Liao, & Hsu (2015) and in contradiction with Philippas et al. (2013); Messis & Zapranis (2014).
This study reports the significant negative effect of money supply on the standard
dispersion of portfolio betas in the Japanese market. It is observed that the Herding coefficient m
is still positive and near to one and mn is different from zero confirming the presence of herding.
Whereas decreased dispersion leads to a reduction in herding intensity. According to Gong & Dai
(2017), the contractionary monetary policy means a decrease in reserve ratio has a significant
negative effect on the investor herding behavior. This study finds similar results in case of Japan.
Money supply is considered as the monetary policy coefficient (Keran, 1971). According to
155
Maskay (2007), the change in money supply or the monetary policy are the most useful tools to
control the actual economic activity used by the central bank of the respective country. In literature
several authors like as Gupta (1974), Maskay (2007) consider money supply an important factor
in determining the stock prices. Monetary policy has a significant effect on the development and
behavior of stock prices. Money supply has a direct effect on the stock prices as the excess
availability of funds and reduced interest rates are linked with cheaper external financing both of
the factors lead to increased investment and consumption that can be translated into growth in
demand of shares (Ioannidis & Kontonikas, 2006).
In rest of the developed markets none of the macroeconomic shocks have significant effect
and herding remains prevalent in all markets, therefore it can be concluded that macroeconomic
shocks do not have a significant effect on the imitating behavior of an investor.
Panel B of the table 4.15 represent the results of the emerging and frontier markets, it can be
observed that exchange rate shocks exhibit a negative significant relationship with cross-sectional
standard deviation of portfolio betas in Chinese, Pakistani and Malaysian markets. Whereas there
exists a significant negative relationship of industrial production index on the cross-sectional
standard deviation of portfolio betas in Sri Lankan market and Thailand market exhibit negative
relation of interest rates and the positive effect of money supply shocks. This study shows that
with the introduction of macroeconomic shocks herding becomes insignificant in the Malaysian
and Taiwanese market. Rest of the markets exhibit similar results and no effect of macroeconomic
shocks is detected.
The effect of macroeconomic shocks on stock return dispersion can be due to several
reasons. One reason can be the implementation of economic reforms in recent years that include
financial liberalization in order to attract foreign capital and economic growth (Hajilee & Al
156
Nasser, 2014) Second reason can be the shift of fixed exchange rates to managed-floating and free-
floating exchange rates which has increased the exposure of exchange rate volatilities. This
volatility can be translated into stock price volatility that has a negative or positive effect on
investor holdings (Chen & Diaz, 2012).
Ibrahim & Aziz (2003) investigate the dynamic relationship and negative correlation in different
exchange rates and stock prices in Malaysian market during a period 1977-1998. Abdalla &
Murinde (1997) investigate Pakistan, India, Korea, and the Philippines market and find co-
integration between stock prices and exchange rates and find unidirectional causality from
exchange rates during 1985 to 1994. Similarly, Lin (2012) investigate co-movement of stock
prices and exchange rates in different emerging Asian markets. They conclude that the emerging
markets have an advantage of local currency appreciation due to the flow of capital funds into the
markets and movement is not strong in export-oriented industries. Demirer et al. (2014) investigate
exchange rate as a plausible reason for investor herding behavior in the Korean market. This study
finds a significant effect of exchange rate shock on Chinese stock return dispersion, these findings
are similar to Gong & Dai (2017), they found that the depreciation in stock returns leads to more
correlated behavior i.e., Herding behavior.
The results indicate that in Pakistani stock market exchange rates shocks have a significant
negative effect on stock return dispersion, these findings are in contradiction with Javaira &
Hassan (2015). They find the insignificant effect of exchange rates on investor herding behavior
in Pakistani market during a period from 2002-2005. Malaysian market exhibit similar behavior.
These findings confirm the findings of Ibrahim & Aziz (2003) as they investigate the dynamic
relationship and negative correlation in different exchange rates and stock prices in Malaysian
market during a period 1977-1998. Malaysian market exhibits unique results as the herding
157
coefficient become insignificant with the inclusion of macroeconomic shocks. According to
Hwang & Salmon (2004) significant macroeconomic announcement and insignificant herding
parameter, indicating that the cross-sectional dispersion in betas is due to macroeconomic shock
not due to herding behavior. This effect can be easily observed in the stock market of Malaysia, a
reduction in significance level is observed but the value of coefficient plus the noise term is still
different from zero.
Another unique result is realized in case of Sri Lankan market, the Herding behavior is
absent in Sri Lankan market before the inclusion of macroeconomic shock. After the inclusion of
macroeconomic shocks the Industrial production shock display positive significant results and the
Herding coefficient also become significant. It means that the shock in industrial production leads
to more correlated behavior of investors in Sri Lankan market and results in herd formation.
According to results Thailand market exhibit negative relation of interest rates and the
positive effect of money supply shocks. Brahmasrene & Jiranyakul (2007) found a positive effect
of money supply and stock prices in Thai stock market. Results of Thai market are interesting in
aspects that it is the only market where the significance of two macroeconomic factors is observed.
Monetary policy is devised to control inflation in a market, this outcome is a collective action of
money supply and interest rate changes (Asghapur et al., 2014). According to Loisel, Pommeret,
& Portier, (2012), the tightening of monetary policy make entrepreneur inclined to borrow, the
investment in new technology increases and if the expectation about productivity is positive. It
results in decreased herd formation and fragility in the stock market also declines.
The findings show that in most of the countries Macroeconomic shocks have an insignificant effect
on investors herding behavior but in few cases, one or two of the four variables have a significant
effect on herding behavior of an investor. In some cases, inclusion of Macroeconomic shocks
158
affects the intensity of herding behavior that can be observed through the changed value of herding
Coefficient. These findings are consistent with the asset pricing model in the presence of sound
fundamentals investors behavior is rational, however unexpected shocks in the economy give rise
to uncertain situations which could be triggered by the social influences.
159
Table 4.15: Impact of Macroeconomic shocks on Herding Panel A: Developed Markets
Country ER IP IR MS
Australia -0.493(0.048)** 0.130(0.121)** 0.974(0.023)** 0.017(0.864)** -0.182(0.336) 0.003(0.029) 0.415(0.967) -0.012(0.010)
Austria -0.642(0.092)** 0.192(0.118)** 0.980(0.020)** 0.027(0.748)** -0.127(0.057)** -0.006(0.012) -0.036(1.922) 0.010(0.015)
Belgium -0.405(0.021)** 0.103(0.111)** 0.904(0.068)** 0.023(0.886)** -0.004(0.011) 0.000(0.001) 0.021(0.071) 0.013(0.007)
Denmark -0.302(0.022)** 0.112(0.217)** 0.822(0.113)** 0.051(0.705)** 0.076(0.071) 0.000(0.002) 0.686(0.654) -0.004(0.005)
France -0.603(0.080)** 0.115(0.140)** 0.970(0.023)** 0.042(0.435)** -0.123(0.077) 0.005(0.007) 0.268(0.121)* 0.006(0.008)
Germany -0.590(0.048)** 0.022(1.001)** 0.901(0.034)** 0.074(0.183)** -0.012(0.151) 0.002(0.004) 0.023(0.033) 0.001(0.003)
Greece -0.637(0.064)** 0.206(0.111)** 0.940(0.048)** 0.044(0.895)** 0.002(0.003) 0.001(0.005) -0.129(0.386) 0.012(0.013)
Hong Kong -0.442(0.074)** 0.117(0.128)** 0.979(0.020)** 0.021(0.703)** -0.002(0.012) -0.002(0.003) 0.220(0.304) 0.010(0.007)
Italy -0.521(0.025)** 0.115(0.117)** 0.868(0.084)** 0.038(0.803)** 0.000(0.000) -0.001(0.003) 0.202(1.412) -0.005(0.008)
Japan -0.699(0.010)** 0.000(1.89E+07) 0.661(0.083)** 0.039(0.221)** 0.001(0.001) -0.001(0.001) -0.416(0.955) -0.010(0.006)*
Netherland -0.650(0.054)** 0.183(0.131)** 0.934(0.043)** 0.051(0.633)** -0.332(0.382) 0.008(0.007) 0.187(1.612) -0.003(0.012)
New Zealand -0.462(0.036)** 0.170(0.104)** 0.954(0.043)** 0.021(0.961)** 0.043(0.222) -0.013(0.010) -0.089(0.734) 0.004(0.008)
Norway 0.008(0.075)** 0.116(0.094)** 0.989(0.012)** 0.016(0.687)** -0.077(0.051) -0.002(0.004) -0.132(0.496) 0.007(0.010)
Portugal -0.453(0.037)** 0.211(0.115)** 0.914(0.111)** 0.034(1.577)** -0.008(0.003)** -0.002(0.004) 0.168(0.904) 0.000(0.016)
Singapore -0.584(0.032)** 0.169(0.128)** 0.913(0.072)** 0.035(0.944)** 0.018(0.051) -0.002(0.002) -0.796(0.609) -0.028(0.049)
Spain -0.486(0.062)** 0.090(0.118)** 0.975(0.018)** 0.029(0.448)** -0.002(0.002) 0.002(0.006) -0.511(0.372) 0.004(0.007)
Sweden -0.511(0.139)** 0.116(0.136)** 0.983(0.021)** 0.038(0.507)** -0.028(0.040) 0.005(0.005) 0.335(1.045) -0.004(0.009)
Switzerland -0.438(0.027)** 0.097(0.120)** 0.944(0.042)** 0.021(0.772)** -0.109(0.304) 0.001(0.003) 0.469(0.594) 0.004(0.009)
UK -0.429(0.078)** 0.120(0.135)** 0.965(0.026)** 0.040(0.479)** 0.194(0.332) -0.007(0.010) 0.478(0.669) 0.003(0.029)
160
US -0.564(0.063)** 0.111(0.158)** 0.948(0.032)** 0.049(0.414)** 0.144(0.279) 0.020(0.015) -0.305(0.889) -0.042(0.031)
Panel B: Emerging and Frontier Markets
Country ER IP IR MS
China -0.660(0.027)** 0.125(0.190)** 0.746(0.117)** 0.082(0.580)** -0.064(0.036)* -0.010(0.007) 0.337(0.387) 0.011(0.014)
India -0.486(0.039)** 0.101(0.176)** 0.904(0.050)** 0.051(0.468)** -0.020(0.014) 0.001(0.003) -0.302(0.200) -0.003(0.005)
Indonesia -0.498(0.040)** 0.191(0.150)** 0.838(0.084)** 0.078(0.568)** 0.000(0.005) -0.006(0.004) 0.047(0.119) -0.011(0.010)
Korea -0.413(0.032)** 0.125(0.106)** 0.962(0.035)** 0.016(0.917)** 0.005(0.011) 0.002(0.006) 0.162(0.711) -0.001(0.004)
Malaysia -0.325(0.017)** 0.070(0.891)** 0.512(0.165) 0.093(0.596) -0.207(0.116)* -0.003(0.004) -0.854(1.598) -0.009(0.011)
Pakistan -0.386(0.051)** 0.168(0.081)** 0.949(0.052)** 0.026(0.870)** 0.032(0.011)** -0.001(0.002) 0.283(0.255) 0.007(0.018)
Philippines -0.644(0.017)** 0.220(0.119)** -0.921(0.542)* 0.009(10.483) -0.018(0.023) -0.003(0.003) -0.551(0.369) -0.003(0.007)
Sri Lanka -0.476(0.028)** 0.139(1.454)** 0.423(0.241)* 0.198(0.826)** 0.007(0.014) -0.005(0.003)* 0.122(0.410) 0.030(0.024)
Taiwan -0.452(0.010)** 0.107(0.123)** 0.931(26.857) 0.000(636.219) -0.017(0.026) 0.005(0.004) 0.057(0.221) 0.007(0.018)
Thailand -0.406(0.014)** 0.103(0.292)** 0.665(0.228)** 0.050(1.128)** 0.004(0.017) 0.001(0.002) -0.845(0.411)** 0.010(0.004)**
Turkey -0.419(0.006)** 0.075(0.137)** 0.913(0.157)** 0.007(2.563)** -0.085(0.105) 0.000(0.001) 0.000(0.003) -0.001(0.003)
***indicate statistical significance at 1%. **Indicate statistical significance at 5%. *Indicate statistical significance at 10%.
161
Contagion of Herding
Figure 4.33 and 4.34 present the results of conditional correlation between the herding measure of
US and rest of the developed countries. It is observed that this correlation coefficient increases
during turbulent period. Specifically this increase is more observable in the third quarter of 2007
and peaked in the first quarter of 2008. All markets do not exhibit variation in herding measure
-1.2
-0.8
-0.4
0.0
0.4
0.8
2005 2006 2007 2008 2009 2010
AUSTRALIA AUSTRIA
BELGIUM DENMARK
FRANCE GERMANY
GREECE HONGKONG
ITALY JAPAN
NETHERLAND NEWZEALAND
NORWAY PORTUGALSINGAPORE SPAIN
SWEDEN SWITZERLAND
UK
Figure 4-33: Correlation between US and other developed markets herding measures
-1.2
-0.8
-0.4
0.0
0.4
0.8
1.2
2005 2006 2007 2008 2009 2010
CHINA INDIA
INDONESIA KOREA
MALAYSIA PAKISTAN
PHILPINES SRILANKA
TAIWAN THAILAND
TURKEY
Figure 4-34: Conditional Correlation Graph between US and other emerging and Frontier Markets
with US market. In both the Graphs it is noted that this peak time period for countries is arbitrary
around 2007 to 2009. In general this correlation increased with the exception of few countries.
162
This study explains the transmission of contagion of herding through two channels, one is the crisis
shocks and the other is the macroeconomic shocks. Both channels are explained below.
Impact of Crisis Dummy on Cross Correlation of Herding Behavior
Panel A of Table 4.16 reports the results of effect of crisis shock on correlation of herding
measure between US and other developed markets. To test the hypothesis of increased correlation
in the presence of crisis, this study conducts OLS estimations. In general this study observe that,
the crisis dummy is observed to have significant impact on this increased correlation in those
economy that are particularly closely interacted with US economy through certain strategic ties.
These results validate the objective of this study that contagion of herding exists during crisis
situation. It is observed that correlation coefficient of US herding measure increased during period
of crisis period in markets of Denmark, France, Germany, Italy, Japan, whereas as Greek and
Swiss markets exhibit opposite results. During the crisis period the correlation coefficient instead
of increasing decreases. This study observes significant decrease in correlation during global
financial crisis by a value of 1% and 0.6% in Greece and Swiss markets respectively. This effect
can be related to the strong financial system in Swiss market. Banks have huge deposits to draw
on and are less dependent on short-term money markets, the freezing-up of which has brought so
many to the brink of collapse. In addition to that Swiss banks got their monetary response right
from the start, they have less need for a fiscal stimulus than other countries.
The correlation of herding measure between US and Denmark increases by 11.7% due to
global financial crisis, similarly French and US herding coefficient correlation increases by 2.4%
during crisis followed by 7.6% in Italian market and 4.3% in Japanese market. These findings are
similar to Ramchand & Susmel (1997). They analyze the relationship between correlation and
163
variance of US market with other markets and conclude that during a high volatility state of US
market this correlation increases on average by 2 to 3.5 times. All these results are at least
statistically significant at 10% significance level. These findings can be related to stock market
integration. Markets which are highly integrated exhibit more correlated behavior in turbulent
periods like crisis (Chiang et al., 2007). Lin et al. (1994) investigate the correlation of volatility
measures between Japan and US markets and find the global impact of market returns on each
other. Rest of the markets exhibit insignificant effect on herding measure during turbulent period.
Hyde et al. (2007) examine the co-movement of returns between Asia-Pacific, US and European
markets and conclude that Germany, UK and Japan markets change in time and vary across
frequencies. Similar findings are observed by Rua & Nunes (2009) in US, UK Germany and
Japanese markets. Chiang et al. (2007) relate correlated behavior of all markets after crisis to
herding behavior. According to McMillan & Speight (2010) the behavior of market investors give
rise to volatility spillover that has a subsequent contagion effects. This effect can be attributed to
speculation markets due to movement in one market. Thus, contagion of herding emerges due to
information transmission.
Panel B of table 4.16 reports the results of crisis dummy on conditional correlation of
herding measure between US and rest of the emerging and developed markets. It is observed that
Chinese Korean, and Malaysian measure of herding correlation decreases by 2.23%, 1.45% and
2.1% in all three markets respectively that can be spotted by the significant negative values. The
Indian, Pakistani, and Philippines market exhibit increase in correlation of herding measure during
global financial crisis by a value of 3.9% in India 2.1% in Pakistan, and 4.5% in Philippines. Messis
& Zapranis (2014) find significant effect of global financial crisis on conditional correlation
164
between Chinese and US stock market. Similarly, Graham et al. (2012) observe that integration of
the US and emerging markets over time.
Table 4.16: Tests of changes in correlations between herding towards market on behalf of
unexpected variations in macroeconomic variables in US countries. Country ki k Dcr
Der Dip Dir Dms R2
Australia
-0.228***
(-6.919)
0.234***
(3.697)
-0.027
(-0.460)
-0.084*
(-1.770)
0.126*
(1.672)
-0.076*
(-1.609)
0.004
(0.069)
0.09
Austria
0.019
(1.633)
0.841**
(22.350)
-0.017
(-0.622)
0.012
(0.472)
-0.057**
(-2.228)
0.028**
(1.979)
-0.019
(-0.952)
0.75
Belgium
0.050**
(4.555)
0.792**
(16.953)
-0.001
(-0.136)
-0.010
(-1.119)
0.010
(1.474)
0.002
(0.248)
0.007
(0.857)
0.60
Denmark
0.016
(0.940)
0.143**
(2.273)
-0.032
(-0.993)
0.030
(0.805)
0.035
(0.728)
0.108***
(3.514)
-0.043
(-1.071)
0.07
France
0.198***
(8.894)
0.276***
(4.896)
0.117***
(3.393)
-0.050
(-1.078)
0.044
(0.844)
0.107**
(2.004)
0.034
(0.617)
0.16
Germany
0.003
(0.656)
0.962***
(61.884)
0.024**
(2.283)
0.002
(0.154)
-0.001
(-0.123)
-0.013
(-1.386)
0.005
(0.328)
0.94
Greece
-0.014***
(-4.879)
0.441***
(4.289)
-0.010*
(-1.664)
0.002
(0.670)
-0.001
(-0.227)
-0.011
(-1.499)
-0.001
(-0.232)
0.275
Hong Kong
0.005
(0.606)
0.712***
(14.060)
0.008
(0.368)
0.017
(0.747)
0.019
(0.865)
0.003
(0.110)
0.048**
(2.239)
0.563
Italy
0.266***
(10.360)
-0.255***
(-3.178)
0.076*
(1.722)
0.036
(0.854)
0.085
(1.555)
-0.019
(-0.319)
-0.114**
(-1.966)
0.13
Japan
-0.039**
(-2.506)
0.203***
(2.968)
0.043*
(1.765)
-0.053
(-1.528)
0.012
(0.288)
-0.055
(-1.563)
-0.012
(-0.424)
0.07
Netherland
0.007*
(2.606)
0.779***
(21.946)
-0.004
(-0.727)
-0.002
(-0.524)
0.010**
(2.111)
0.003
(0.802)
0.007
(1.889)
0.67
New Zealand 0.005 0.857*** -0.016 -0.002 0.006 0.015* 0.020 0.83
165
(0.907) (27.718) (-1.201) (-0.335) (0.498) (1.713) (1.353)
Norway
-0.009
(-0.734)
0.832***
(15.443)
0.005
(0.286)
-0.031
(-1.124)
0.042
(1.391)
0.008
(0.245)
0.031
(1.146)
0.72
Portugal
-0.002
(-0.209)
0.812***
(9.693)
-0.011
(-0.827)
-0.007
(-0.399)
0.024*
(1.769)
-0.018
(-1.521)
0.000
(0.026)
0.75
Singapore
0.056***
(4.779)
0.733***
(14.361)
0.006
(0.427)
0.016
(1.109)
0.003
(0.239)
-0.011
(-0.654)
-0.004
(-0.200)
0.67
Spain
0.039***
(3.664)
0.809***
(16.711)
0.015
(0.410)
0.021
(0.921)
-0.048*
(-1.692)
0.014
(0.502)
-0.006
(-0.312)
0.70
Sweden
0.018***
(4.192)
0.859***
(40.043)
-0.002
(-0.513)
-0.001
(-0.244)
-0.003
(-0.614)
0.001
(0.180)
-0.008
(-1.154)
0.91
Switzerland
0.008*
(1.864)
0.854***
(16.149)
-0.006**
(-2.121)
-0.004
(-1.080)
0.002
(0.545)
-0.005
(-0.618)
0.010
(1.069)
0.89
UK
0.018***
(4.192)
0.859***
(40.043)
-0.002
(-0.513)
-0.001
(-0.244)
-0.003
(-0.614)
0.001
(0.180)
-0.008
(-1.154)
0.91
Panel B: Emerging and Frontier Markets
Country ki k Dcr Der Dip Dir Dms R2
China
0.161***
(5.185)
0.315***
(4.447)
-0.223**
(-2.402)
0.031
(0.580)
0.126***
(2.955)
-0.015
(-0.331)
-0.022
(-0.441)
0.18
India
0.009
(1.053)
0.907***
(28.699)
0.039*
(1.844)
0.006
(0.485)
0.029
(1.164)
-0.005
(-0.169)
-0.028
(-1.098)
0.856
Indonesia
-0.013
(-1.115)
0.662***
(14.585)
0.029
(0.898)
-0.022
(-0.844)
-0.025
(-0.983)
-0.005
(-0.173)
0.000
(-0.022)
0.47
Korea
-0.040
(-1.365)
0.347***
(6.152)
-0.145**
(-2.388)
0.006
(0.092)
0.017
(0.233)
-0.036
(-0.597)
-0.044
(-0.527)
0.14
Malaysia
0.029***
(0.007)
0.867***
(0.024)
-0.012*
(0.007)
0.009
(0.009)
-0.003
(0.007)
-0.002
(0.008)
0.004
(0.011)
0.86
166
Pakistan
0.011**
(2.031)
0.689***
(16.645)
0.021*
(1.670)
-0.036**
(-2.320)
0.004
(0.293)
0.010
(0.814)
-0.020
(-1.527)
0.61
Philippines
0.072***
(5.822)
0.390***
(6.070)
0.045*
(1.729)
-0.004
(-0.110)
-0.036
(-0.719)
0.033
(1.339)
-0.018
(-0.654)
0.18
Sri Lanka
-0.008
(-0.909)
0.914***
(28.135)
-0.016
(-0.503)
0.031
(1.060)
0.016
(0.860)
0.021
(1.310)
-0.008
(-0.367)
0.84
Thailand
0.039***
(3.664)
0.809***
(16.711)
0.015
(0.410)
0.021
(0.921)
-0.048*
(-1.692)
0.014
(0.502)
-0.006
(-0.312)
0.70
Turkey
-0.008
(-0.909)
0.914***
(28.135)
-0.016
(-0.503)
0.031
(1.060)
0.016
(0.860)
0.021
(1.310)
-0.008
(-0.367)
0.84
***indicate statistical significance at 1%. **Indicate statistical significance at 5%. *Indicate statistical significance at 10%.
Impact of Macroeconomic Shocks on Cross-Correlation of herding behavior
This regression analysis aims to investigate the impact of macroeconomic shock in one of
the developing, emerging and frontier markets on the pairwise correlation coefficient of herding
measures. Minimum acceptance criteria is selected at 10% in line with the herding literature.
Chiang et al. (2007) argued that public news in one market can be regarded as information for the
rest of the markets in a region due to herding behavior. Therefore, if the markets are highly
integrated with each other the effect of herding contagion can be more intense in magnitude. In
this section of the study contagion of herding due to macroeconomic shock is discussed.
Panel A of the table 4.16 presents the results of macroeconomic shocks on correlated
behavior of developed markets with US. It is observed that only in Australian market exchange
rate shock have significant negative effect of 8.4% on investors herding behavior. It is generally
believed that if investors are uncertain about the fluctuations in exchange rates, they normally sell
the currency that could lead to a crisis situation not driven by the fundamental value (Obstfeld,
1986). Imperfections in the market can also imbalance the fundamental values (McKinnon & Pill,
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1997). Cho et al. (2016) discuss that the reaction of developed and emerging market is different to
the changes in exchange rates along with different market conditions. When there is downturn in
global market capital flow is directed towards developed markets from the developing markets,
that generate a positive correlation of stock returns and exchange rates in emerging markets,
however negative effect on the developed markets. Markets follow same trend in up markets.
The effect of interest rate shock is more obvious in most of the markets, like Australia,
Austria, Denmark, France and New Zealand. Except Australian market, it is evident that all
markets exhibit positive interest rate shocks on correlation between herding measures. The interest
rate shock is linked to the financial markets. Frankel & Rose (1996) examine the role of interest
rate in determining the financial crisis. Klein (2013) investigates the significant impact of short
term rates, term spread and first difference of on the dispersion of stock returns during spillovers
from US market to other markets. During spillover they find significant effect of interest rates,
however without spillover these variables reduce the dispersion of returns. It means that in the
time of volatility investors combine the macroeconomic information with their risk perception and
react accordingly. This view can be justified by the “Wake up call hypothesis”. According to this
hypothesis, in the event of crisis the investors in other markets reevaluate their investment risk
with similar market fundamentals. Due to the fear of facing similar conditions they overreact and
spread instability that has a contagion effect spread in other markets. This effect can be related to
the evidence of positive significant coefficient of interest rates in this study.
In case of money supply this study observes that in almost all markets, money supply shock
has insignificant effect on herding behavior. This money supply shock can be translated in to the
monetary policy shock as discussed previously in section 4.6. Only Hong Kong markets exhibit
significant effect of herding correlation with opposite signs at 5% significance level. It is argued
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that contractionary monetary policy shock increases the herding behavior in an market, while
expansionary policy has displayed anti herding behavior of an investor (Gong & Dai, 2017).
Similarly, effect of industrial production shocks are also observed on herding correlation between
the US and respective countries in the sample, Australian, Netherland and Portuguese markets
exhibit the positive effect of industrial production shocks on herding correlation.
Panel B provides the results of macro-economic shocks on the correlation of US and other
emerging and frontier markets macroeconomic shocks. It is observed that except few most of the
macro economic shocks in almost all markets have no impact on the correlation of herding
measures of certain markets. Several authors argued that contagion in herding is principally caused
by the behavioral factors and that are independent of the economic fundamentals (Khan & Park,
2009)
It is observed that correlation of herding measure between US, Pakistan and US, Taiwan
is affected by the shock in exchange rates against US dollar. Lin (2012) investigates the co-
movement of stock returns and exchange rates in a set of emerging market industries. They observe
weak correlation in export oriented societies and find the contribution of capital account flows as
a channel between exchange rate fluctuation and stock returns. Generally depreciation in local
currency is perceived as a “bad news” in emerging markets and benefit is taken by currency
appreciation due to capital inflow in these markets as discussed by Cho et al. (2016).
In Chinese and Thai markets herding correlation with US is affected by the shocks in
industrial production. It is observed that Chinese shock impact have positive effect on correlation
in herding measure. In the specific time period Chinese market faced a decline in industrial
production. Whereas shock in Thai market has negative effect on correlation of herding measure.
Bekaert, et al. (2014) finds significant effect of US market contagion on manufacturing and
169
production sector. They argued that usually external crisis has minimal effect on investor behavior,
instead local investors usually focused on domestic factors and react adversely due to poor
macroeconomic fundamentals, and weak regulatory structures.
In general it is observed that more integrated markets exhibit more intense effect of herding
contagion. The volatility spillovers in one market can generates contagion effect in other markets
due to investor behavior, this behavioral impact generate speculations that have impact across the
markets. In general certain macroeconomic shocks specifically in developed markets exhibit
significant effect on herding correlation, these results are consistent with the findings of Mylonidis
& Kollias (2010). The results of the study indicate that correlation between herding measure can
provide an incentive of portfolio diversification to investors. The significance of certain
macroeconomic shocks validate the hypothesis that Macroeconomic uncertainty and crisis shocks
have significant effect on herding behavior, but this effect is observed in a limited set of economies.
These findings can be related to the wake-up call hypothesis where similarities are observed in
two markets if any crisis event or a specific shock hits the market (Chiang et al., 2007). These
findings meet the objectives that not only the turbulent situation but also the unexpected
components of macroeconomic information has significant effect on the inefficient behavior of
market participants in stock markets.
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Chapter 5
5. Conclusion
Summary and Findings
The basic purpose of this research is to investigate herding behavior, the role of culture and
macroeconomic shocks on herding behavior, and the existence of contagion of herding due to the
crisis and macroeconomic shocks in the market. This study investigates herding behavior towards
market consensus in a set of Developed, Emerging and Frontier Markets of Asia, Asia Pacific and
Europe. In general, this study employed data on daily and monthly frequency from a period of
varying starting dates (depends on the data availability) to December 2015. For the identification
of herding behavior towards market consensus, this study employs return dispersion models
proposed by Christie & Huang (1995), Chang et al. (2000), and Chiang & Zheng (2010) and the
state space model of Hwang & Salmon (2004). In order to meet few objective return dispersion
models are used over a time of varying frequencies. For some fundamental based objectives state
space model is used, due to the nature and requirement of analysis.
The findings of the study suggest that during the period of extreme stress equity return
dispersion increases rather than decreasing. These outcomes are consistent with the prediction of
CAPM as the rational asset pricing models state that equity return dispersion increases during the
turbulent period. Christie & Huang (1995) model similar to previous literature fails to capture
herding in extreme stress in any of the markets under observation. This model faces certain
criticism, therefore, this study employed two other models to test herding behavior.
The second model is nonlinear return dispersion model proposed by Chang et al. (2000)
and Chiang & Zheng (2010). The findings based on these models report significant herding
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behavior in majority of markets. It is observed that all developed markets except Australia,
Belgium, and France exhibit herding behavior when (using Chang et al. (2000model. By
setting Chiang & Zheng, 2010), the French market also exhibits herding behavior along
with other markets in the sample. However, the presence of herding is observed in all emerging
and frontier markets of Asia, Asia Pacific and Europe. These findings are consistent with previous
studies as herding is more likely to be present in emerging and developing markets (Demirer &
Kutan, 2006).
The evidence of strong herding in developing markets can be due to certain market
characteristics including weak regulatory system, immature financial system, control of market in
the hands of few institutional investors, the existence of speculators, and transmission of highly
volatile capital flows from international markets (Economou et al., 2000). As rational asset pricing
model assumes a linear relationship between returns that is why Christie & Huang (1995) model
is unable to capture herding in extreme market returns. In the presence of herding relationship of
returns and cross-sectional dispersion becomes nonlinear, Chang et al. (2000) and Chiang & Zheng
(2010) include the nonlinear term in the model. That is why this measure provides better results in
most of the markets.
One of the main objectives of this research is to determine the role of culture in driving
herding behavior. This study employs Hofstede’s six cultural dimensions of culture and introduces
them as modulating variables in the Chiang et al. (2000) and Chiang & Zheng (2000) model. It is
observed that power distance and uncertainty avoidance has no impact on herding behavior of
investor, however individualism, masculinity, and indulgence influence the relationship between
nonlinear returns and market dispersion. In the presence of long-term orientation, herding
coefficient becomes insignificant, slope dummy of interacting term decreases the dispersion of
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returns. The effect of these factors can be analyzed from different aspects, first, investor relation
to rational opportunistic behavior. Second, herding is seen as a way to learn from psychological
forces in the market (less importance of fundamentals). Third, another strong relation exists
between herding and cost of getting information. In addition to these many influences, the cultural
dimension of individualism, masculinity, and indulgence also appeared to contribute to a better
understanding of herding behavior of individual investors.
Previous literature provides ample evidence on the use of a constant coefficient model of
return dispersion. Research on time variation of herding behavior in return dispersion model is
very limited. This study uses Kalman filter for the estimation of time-varying herding behavior
through return dispersion in a large sample of markets, including developed, emerging and frontier
markets of Asia, Asia Pacific and Europe. The impact of domestic and cross-border determinants
on time series herding behavior is also observed. This study detects time variation of herding
behavior in all emerging markets and majority of the developed markets. Like Belgium, France,
New Zealand, Singapore and Switzerland exhibit time-invariant herding behavior. Therefore, it
can be concluded that developed and developing markets provide support to the hypothesis of
time-variant herding phenomenon, not a short-term disequilibrium. This time variation is mainly
dependent upon the stock market functionality and presence of volatility in the market. The role
of stock market performance, domestic and global volatility cannot be neglected in the
determination of time variation of herding behavior. Herding behavior is more pronounced in
periods of high volatility and lagged value of global volatility provides similar results as domestic
volatility.
The second line of the methodology adopted is state space model of herding behavior.
Herding towards market portfolio is identified through this model. Hwang & Salmon (2004) argue
173
that the state space model of cross-sectional beta dispersion provide a better measure. This measure
is based on the cross-sectional dispersion of betas, therefore conditioned on fundamental and other
factors in the market. The outcomes of this measure provide better results among all of the markets
as the market dynamics are properly captured. All countries in the sample except Sri Lanka exhibit
herding behavior around market portfolio and particularly in stress. The Graphs from the herding
evolution validate the observation because herding evolution is observed in rising and falling
markets. The major crisis in the sample is a Global financial crisis. One can observe that a
significant reaction of all market participants of developed and emerging markets said to have
affected by this event and can be seen as sharp spikes and turning points in the herding behavior.
Therefore, Hwang & Salmon (2004) model provides a comprehensive analysis of dynamics of
herding spotted from both estimation results and time series of herding evolution. According to
these results, Sri Lanka is the only market in the region where the full benefit of portfolio
diversification can be enjoyed.
The role of a macroeconomic factor helps in understanding the market structure and
mechanism. Herding behavior is the deviation of the market from its fundamental structure,
therefore unexpected variation in macroeconomic variables can help to understand the investors
herding behavior. For this purpose ARIMA residuals are incorporated in Hwang & Salmon (2004)
model to figure out the effect of unexpected shocks on a macroeconomic variable on investor
herding behavior. It is generally observed that macroeconomic fundamentals itself do not explain
herding behavior of an investor (Hwang & Salmon, 2004) but the unexpected variations in these
variables can contribute to irrational investor behavior (Messis & Zapranis , 2014). In majority of
the markets macroeconomic shocks provide insignificant effect but in few cases, one or two of the
four variables have significant effect on herding behavior of an investor. In some cases, the
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inclusion of Macroeconomic shocks affects the intensity of herding behavior that can be observed
through the changed value of herding Coefficient. Therefore, it can be concluded that not
macroeconomic variable itself, but the unexpected variation in macroeconomic components
influence the herding behavior of an investor in few markets.
In the era of globalization and internationalization, along with fundamental, trade and other
linkages between the market participants cannot be explained without describing the behavioral
impact of the decision-making process. According to the literature, other than fundamental and
common cause linkages certain behavioral factors play a vital role in the transmission mechanism
of volatility and crisis (Chiang et al., 2007). This study focuses on the behavioral link namely
contagion of herding (pure contagion) in a given set of markets and determines the cross country
linkages of each market with a global leader (US) through extreme shocks in macroeconomic
components or crisis. For the said purpose conditional correlation between herding measures is
generated through diagonal VECH bivariate GARCH (1,1) model. In order to assess the factors
contributing to this correlation, OLS regression is used to employ the dummy variables of extreme
shock due to macroeconomic news and due to the common global financial crisis on the
conditional correlation series for each country under analysis (having significant herding
behavior).
In general, it is observed that more integrated markets exhibit the more intense effect of
herding contagion. The volatility spillovers in one market can generate contagion effect in other
markets due to investor behavior, this behavioral impact generates speculations that have an impact
on the markets. In general certain macroeconomic shocks and financial crisis specifically in
developed markets exhibit a significant effect on herding correlation, these results are consistent
with the findings of Mylonidis & Kollias (2010). These outcomes can be related to the wake-up
175
call hypothesis where similarities are observed in two markets if any crisis event or a specific
shock hits the market (Chiang et al., 2007). These findings provide interesting insight, as the
shocks itself do not exhibit significant effect on a herding behavior of investor in local markets but
have significant impact on the correlation of herding series with US market. Therefore, it can be
concluded that if there is contagion of herding in from one economy to another, then fundamental
and crisis shocks both contribute to irrationality of investor behavior in target market.
Discussion
In this section this study focusses on the main contribution of the study and discuss that to how
much extent the cultural differences and adverse macroeconomic conditions affect the herding
intensity of an investor in a global context.
The herding behavior affected by the degree of individualism, which has direct
implications for the stock market pricing and market efficiency. In individualist societies usually
investors exhibit less herding behavior but these characteristics can be analyzed in different ways.
Usually some rational component of herding behavior trigger the whole market mimetic actions
and predecessors follows the actions of market players. This behavior is obvious when investors
give more weightage to psychological forces rather than market fundamentals. Other than these
problems information disadvantage, personal experience and reputation concerns defined in to
career steps also matters. Therefore, one can say that individualist behavior can be a cause of
cultural differences. However, the change in behavior is complex to understand as other factors
also contribute like the type of financial system and its degree of development, the surrounding
176
information and the regulatory framework. Therefore, the herding behavior of investor cannot be
easily understandable solely in cultural environment.
Masculinity and indulgence have direct influence on the strategic investment decision and
behavior. It can be argued that Individuals in highly indulged societies exhibit weak control over
desires, whereas societies with a low level of indulgence control their urges strongly. These
findings suggest that masculinity and indulgence has important implication for herding in stock
market as the markets characterized by these dimension indicate competition, achievement and
success. These characteristics are supposed to reduce herding but in practice promote more intense
herding behavior in the market. These findings can be explained with reference to reputational
concern of herding behavior where investor in order to maintain its reputation follow the practices
of bench mark while making investment strategies and decisions.
Moreover in masculine societies there are more men than women and hold overrepresentation in
leading position in asset management. Males are more aggressive and less risk averse and
characterized by overconfidence. These characteristics give rise to more aggressive investment
styles where other investors in the market due to lack of information and high cost of obtaining
relevant information follows the market consensus to maintain their reputation.
Along with the cultural variables, Global as well as domestic market volatility are observed to be
significant determinants of the time-varying herding behavior in almost all developed, emerging
and frontier markets. It is observed that the role of global factors specifically the U.S. market–
related factor, CBOT volatility index (VIX) dominate the time variation in herding behavior
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specifically in emerging and frontier markets and this behavior is more obvious during the global
financial crisis of 2008. Therefore, has major implication for market efficiency under the financial
crisis. This suggests that market stress in the United States not only harvested by the investor of
other developed, emerging and frontier markets but also have behavioral implication. The herding
behavior of the investor show strong correlation with US market and this behavior has contagious
effect that is more intense during adverse macro environment and crisis situation.
Methodological Implications and limitation
In order to observe the best measure to capture the herding towards market consensus
towards the market, this study employs all three models available Christie & Huang (1995), Chang
et al. (2000), and Chiang & Zheng (2010) and the state space model of Hwang & Salmon (2004).
As rational asset pricing model assumes a linear relationship between returns that is why Christie
& Huang (1995) model based on CAPM is unable to capture herding in extreme market returns.
In the presence of herding relationship of returns and cross-sectional dispersion becomes
nonlinear, Chang et al. (2000) and Chiang & Zheng (2010) include the nonlinear term in the model.
The results provide comparatively better outcomes than Christie & Huang (1995).
It is observed that herding appears during a crisis period and disappears after giving a flight
to the fundamentals of the market. The first model only observes it under extreme stress while the
second class of return dispersion models negates the effect of fundamentals and other factors. The
return dispersion model fails to capture this characteristic of the market. Therefore, this study
employs Hwang & Salmon (2004) model to address these shortcomings. Hwang & Salmon (2004)
not only conditioned on fundamental and other factors but it also incorporates the effect of time
178
series volatility. This is the reason, out of all three measures this model has strong theoretical
properties and provide better empirical results.
This study provides an additional insight into the existing literature by examining the best
model to identify the herding behavior of an investor in dynamic settings. It is observed that model
based on the state space model better predicts the time variation of herding behavior as the value
of AIC and BIC are lower as compared to return dispersion models.
In order to capture unexpected component this study employed ARIMA models to generate
residuals of macroeconomic structure over GARCH methodology. According to literature, these
models are appropriate for forecasting and provide better results than over parametrized models.
Generalizability
The results of this study are generalizable as it uses a large dataset of sampled countries that
include developed, emerging and frontier markets of Asia, Asia Pacific and Europe. The market
participants of other markets not included in the data set sharing similar culture similarities or
market dynamic can use this research as a benchmark for their decision-making process. The
generalizability of this study is further supported by the use of larger data set. In this time period
history of the global economy shared several events that have common effects around the globe.
Therefore, in the wake of an uncertain event in future, this research can be used to understand the
market dynamics collectively.
Theoretical Implications
This study has some theoretical contributions for academicians. They are listed below.
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For Risk Diversification
The traditional relationship between risk and return explained by the Capital Asset Pricing
Model provides the basis for understanding the dynamics of herding behavior on risk
diversification. Chiang & Zheng (2010) stated that herd behavior describes the increased
correlation in trades due to the interaction of investors in stock market. So, in the presence of
herding behavior, a large number of securities are required in a portfolio to achieve the same level
of diversification due to the existence of an inferior degree of correlation. Herding has negative
implications in portfolio diversification, if there is a strong co-movement between stocks, the
benefit of diversification is eliminated as the stock prices tend to move in unison. This study
indicates that a large number of markets in the sample exhibit herding behavior and some of the
markets exhibit a strong correlation in herding measures. Therefore, it can be implied that investing
in these markets prevent the investor from enjoying the full benefit of diversification and their risk
increases.
For Behavioral Asset Pricing Model
The risk involved in the pricing of investment under the current scenario has both
fundamental and behavioral components. Therefore, it is deemed necessary to incorporate
behavioral factors like herding while modeling the asset prices. As this research validates the effect
of herding behavior and its consequences on securities returns, therefore new asset pricing models
suggested by several authors should be utilized. For example, the behavioral asset pricing theory
proposed by Shefrin and Statman(1994), affects behavioral asset pricing model by Statman, et al.
(2008) and investor psychology in Hirshleifer (2001). By improving the information disclosure
trading activity of investors can be improved. This informational efficiency minimizes the risk of
180
securities as prices are determined on the basis of fundamentals. Better knowledge of market
dynamics and clear understanding of factors behind irrational behavior could result in more
accurate valuation, estimation, and forecasting decisions.
For Behavioral Portfolio Management
In Behavioral finance irrationality of investors can be explained in several ways and several
psychological factors give rise to investor portfolio diversity (Shum & Faig 2006). Heterogeneity
in beliefs can be an outcome of intrinsic differences (He & Shi 2010). In a situation of high
uncertainty and complexity investors usually, ignore fundamental information and rely on their
intuitions (Kahneman & Riepe 1998). These intuitions give rise to several systematic errors and
correlated behavior in markets where investors overestimate their knowledge and underestimate
risk and are unable to control events (Giordani & Söderlind, 2006).
Professional portfolio managers can use this study to make better strategies and funds that
are behaviorally centered. Along with behavioral approach, they can also incorporate the effect of
social influences like national culture. Policymakers should focus on the communications channel
with financial markets and manage the prospects of fluctuations in policy because herding can
aggravate in financial market due to change in macro information.
Policy and Practical Implication
This study has several practical implications for practitioners and policymakers. Few are listed
below
For Individual Investors
With the emergence of behavioral finance, few governments are designing a vast range of
programs to provide economic and financial education to the individual investors and general
public. The basic purpose is to educate those individuals who are practically omitted from the
181
formal financial sector. They practically lack financial knowledge and make poor investment
decisions. If these investment decisions are influenced by the herd formation, these can lead to a
huge disturbance in the financial markets. Few researchers estimate the effect of such program on
consequent financial behaviors (Lusardi, 2008; Lyons, 2010). Therefore, if the government of less
sophisticated markets particularly emerging and frontier markets provide financial education to
the investors the problem of irrational decision making can be resolved to reduce the impact of
speculators in the market.
For Institutional Investors
Institutional investors and particularly fund managers can get benefit from this study. As
origin, cause and effect of behavioral factor like herding could be utilized to device investment
analysis and related management strategies to implement certain decisions under the positive or
negative environment under the effect of this bias. Shefrin (2000), and Montier (2002) stress on
the utilization of behavioral biases in investment analysis to overcome the consequence
inefficiency and fragility of the financial system. The strategies devices by Baker & Riccardi
(2014) can be utilized to get the full benefit of optimal decision making.
For International Investors
This research has implication not only for the local investor but also for the international
investor. If the herding measures are correlated between two markets especially global leaders then
the benefit of portfolio diversification is reduced as the same risk factors are transmitted across the
markets due to volatility spillovers or contagion of herding. The results indicate that correlation
between herding measure can provide an incentive for portfolio diversification to investors.
182
For Financial Market Regulators
This paper has highlighted one of the behavioral risk (herding behavior) stressed in Daniel
Hirshleifer & Teoh (2003). They suggest two steps to improve the public policy, one is designing
of such policies that can minimize the errors and second is to improve the efficiency of the market.
Thus, the regulation of this risk factor will reduce the impact of irrational behavior and market
imperfection. Similarly, Suto & Toshino (2005) stress the need for governance of financial markets
against the behavioral risk factors. Asian markets are more vulnerable to sociological and
psychological inclinations as suggested by the (Kim & Nofsinger, 2007). Therefore, there is a need
to frame the financial regulation under the fund management industries in the markets of Asia. In
the era of globalization and transmission of shocks through behavioral channels has been validated
through this study, therefore there is an immense need to design global governance framework that
will help the international investors to play safely in the global financial market.
Limitations of the Study
The first limitation of this research is the identification of irrational herding of individual
herding particularly towards market consensus. The rational herding normally done by the
institutional investor is completely ignored due to data limitations.
Another limitation of this research is an inability to differentiate between spurious and
intentional herding in the stock market.
Herding behavior in the stock markets specifically in developing market is driven mostly
by the financial analyst (Graham, 1999; Welch, 2000). They contribute to making an
informational cascade which is thus followed by the individual investors. This type of
herding is also not captured in this study.
183
Future Recommendations
Future research can examine other macro information on herding and further distinguish
between spurious herding and intentional herding in the stock market.
The institutional investors, specifically asset managers have a strong influence on the
herding behavior, particularly, when herding is caused by the informational cascade. It may
be interesting to incorporate their view and behavior to check the robustness of this analysis
specifically with reference to cultural dimensions.
It can be beneficial in the market to understand the interaction between investor types.
Different type of investors share different set of information like individual and
institutional investors, domestic and foreign investors and their reaction in the market can
also be different.
The results from this research validate the effect of time variation in herding behavior but
literature is limited to few markets. Future research can identify the factors behind these
time dynamic specifically in a large set of emerging market around the globe.
There is useful evidence that cultural differences cause different behavior, yet the way how
behavior is changed may be complex. It may be useful to incorporate further factors such
as the type of financial system and its degree of development, the information surrounding
it, or the regulatory framework to get a more comprehensive picture of investor behavior.
This research can be extended to the other markets of the economy also, like real estate,
commodity markets, and currency market. These all markets play important role in the
economy.
184
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Appendix 1
Table 1: List of Biases
Cognitive Bias Description
Overconfidence bias Excessive confidence in one's own answers to questions. For example, for certain
types of question, answers that people rate as "99% certain" turn out to be wrong
40% of the time.
Representative Bias According to this bias people identify common component in entirely different event
by fitting a completely unknown and new one event to the current. Tversky and
Kahneman (1974) argued that individuals usually judge probabilities “by the degree
to which A is representative of B, that is, by the degree to which A resembles B.”
Anchoring and
Adjustment bias
While making estimates to find resulting answers, people usually hooked up in an
initial state. (Slovic, & Lichtenstein, 1971).
Cognitive
dissonance bias
When newly acquired information conflicts with preexisting understandings, people
often experience mental discomfort—a psychological phenomenon known as
cognitive dissonance.
Availability Bias Estimating what is more likely by what is more available in memory, which is biased
toward vivid, unusual, or emotionally charged examples.
Self-attribution bias Self-attribution bias refers to the tendency of individuals to ascribe their successes
to innate aspects, such as talent or foresight, while more often blaming failures on
outside influences, such as bad luck.
218
Illusion of control
bias
People not only overestimate others' ability to know themselves but also
overestimate their ability to judge others.
Ambiguity Aversion
Bias
The tendency to avoid options for which missing information makes the probability
seems "unknown".
Gambler Fallacy
bias
Gambler fallacy is a false impression of the precision of the law of chance (Tversky
and Kahneman, 1971). It occurs when an investor wrongly forecast the reversal in
stock prices and trade in a market with an expectation that random event are self-
correcting.
Self-control bias The ability to restrain emotions and impulses; choosing to stick to one’s plans. It
can be depleted with use but increased over time.
Excessive Optimism
bias
Excessive optimism refers to the inclination of people to prefer the probability of
occurrence of favorable outcomes to that of unfavorable outcomes.
Mental accounting
bias
Mental accounting is the tendency of keeping separate accounts by the people on
different subjective motives, often has an irrational and negative effect on
consumption decision and other behaviors (Thaler, 1999)
Confirmation bias The tendency of interpreting and searching information in a way that is in
confirmation with one's preconceptions.
219
Hindsight bias Shiller (2000) explains this bias as a tendency of a person to overestimate their
ability to have predicted an outcome before it actually happened. Therefore, it is a
psychological fact in which past events seem to be more prominent than they
appeared while they were occurring.
Recency bias It is the tendency by which an individual weigh recent events more than previous
events.
Framing bias Using an approach or description of the situation or Framing issue that is too
narrow. Also, drawing different conclusions based on how data is presented
Disposition effect The tendency to selling overvalued assets and buying assets that are undervalued.
Professors Shefrin and Statman (1985) developed the idea of loss aversion into a
theory called the ‘disposition effect’, which indicates that individuals tend to sell
winners and hold losers.
Herding effect Herding behavior is a tendency where an investor simply imitates the action of
others.
220
Appendix 2
Table 2: Industry Classification based on Industrial classification bench mark
Industries Sub sectors
Oil & Gas 5
Basic Materials 12
Industrials 21
Consumer Goods 19
Health Care 5
Consumer Services 16
Telecommunications 2
Utilities 5
Financials 39
Technology 7
221
Appendix 3
Table 3: List of variables and their description
Variables Abbreviations Description
Cross sectional standard
deviation of returns
CSSD CSSD is standard measure of dispersion
Cross sectional absolute
deviation of returns
CSAD CSADt is absolute measures of return dispersion
Industry returns Ri,t Ri,t is the observed return on industry index i for day t
Market returns Rm,t Rm,t is cross-sectional average of the number of returns in the
aggregate market portfolio for day t.
Cross sectional Standard
deviation of betas 𝑆𝑡𝑑𝑐(𝛽𝑖𝑚𝑡
𝑏 ) Stdc(Bimt) is a standard measure of beta dispersion.
Volatility �̂�𝑡2 �̂�𝑡
2 is a measure of volatility, the conditional variance based on
asymmetric GARCH(1,1)
Global Volatility index VIXt-1 Lagged value of CBOE global implied volatility Index
Individualism index, IDVi IDV is the tendency by which the people are not closely connected
and are more concerned about themselves or their families.
masculinity index, MASi, MAS index define the clear difference between gender roles.
uncertainty avoidance
index,
UAIi UAI is the level of threat perceived during an uncertain or unknown
situation.
power distance index PDIi, PDI describe the unequal power distribution in an organizations.
Long-term orientation LTOi LTO defines the virtues by which promote and encourage the search
of future returns orientation.
Indulgence Index IVRi IVR refers to the situation how individuals have control over their
desires.
Exchange Rate ER ER is the measure of exchange rate shocks at time t.
Money supply MS MS is the money supply in a given country at a given time period t.
Interest rates i Short term rates in a given economy at time t.
Industrial production IP IP is the industrial production index of a country at time t
222
Appendix 4
Table 4: Correlation between herding measure Hmt
Panel A: Developed Markets
Code AU OE BG DK FR BD GR HK IT JP NL NZ NW PT SG ES SD SW UK US
AU 1
OE -0.68** 1
BG -0.44** 0.22** 1
DK -0.45** 0.46** 0.58 1
FR -0.74** 0.65** 0.62** 0.64** 1
BD -0.72** 0.64** 0.53** 0.52** 0.81** 1
GR -0.19** 0.44** 0.061 0.26** 0.00 0.085 1
HK -0.71** 0.91** 0.14 0.34** 0.56** 0.60** 0.44** 1
IT -0.58** 0.61** 0.45** 0.63** 0.82** 0.64** 0.03 0.49** 1
JP -0.34** 0.12 0.21** 0.19** 0.05 0.21** 0.54** 0.17* 0.01 1
NL 0.067 0.15* 0.16** 0.28** 0.39** 0.20** -0.39** 0.02 0.56** -0.52** 1
NZ 0.69** -0.68** -0.13** -0.20** -0.42** -0.41** -0.37** -0.68** -0.32** -0.24** 0.27** 1
NW -0.65** 0.73** 0.35** 0.37** 0.77** 0.75** -0.03 0.68** 0.66** -0.03 0.40** -0.37** 1
PT 0.28** -0.2** 0.072 0.17* -0.32** -0.28** 0.50** -0.13 -0.21** 0.26** -0.25** 0.11 -
0.60** 1
SG 0.10 0.26** 0.23** 0.17* 0.18* 0.17* -0.02 0.18* 0.26** -0.33** 0.62** 0.08 0.42** -0.14* 1
ES -0.65** 0.65** 0.44** 0.53** 0.85** 0.76** 0.06 0.53** 0.76** 0.14** 0.42** -0.27** 0.85** -0.43** 0.22** 1
SD -0.77** 0.79** 0.32** 0.46** 0.67** 0.78** 0.41** 0.80** 0.53** 0.38** -0.02 -0.55** 0.75** -0.20** 0.15* 0.71** 1
SW -0.98** 0.70** 0.43** 0.45** 0.78** 0.73** 0.18** 0.73** 0.61** 0.31** -0.03 -0.68** 0.67** -0.30** -0.12 0.68** 0.78** 1
UK -0.64** 0.39** 0.78** 0.67** 0.79** 0.66** 0.02* 0.30** 0.65** 0.28** 0.20** -0.20** 0.47** -0.03 -0.01 0.66** 0.44** 0.64** 1
US -0.75** 0.53** 0.65** 0.59** 0.79** 0.74** 0.01 0.43** 0.67** 0.27** 0.21** -0.30** 0.60** -0.19** 0.00 0.74** 0.60** 0.74** 0.88** 1
** indicate significance at 5%, * indicate significance at 1%
223
Appendix 4 (Continued…)
Panel B: Emerging and Developed Markets
Probability CA IN ID KO MY PK PH CY TA TH TK US
CA 1
IN 0.07 1
ID -0.03 -0.20** 1
KO 0.03 -0.02 0.22** 1
MY 0.25** 0.25** 0.08 0.12 1
PK 0.30** 0.41** -0.38** -0.06 0.34** 1
PH 0.12 0.12** -0.20** -0.32** -0.06 0.48** 1
CY 0.06 -0.06 0.07 0.06 0.12 -0.11 -0.29** 1
TA 0.31** 0.20** -0.05 -0.08 0.12 0.28** 0.28** -0.1 1
TH -0.12 0.24** 0.04 -0.03 0.01 0.08 0.27** 0.06 0.17* 1
TK 0.27** 0.16* -0.06 0.11 0.14* 0.40** -0.11 0.20** 0.04 0.03 1
US 0.16* 0.52** -0.44** -0.19** 0.14* 0.34** 0.34** -0.19** 0.26** 0.31** -0.17* 1
** indicate significance at 5%, * indicate significance at 1%