what factors affect behavioral biases?: evidence from ... · definition of rationality is unique in...
Post on 12-Jul-2020
2 Views
Preview:
TRANSCRIPT
Working Paper Series
n. 42 ■ May 2013
What Factors Affect Behavioral Biases?: Evidence From Turkish Individual Stock Investors
Bülent Tekçe
2 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Statement of Purpose
The Working Paper series of the UniCredit & Universities Foundation is designed to disseminate and
to provide a platform for discussion of either work of UniCredit economists and researchers or outside
contributors (such as the UniCredit & Universities scholars and fellows) on topics which are of special
interest to UniCredit. To ensure the high quality of their content, the contributions are subjected to an
international refereeing process conducted by the Scientific Committee members of the Foundation.
The opinions are strictly those of the authors and do in no way commit the Foundation and UniCredit
Group.
Scientific Committee
Franco Bruni (Chairman), Silvia Giannini, Tullio Jappelli, Levent Kockesen, Christian Laux, Catherine
Lubochinsky, Massimo Motta, Giovanna Nicodano, Marco Pagano, Reinhard H. Schmidt, Branko
Urosevic.
Editorial Board
Annalisa Aleati
Giannantonio De Roni
The Working Papers are also available on our website (http://www.unicreditanduniversities.eu)
3 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Contents
Abstract 4
1. Introduction 5
2. Literature Review and Hypothesis Development 7
3. Data and Methodology 12
4. Results 20
5. Conclusion 29
4 WORKING PAPER SERIES N. 42 - MAY 2013 ■
What Factors Affect Behavioral Biases?: Evidence
From Turkish Individual Stock Investors
Bülent Tekçe
Yapı Kredi Bank, Business Performance Management Manager
Abstract
This paper uses nationwide individual stock investor transaction data for 244,146 investors with a total
of 64 million buy and sell transactions in 2011 to analyze how common overconfidence, familiarity
bias, representativeness heuristic and status quo bias are among Turkish individual stock investors
and what factors affect these biases. This study is unique in the sense that, up to our knowledge no
research focuses on nationwide data to analyze more than one bias. We find that overconfidence and
familiarity bias are common among individual investors. Findings of status quo bias are totally in line
with overconfidence. Male, younger investors, investors with lower portfolio value and investors in
less developed (low income, low education) regions exhibit overconfidence, familiarity bias and status
quo bias more. Our findings are robust to the use of different subsamples, bias measures and
analysis methods.
5 WORKING PAPER SERIES N. 42 - MAY 2013 ■
1. Introduction
Empirical evidence in the behavioral finance literature show that individuals do not behave rationally.
Barberis and Thaler (2003) provide a good summary of models that try to explain the equity premium
puzzle, excess volatility, excessive trading, stock return predictability using both Prospect Theory of
Kahneman and Tversky (1979) and beliefs. Daniel et al. (2002) support the view that markets are not
efficient and investor biases affect security prices substantially. Black (1986), De Long et al. (1990),
Shleifer and Vishny (1997), Barberis et al. (2001), Hirshleifer (2001), Daniel et al. (2002), and
Subrahmanyam (2007) show that investors are not rational or markets may not be efficient and hence
prices may significantly deviate from fundamental values due to existence of irrational investors.
Vissing-Jorgensen (2004) uses investor optimism survey data conducted by UBS and Gallup from
1998 to 2002 and find that irrational behavior (such as, representativeness heuristic, self-attribution
bias, disposition effect, under-diversification and status quo bias) are weaker for more sophisticated
investors (wealth and investor experience used as proxies for investor sophistication). Hence, it can
be proposed that behavioral biases affect some investors less than others. As biases may
significantly affect stock prices, it is important to understand which factors affect biases.
Definition of rationality is unique in the sense that irrespective of personality differences every rational
decision maker behaves same. However, there are many ways of being irrational which may depend
on individual as well as cultural differences. Hence, individuals may tend to behave differently in their
financial decisions from one society to another. Cultural differences may cause differences in biases
as cognitive biases can be triggered or suppressed by different life experiences and cultural
backgrounds. Degree of individualism/collectivism has significant impact on cognitive styles, risk
attitudes and behavioral tendencies of inhabitants. Individuals in collectivist societies tend to be more
risk tolerant. As presented by Fan and Xiao (2005) and Statman (2010), individuals in different
societies / cultures may have different behavioral biases which may affect their financial decisions.
Majority of behavioral finance literature analyzes individual investors in developed markets such as
USA, UK and Western Europe. Hofstede (2001) finds that Turkish people are more collectivist
compared to USA, UK and Western Europe. Besides, the ambiguity avoidance index, which captures
the tolerance of a society for uncertainty and ambiguity, is high among Turkish citizens. As Turkey is
an emerging market and there exists cultural differences compared to USA, UK and Western Europe,
it is worth analyzing Turkish individual investors in terms of behavioral biases they exhibit. If Turkish
individuals differ from those in the developed countries, the behavioral biases of Turkish individual
investors may differ from the findings in the literature.
Many of the research in behavioral finance literature depend on data that is generally limited to the
subsamples of overall investor groups in these countries. This study is unique in the sense that,
although there are several studies using nationwide data (either in developed markets such as
6 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Finland, or in emerging markets such as Taiwan) to analyze a specific bias, up to our knowledge no
research focuses on nationwide data to analyze different biases.
It is also interesting to analyze Turkish individual investors as Istanbul Stock Exchange (ISE) has
specific characteristics. ISE is a member of World Federation of Exchanges (WFE) and Federation of
European Securities Exchanges (FESE). As a leading / advanced emerging market stock exchange,
ISE is recognized as an investable market according to US Securities and Exchange Commission
(SEC) and Japan Financial Services Agency. ISE has one of the highest turnover ratio among world
stock markets, which may be related to the biases among Turkish stock investors. According to World
Bank, in 2011, ISE is the 5th highest stock market in terms of turnover ratio after Italy, Republic of
Korea, China and USA. Trading volume in ISE is relatively high and provides a liquid market for
investors. Although foreign investors hold around 65% of free float in ISE, they constitute only around
15% of the trading volume. Foreign investors mostly prefer ISE30 and ISE100 (a major benchmark)
stocks, which have high market capitalization, high liquidity and are representative of sectors they
operate. Trading volume and liquidity is mostly provided by local individual investors.
This study focuses on four behavioral biases; overconfidence, familiarity bias, representativeness
heuristic and status quo bias of all the Turkish individual stock investors and analyzes how prevalent
these biases are among investors. We use transaction data and also analyze what factors such as
gender, age, wealth, experience and geographical region of residence affect these biases.
Due to aggressive trading behavior, overconfident investors may have to pay significant amount of
commissions. Besides, overconfident investors may hold riskier portfolios than they should tolerate
due to their underestimation of risks. Overconfidence not only affects financial markets and prices, but
also individuals in the sense that they make investment mistakes and lose money. Hence it is
important to determine overconfidence among investors and factors affecting overconfidence.
Familiarity bias is important in the sense that it explains how investors decide to purchase a stock for
reasons other than rational motives. The psychology literature shows how representativeness
heuristic can explain expectation formation which directly affects investment decisions. There are
several studies which focus on how investors extrapolate past price trends to predict future prices and
measure representativeness heuristic accordingly. Representativeness heuristic may lead individuals
to give investment decisions that harmfully affect their wealth. Such an approach may also distort
asset prices. Although overconfident investors trade too much, investors exhibiting status quo bias
may refrain from trading at all.
7 WORKING PAPER SERIES N. 42 - MAY 2013 ■
2. Literature Review and Hypothesis Development
2.1. Overconfidence
Overconfidence can be defined as the unmerited confidence in self’s judgments and abilities. Odean
(1998) describes overconfidence as the belief that a trader’s information is more precise than it
actually is. This is equivalent to narrow confidence intervals in predictions. Daniel et al. (1998) define
an overconfident investor as one who overestimates the precision of his private information signal, but
not of information signals publicly received by all.
Overconfidence may stem from different reasons. Self-attribution bias is attributing successful
outcomes to own skill but blaming unsuccessful outcomes on bad luck as discussed in Miller and
Ross (1975) and Kunda (1987). Langer (1975) states that illusion of control is the tendency for people
to overestimate their ability to control events that they have no influence over. Unrealistic optimism is
simply confidence about the future or successful outcome of something. It is the tendency to take a
favorable or hopeful view as discussed by Weinstein (1980) and Kunda (1987). Russo and
Shoemaker (1992) define confirmation bias as the tendency for people to favor information that
confirms their arguments, expectations or beliefs. As discussed by Svenson (1981), better than
average effect implies that people think they have superior abilities than on average. Hence,
individuals tend to believe they are in the best class among peers. Calibration refers to how
individuals can assess the correctness of their estimates. Deaves et al. (2010) argue that a
miscalibrated agent assumes lower level of mistake than she / he actually makes.
Different forms of overconfidence reveal that overconfident investors believe that their decisions will
prove to be correct and expect higher returns than average. However, this is not necessarily the case
and overconfident investors are exposed to possible losses due to their investment decisions.
Fischhoff et al. (1977), Russo and Shoemaker (1992), Griffin and Tversky (1992), Kahneman and
Riepe (1998) show that overconfidence is common among decision makers. Odean (1998) presents a
good summary of overconfidence in different professional fields such as investment bankers and
managers. The author also finds that overconfidence affects financial markets; overconfidence
increases expected trading volume, increases market depth and decreases the expected utility of
overconfident traders. In line with literature, we hypothesize that overconfidence is common among
Turkish individual equity investors.
Barber and Odean (2001) test whether men are more overconfident than women by partitioning
investors on gender. The authors use data from a nationwide brokerage house for the period 1991-
1996 by focusing on common stock investments of households. The authors define overconfidence as
annual turnover and find that women turn their portfolios almost 53% while men turn 77% annually
indicating that men trade 45% more than women annually. Findings of Barber and Odean (1999),
8 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Chen et al. (2007), Acker and Duck (2008), Graham et al. (2009), Grinblatt and Keloharju (2009),
Hoffmann et al. (2010) also support the view that men are more overconfident than women. In line
with literature, we also expect Turkish male investors to be more overconfident than female investors.
Chen et al. (2007) use transaction data of a large Chinese brokerage house to analyze
overconfidence in Chinese investors. The authors find that individual investors in China trade more
frequently than US individual investors. Acker and Duck (2008) use a stock market game and
predictions of examination marks to measure overconfidence among Asian and British students. They
find that Asian students are more overconfident than British students. These findings imply that level
of overconfidence can be different among cultures. In line with literature, we hypothesize that Turkish
individual stock investors are more overconfident than US individual investors.
Graham et al. (2009) find that wealthier and highly educated investors are more likely to perceive
themselves as competent, implying overconfidence. On the other hand, Ekholm and Pasternack
(2007) confirm that investors with smaller portfolios are more overconfident compared to investors
with larger portfolios as these investors are more experienced and wealthier. Hence, we hypothesize
that sophisticated investors are less prone to overconfidence.
2.2. Familiarity Bias
According to Fox and Tversky (1995), when people are offered two alternatives, they prefer the one
that they are familiar with. This finding is also valid for stock selection. This is because people are
better informed about the securities that they are familiar compared to the ones that they are not.
According to Huberman (2001) this is the defining property of familiarity. Huberman (2001) argues
that due to preference for familiar and distaste for and fear from unfamiliar leads to the basic result
that people simply prefer to invest in familiar securities. This is probably due to the fact that investors
tend to feel they know more about the stocks that they are familiar with. Merton (1987) develops a
capital market equilibrium model in which each investor knows only a subset of available securities.
The subset differs across investors. The model implies that investors make their investment decisions
from the stocks that they are familiar with. According to Merton, increasing analyst coverage for the
firm can also help increase investor base and grab their attention. Massa and Simonov (2006) use
Swedish data set, including income, wealth, demographic variables, and some other additional control
variables and find that familiarity affects individual investors’ investment decisions.
Investors face a challenge when they decide to buy a security among many alternatives that is
beyond the capabilities of human capacity to analyze and select. Hence, when deciding what
securities to invest, individual investors should simplify the search process. This means that individual
investors focus on securities that grab their attention most, implying that investors will be inclined to
invest in familiar securities. The literature presents that familiarity whether in the form of more
9 WORKING PAPER SERIES N. 42 - MAY 2013 ■
marketing / advertising, media citation, being local to investor or analyst coverage affects investment
decisions.
Taking search costs into accounts, Sirri and Tufano (1998) hypothesize that consumers purchase
equity funds that are easier or less costly for them to identify. These may be among funds with more
marketing expenses than competitors and those receiving greater media attention which increases
brand awareness. Investors will probably put this fund in a “consideration set” from which they select
products. The authors find that a larger share of media citation is related to faster growth in funds.
Although the authors state that they cannot disentangle the direction of causality, the findings indicate
that the more familiar the investors are with a security, the more likely they are to buy it as it will be in
the “consideration set” of the investor. Jain and Wu (2000) and Barber et al. (2005) also find that
individuals invest in securities that they are familiar with, familiarity being increased through
advertising.
Grinblatt and Keloharju (2001) argue that home bias may be a part of a larger phenomenon in which
investors exhibit a preference for the familiar companies. As the authors mention, familiarity has many
facets such as distance of the headquarter of the stock from investor, similarity in culture and / or
language of the firm may be the roots for familiarity. Using these facets as proxies for familiarity,
authors find that investors in Finland are more likely to buy stocks that are familiar to them.
Coval and Moskowitz (1999) show that the preference for investing close to home also applies to
domestic stock portfolios. According to authors, investment managers exhibit a strong preference for
locally headquartered firms, particularly small, highly levered firms. As the firm size increases, more
non local investors add the security to their portfolio. These results suggest that investors prefer the
securities that they are more familiar with and have advantage over nonlocal investors due to
asymmetric information. Coval and Moskowitz (2001) confirm the findings also for mutual fund
managers that fund managers trade local securities at an informational advantage due to familiarity
towards these assets.
Zhu (2002) analyzes individual investor preference for nearby investments for equities. The author
argues that local bias (the tendency to invest in nearby investment alternatives) and home country
bias may be a function of the same underlying driving factor, familiarity bias. The results confirm that
both institutional and individual investors tend to hold stocks of companies with nearby headquarters
(individuals exhibiting higher degree of bias).
In line with literature, we hypothesize that a significant portion of Turkish individual equity investors
invest in stocks that they are familiar with.
10 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Barber and Odean (2008) argue that professional investors are less prone to familiarity bias than
individual investors. Hence, we expect sophisticated investors to have lesser degree of familiarity
bias.
2.3. Representativeness Heuristic
Representativeness heuristic describes the degree to which a sample is similar to another sample in
all essential characteristics. It is based on stereotypes. Tversky and Kahneman (1971) argue that
people have erroneous intuitions about chance. Due to law of small numbers, they view a sample
randomly drawn from a population highly representative of the population which can be described as
representativeness heuristic. Representativeness can affect the prediction procedure of individuals.
Tversky and Kahneman (1974) state that people often predict the future value of a stock based on
representativeness. If this is the case, investors will be inclined to buy stocks, which have been
increasing recently (extrapolation bias).
In an experimental study, Andreassen and Kraus (1990) analyze the effects of stock market trends in
investment decisions. Investors extrapolate recent past stock price trends which results in more
purchasing after two successive bull periods and more selling after two successive bear periods.
Extrapolation of stock price trends to the future may be related to representativeness heuristic since
investors may think that recent short period of price movements is derived from a process with bull
(bear) characteristics.
As presented by Lakonishok et al. (1994), in the long run (3-5 years) value stocks outperform growth
stocks which cannot be attributed to riskiness of value stocks. The authors argue that, investors think
recent past performance of growth stocks will continue in the future as they extrapolate the return
trend of these stocks and invest in growth stocks. When it turns out that return patterns do not realize
as investors predict, value stocks outperform growth stocks in the long run. According to authors,
investors make judgment errors and extrapolate past growth into the future. Empirical research in
finance literature identified two patterns on stock returns: underreaction over shorter periods (1-12
months) and overreaction in longer periods (3-5 years). Barberis et al. (1998) develop a theoretical
model to explain these two phenomena. The underlying basics of the model depend on
representativeness heuristic as well as conservatism. Extrapolation of past returns is the form of
representativeness in the model. Individuals who exhibit representativeness heuristic extrapolate past
performance into the future. Representativeness in the model assumes that short term trend in the
price movements will be followed in the longer term.
Benartzi (2001) uses retirement saving plans of S&P 500 firms. The author finds that there is a
positive correlation between past returns and subsequent allocations to company stocks, and that
correlation gets stronger as the return accumulation period lengthens. This implies that employees
extrapolate past returns into the future. Benartzi confirms the extrapolation hypothesis using a survey
11 WORKING PAPER SERIES N. 42 - MAY 2013 ■
conducted on Internet among Morningstar subscribers. According to survey results, past returns of
stocks are likely to persist, which is supportive evidence for extrapolation hypothesis.
In line with findings from theoretical and empirical research, we hypothesize that representativeness
heuristic is common among Turkish individual stock investors.
Findings presented by Grether (1980) confirm representativeness heuristic for inexperienced or
financially unmotivated subjects; the evidence is less clear for other subjects. Chen et al. (2007) find
that representativeness heuristic is only applicable to individual investors; institutional investors being
unaffected by recent past return performance. Hence we also hypothesize that sophisticated investors
are less prone to representativeness heuristic.
2.4. Status Quo Bias
Most real decisions have a default alternative of doing nothing. Samuelson and Zeckhauser (1988)
define status quo as doing nothing or maintaining one’s current or previous decision. In an
experimental setting, the authors show that individuals stick to status quo. As Tversky and Shafir
(1992) state, choice always produces conflict because individuals have difficulties in trading off costs
against benefits or comparing risks against value which makes it difficult to give important decisions.
Making decisions becomes more complicated due to uncertainty about the actions. When each
alternative has its own advantages and disadvantages or when each alternative has risks, then
individuals face difficulties to make decision. This may lead individuals to refrain from making
decisions and stick to their current positions or at least delay the decision and exhibit status quo bias.
The authors argue that conflicts about the alternatives can increase the tendency to choose the
default option (status quo), not only the tendency to defer choice.
Samuelson and Zeckhauser (1988) argue that status quo bias may stem from rational decision
making as well as biases such as loss aversion, regret aversion and avoiding cognitive dissonance.
Similarly, Kahneman and Tversky (1982) and Ritov and Baron (1995) argue that status quo may stem
from regret aversion, Kahneman et al. (1991) relate status quo with loss aversion and Ritov and
Baron (1992) argue that status quo is a result of omission bias as keeping status quo requires
omissions of choices. Since there are numerous alternatives in equity investments, individuals may
just omit the alternatives to prevent facing the difficulties of making decisions.
According to Madrian and Shea (2001), preference of default contribution rate and plan in 401(k) plan
of employees in a large US corporation is related to status quo bias. Agnew et al. (2003) use
transaction data of participants from retirement plan of a large firm in US. They find that these
investors infrequently re-balance their portfolios and tend to maintain their initial asset allocations,
which imply status quo bias.
12 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Although a strand of literature shows that individual investors are overconfident and trade excessively,
studies with retirement plans reveal that investors may exhibit status quo bias in their investment
decisions. The analysis based on brokerage house data yield excessive trading whereas analysis
based on retirement plan data reveals infrequent trading. Since equity investments have many
alternatives to decide among (whether to buy, sell or hold, when to buy/sell or what to buy/sell), and
risks and benefits may not be evaluated easily, equity investors may be inclined to stick to status quo
(do nothing) or just defer the decision.
As psychology literature suggests, we expect a portion of stock investors to keep their portfolio
positions unchanged.
Status quo is related to reluctance to trade whereas overconfidence is related to excessive trading.
Hence, as argued by Hoffmann et al. (2010), it can be assumed that individuals in the opposite edge
of overconfidence scale are subject to status quo bias.
Madrian and Shea (2001) find that men prefer default plan to a lesser degree than women and default
contribution rate declines significantly with compensation. These findings imply that women may have
higher degree of status quo bias than men and more sophisticated/experienced individuals have
lower degree of status quo bias.
We also expect that women exhibit higher degree of status quo bias than men.
We hypothesize that sophisticated individuals exhibit status quo bias to a lesser degree than less
sophisticated investors.
3. Data and Methodology
3.1. Data
The analysis is based on Turkish individual stock investors. The main data set consist of all buy and
sell transactions as well as monthly stock only and total portfolio positions (stock, funds, private sector
bonds and warrants) of whole Turkish individual investors in 2011. The second data set consists of
demographic and other information of these investors (age, gender, geographical region of residence,
account open date). Pursuant to the permission of Capital Markets Board (CMB) and Istanbul Stock
Exchange (ISE), analysis on these data sets has been conducted on Central Registry Agency (CRA)
servers due to privacy restrictions. 2011 stock market performance is slightly bearish. ISE100 index,
which consists of the largest 100 companies, decreased from 67,608 at the beginning of year to
51,267. However, out of 253 trading days, ISE100 index had positive returns at 129 days and
negative returns at 124 days.
13 WORKING PAPER SERIES N. 42 - MAY 2013 ■
According to CRA monthly statistics as of 2011 January, total number of Turkish individual stock
investors is around 1 million. However a significant portion of these investors is either dormant or
have very low stock portfolio value. When data set is limited to individual stock investors whose total
stock portfolio in any month in 2011 is above 5,000 TL (approximately US$ 3000), number of
investors reduces to 432,085. Of these, 74,051 investors do not have any buy or sell transactions
(dormant investors) during the entire year. Dormant investors are mostly at high ages as 75% of them
older than 50 and are in the stock market for a long period of time. 66% opened their accounts before
2002. Female investors constitute 41% of the dormant investors and 18% of the active investors. In
order not to distort overall results, these investors are excluded from the analysis, reducing number of
investors to 358,034 (labeled as expanded investor set).
Table 1 shows that total trading volume of these investors is 518.6 billion TL (buy and sell amounts
divided by two), 76% of total trading volume in ISE in 2011, indicating that the sample has significant
influence on price formation in the stock market. 15% of remaining trading volume is attributable
foreign investors and rest (9%) is attributable to low portfolio value Turkish individual stock investors
(investors with 2011 monthly average stock portfolios lower than 5,000 TL) and local institutional
investors.
Some of these investors do not have any buy or sell transaction. Hence, data set is further limited to
those investors who have at least 1 buy and 1 sell transaction, reducing data set to 305,546 investors.
However, a portion of these investors have very high annual turnover values such as 50,000 and
even increasing to 10 billion levels for a few investors. One possible explanation is that these
investors (labeled as abnormally high turnover investors) have their wealth managed by professional
money managers and / or they act like day traders and scalpers. As it seems that they have different
investment characteristics, in order not to distort overall analysis and use same sample for all biases /
proxies, these investors are also excluded from further analysis. Using trial and error and comparing
with international benchmarks, high annual turnover cut off point is set to be 100. Although this cut off
can be increased (up to 10,000) or decreased, back of the envelope calculations reveal that overall
results do not change significantly. Capping turnover at 100, sample size decreases to 244,146
investor (labeled as analysis investor set) with exclusion of 61,400 abnormally high turnover investors.
This data set is used as the data set for detailed analysis. Although analysis investor set is
significantly reduced, majority of the results are also confirmed using the expanded data set with
358,034 investors.
Table 1 shows that total trading volume of the investors is 147.9 billion TL (average of buy and sell
amounts), which is 22% of the total trading volume in ISE in 2011. The investors made 31.9 million
buy transactions amounting to 149 billion TL and 31.7 million sell transactions amounting to 146.6
billion TL. Average buy volume is 4,674 TL, slightly higher than average sell volume of 4,621 TL.
14 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Demographic break down of 244,146 investors are presented in Table 2. Due to confidentiality
reasons, CRA provided categorized data for age, experience and wealth. Age is the age of investors
as of 2011. Wealth is the average of 12 end of month portfolios consisting of equity, funds, warrants
and corporate bonds. Experience is the date the investor opens the account (if more than one
accounts available, opening date of oldest account taken into account). Region is the geographical
region of residence of investor registered in CRA database.
Male investors constitute 83% of the total investor base. 30-55 age groups constitute 76% of all
investors. 76% of the investors have average 2011 wealth of 50,000 TL or less. 90% of the investors
have 3 or more years of investment experience in stock exchange. Almost half of the investors (45%)
reside in Marmara Region, mostly in Istanbul, which is the largest city in Turkey. Next is Central
Anatolia with 17%, probably mostly Ankara, which is the 2nd largest city. 15% is in Aegean, in İzmir,
the 3rd largest city. Marmara region is the most developed and Southeast Anatolia region is the least
developed among the regions in terms of welfare, income, education, etc.
Demographics of abnormally high turnover investors (61,400 investor with turnover higher than 100)
are slightly different. Compared to analysis investor set, abnormally high turnover investors are mostly
male (88% versus 83%), younger (investors up to 35 years old are 21% versus 27%), not as wealthy1
(investors with wealth up to 10,000 TL - approximately US $6,000 - are 41% versus 34%) and more
experienced (account open date 2002 or earlier 49% versus 36%). There is no difference in terms of
region of residence.
However, as expected, investors with abnormally high turnover have significantly more buy and sell
transactions than those in analysis data set. Number of buy trade higher than 1,000 at 12% (versus
2% in analysis investor set), total value of buy trades higher than 1.5m TL (approximately US$ 800K-
850K) at 20% (versus 6% in analysis investor set), number of sell trade higher than 1,000 at 12%
(versus 2% in analysis investor set) and total value of sell trades higher than 1.5m TL at 20% (versus
6% in analysis investor set).
3.2. Methodology
Using a theoretical model, Harris and Raviv (1993) show that, differences in opinions lead to trading
among investors. Hence, trading volume is related to different expectations among investors.
Differences in opinions are result of different interpretation of same signal by investors. As they rely
on their beliefs and decisions more, overconfident investor’s interpretation of the same signal will
significantly differ compared to rational investors. This difference should cause increased trading
volume for overconfident investors. De Bondt and Thaler (1995) state that the key behavioral factor to
understand trading puzzle is overconfidence. Kyle and Wang (1997) and Benos (1998) argue that
overconfident investors trade more aggressively as they believe that they have better information.
1 GDP per capita in Turkey is USD 10,469 in 2011
15 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Kahneman and Riepe (1998) propose that overconfidence should be expected from those who do not
face similar problems every day, make explicitly probabilistic estimates and obtain feedback on
outcomes of their decisions, implying that individual stock investors are likely to be overconfident.
Odean (1998) develops a theoretical model in which overconfident investors overestimate the
precision of their knowledge about the value of an asset. These investors overestimate the probability
that their personal assessment of an asset’s value is more accurate than that of others. Thus,
overconfident investors believe their valuations more which increases the differences in opinions
among individual investors. The author proposes that that trading volume increases when investors
are overconfident. Odean (1999) tests this hypothesis using data provided by a nationwide discount
brokerage house in US. He argues that if traders are overconfident in precision of information, then
average return of securities they sell must outperform average return of securities they buy. He finds
that average return of securities sold outperform average return of securities purchased over horizons
of four months, one year and two years. The author looks for possible explanations to excessive
trading resulting in losses and eliminates meeting liquidity needs, realizing tax losses and rebalancing
the portfolio or moving to a less risky portfolio. He concludes that excessive trading resulting in losses
may be due to overconfidence. Barber and Odean (1999), Barber and Odean (2000), and Hirshleifer
and Luo (2001), Gervais and Odean (2001), Barber and Odean (2001), Barber and Odean (2002),
Chuang and Lee (2006), Statman et al. (2006), Glaser and Weber (2007), Graham et al. (2009),
Glaser and Weber (2009), Grinblatt and Keloharju (2009), Hoffmann et al. (2010) also confirm that
overconfident investors trade more.
Barber and Odean (2001) define monthly portfolio turnover as one-half the monthly sales turnover
plus one-half the monthly purchase turnover. The monthly sales turnover is calculated as the shares
sold in month t times beginning of month price divided by total beginning of month t market value of
household’s portfolio. The monthly purchase turnover is calculated as the shares purchased in month
t-1 times beginning of month t price divided by total beginning of month t market value of household’s
portfolio. Annual turnover is simply twelve times monthly turnover. Similar to Barber and Odean
(2001), we measure overconfidence as annual turnover. Higher turnover implies higher
overconfidence. Since both theoretical and empirical findings for turnover are robust, it is used as the
main proxy to measure overconfidence while others are used for robustness checks.
Josephs et al. (1992) argue that low self esteem individuals take less risk than individuals high in self
esteem. As Campbell (1990) shows, high self-esteem people have higher confidence. Hence, it can
be inferred that overconfident investors tend to take more risk. Chuang and Lee (2006) find that
overconfident investors trade more in riskier securities. They measure riskiness of a security as return
volatility and firm specific risk (return volatility minus market component). Glaser and Weber (2009)
also find that individuals buy high risk stocks. These findings imply that overconfidence can also be
measured by using portfolio riskiness. Consistently, percentage of stocks from ISE 30 (as these
stocks have high market capitalization and high liquidity, they are assumed to be less risky) and
percentage of small stocks in the portfolio (assuming smaller firms are riskier) are used as proxies of
16 WORKING PAPER SERIES N. 42 - MAY 2013 ■
portfolio riskiness in this study. For all month ends, number of different stocks from ISE 30 divided by
total number of different stocks in the portfolio is calculated. Average of 12 months (ISE30 ratio) is
used to measure portfolio riskiness. The lower the percentage, the riskier the portfolio is. For
example, suppose a portfolio consists of stocks A, B and C and suppose A and B are in ISE 30. For
this portfolio, ISE 30 ratio is calculated to be 67%.
Likewise, for all month ends, number of different stocks labeled as small based on market
capitalization divided by total different number of stocks is calculated. Average of 12 months (small
Mcap ratio) is used to measure portfolio riskiness. The higher the percentage, the riskier the portfolio
is. Firms with market capitalization lower than USD 100m are labeled as small. As of 2011 year end,
almost 50% of stocks have Mcap lower than USD 100m. Maximum Mcap is USD 13,119m.
Using 2009-2011 return data, we found that volatility of small stocks is on average larger than rest of
the stocks. Besides, average volatility of stocks in ISE30 is smaller than rest of the stocks. Hence,
taking also return volatility into account, ISE30 stocks turn out to be less risky and small stocks are
more risky as expected.
Heath and Tversky (1991) argue that as explained by competence hypothesis, overconfident
investors may forego the advantage of diversification and concentrate on a small number of
companies with which they are more familiar with. Odean (1998) finds that overconfident traders hold
under-diversified portfolios. Goetzmann and Kumar (2008) find that high portfolio turnover, which is a
sign of overconfidence is related to under-diversification. According to authors, this finding implies the
more overconfident investors hold under-diversified portfolios along with investors with a tendency in
local stocks (familiarity bias). Glaser and Weber (2009) argue that, with increased portfolio turnover,
individuals reduce number of stocks in their portfolio. These findings imply that overconfidence can be
measured using diversification. In line with literature, average number of stocks in the portfolio is used
as a naïve way of measuring diversification level.
Odean (1999) suggests that securities that have performed unusually poor or well are more likely to
be discussed in the media, more likely to be considered by individual investors and as a result more
likely to be purchased. He finds that the investors tend to buy securities that have risen or fallen more
over the previous six months than the securities they sell. Gervais et al. (2001) find that stocks
experiencing high trading volume over a day or week tend to appreciate over the following month. The
findings imply that shocks to trading activity increase a stock’s visibility and demand in the upcoming
days increase. Hirshleifer et al. (2008) use transaction data of individual investors from a brokerage
house and find that investors are net buyers after both negative and positive extreme earnings
surprises, consistent with an attention effect. This can be interpreted as stocks with extreme positive
or negative earnings grab attention of investors, whose familiarity towards these stocks increase and
tendency to invest in these stocks increase. Barber and Odean (2008) argue that buying behavior of
individual investors is heavily influenced by stocks that draw their attention. Authors use stock news in
17 WORKING PAPER SERIES N. 42 - MAY 2013 ■
the media, unusual trading volume and extreme returns as proxies for attention grabbing factors. The
authors find that abnormal trading volume is the best indicator of attention while return and news
metric follow abnormal trading volume.
Findings imply that familiarity bias can be measured by looking at the relation between stock
purchases and factors increasing familiarity towards these stocks. The more the investor is exposed
to the stock, the more familiar he or she becomes.
From this standpoint, previous ownership is expected to be a good measure for familiarity bias. After
an investor buys a stock, it becomes more familiar. Following this argument, all purchase transactions
are flagged if the stock has been purchased previously in 2011. Number of flagged purchase
transactions divided by total number of purchase transactions is used as a proxy (previous ownership
ratio) to measure familiarity bias. Higher previous ownership ratio indicates higher familiarity bias.
Previous ownership ratio is used as the primary proxy to measure familiarity bias as it is the most
direct indicator of familiarity towards a stock whereas others will be used for robustness checks.
Similar to Barber and Odean (2008), extreme return can also be used to measure familiarity bias.
Number of stock purchase transactions with absolute abnormal return (positive or negative) divided
by total number of stock purchase transactions (absolute abnormal return ratio) is used as a proxy to
measure familiarity bias. Higher ratio indicates higher familiarity bias. A purchase transaction is
counted to have absolute abnormal return if absolute value of previous day return of stock divided by
previous day ISE100 (index composed of largest 100 companies in ISE) return is above 125%. This
cut off point is determined based on the absolute return of stocks and ISE100. In 2011, average of
mean absolute return of stocks is 2.03% whereas mean absolute return of ISE100 is 1.27%. On
average, 123 days of 253 trading days, stocks' absolute return is higher than 125% of ISE100
absolute return (minimum 0 days, maximum 182 days).
As presented in Barber and Odean (2008), unusual trading volume can also be used to measure
familiarity bias. Number of stock purchase transactions with abnormal volume change divided by total
number of stock purchase transactions (abnormal volume ratio) is used as a proxy to measure
familiarity bias. Higher ratio indicates higher familiarity bias. A purchase transaction is counted to
have absolute volume change if value of previous day volume change (versus 2 days ago) of stock
divided by previous day ISE100 volume change (versus 2 days ago) is above 150%.
As proposed by Merton (1987), analyst coverage can be used as another proxy to measure familiarity
bias. It has been hypothesized that the more analyst covers a stock, the more likely that it will grab
attention of investors. Hence, average number of analysts covering stocks purchased can be used to
measure familiarity bias. Higher analyst coverage indicates higher familiarity bias.
18 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Chan et al. (2004) argue that representativeness heuristic may lead investors to extrapolate past
performance of assets into the future and thus, set prices too low or too high which in turn generates
return reversals. This argument implies that representativeness heuristic can be measured by using
the relation between stock purchases and recent past performance of stocks. Chen et al. (2007) use
transaction data of a large Chinese brokerage house to analyze representativeness heuristic in
Chinese investors. The authors use extrapolation as a form of representativeness heuristic. They find
that 4 month prior performance of stocks purchased is surprisingly high whereas past 1-year return is
almost normal. This finding indicates that investors extrapolate recent past returns of stocks they
purchase. Barber et al. (2009) use extrapolation as a form of representativeness heuristic and
measure 3 year prior market adjusted return of stocks purchased by individual investors. The authors
find that, individual investors buy stocks with strong past returns. This relation peaks in 1-year prior to
purchase and lasts till 3 years prior to purchase.
Hence, as presented in Chen et al. (2007) and Barber et al. (2009), representativeness heuristic can
be measured by the degree to which investors make their buy decisions according to recent past
trend of stock prices. Chen uses prior 4-month and 1-year returns. Barber et al. (2009) finds that
representativeness heuristic peaks with 1 year prior returns and diminishes in the longer periods. As
stated in Bildik and Gülay (2007), Turkish individual stock investors are more myopic. Hence, we
employ shorter time period. For each buy transaction, positive return trend is calculated to be number
of positive returns in last 90 trading days prior to purchase date divided by 90. For each investor,
representativeness heuristic is measured as average positive return trend for all purchases.
Representativeness heuristic is also measured for last 30 as well as 150 trading days before
purchase date using the same calculation methodology.
The status quo bias is related to doing nothing or maintaining current decisions, implying that status
quo bias involves reluctance to trade. Hence, individuals exhibiting status quo bias are expected to
keep their current portfolios unchanged. The more the portfolio of an individual changes, the more
decisions he/she has given implying lesser degree of status quo bias. Using all buy and sell
transactions in 2011, end of day portfolios for each investor are formed. Average percentage of
change in number of stocks in the portfolios for each day is used to measure status quo bias (portfolio
percentage change). The higher the portfolio percentage change, the lower the status quo bias. For
example, suppose in day 1, portfolio consists of 2 A and 4 B stocks and suppose in day 2, portfolio
consists of 4 A, 2 B and 2 C stocks, daily percentage change in the portfolio is 67% (50% change in
A, 50% change in B and 100% change in C divided by 3 representing number of stocks A, B, C).
Correlation of proxies for each bias is presented in Table 3. Turnover is negatively correlated to ISE30
ratio and diversification and positively correlated to small stock ratio. Small stock ratio is by definition
negatively correlated to ISE30 ratio and not correlated to diversification. Correlation of diversification
with other overconfidence proxies is either low or insignificant. Similarly, correlation among familiarity
19 WORKING PAPER SERIES N. 42 - MAY 2013 ■
bias proxies is either insignificant or too low. All correlations among representativeness heuristic are
statistically significant, positive and high.
For all proxies, using histograms and descriptive statistics, level of prevalence of each bias among
Turkish individual stock investors is assessed. Then regression analysis is conducted to determine
how each demographic factor affects behavioral biases taking others into account.
In this regression model, bias(es) are overconfidence, familiarity bias, representativeness heuristic
and status quo bias. Age is the age of investor and is a continuous variable. Male is a dummy
variable, which equals one for male investors. Experience is the date account is opened and is a
continuous variable. is a dummy variable, which equals one for wealth levels up to
10,000 TL and is a dummy variable and is equal to one for wealth levels higher than
100,000 TL. Marmara is a dummy variable, which equals one for Marmara (most developed) region
and Southeast is a dummy variable, which equals one for Southeast (least developed) region.
Coefficients are expected change sign between and and between
Marmara and Southeast.
Turnover, previous ownership ratio, 90 day positive return trend and portfolio percentage change are
used as main measures of overconfidence, familiarity bias, representativeness heuristic and status
quo bias respectively. Other proxies such as ISE30 ratio, small Mcap ratio, and absolute abnormal
return ratio are also used for robustness checks.
Since explanatory variables are categorical, three additional regression models have been utilized for
robustness checks. In these models, wealth is a dummy which equals one either for each wealth level
presented in Table 2 or for low and high wealth levels presented above. In these models, experience
is either continuous or is a dummy variable which equals one for each experience level presented in
Table 2.
20 WORKING PAPER SERIES N. 42 - MAY 2013 ■
4. Results
4.1. Overconfidence
i. Turnover
Turkish individual stock investors have high turnover. As presented in Table 4, on average, an
investor shifts his or her portfolio 11 times annually which is high compared to similar studies. When
we compute the mean annual turnover including those with turnover higher than 100, mean turnover
increases to 1.15 million mainly due to a small set of investors (around 4, 000 investors) whose
turnover is above 1 million, which is extremely high for a typical individual investor. Both standard
deviation presented in Table 4 and histogram in Figure 1 confirm that turnover level is highly
dispersed.
Barber and Odean (2001) find that for a sub sample of US investors, mean turnover ratio is 0.77 for
men and 0.53 for women, implying that Turkish individual stock investors have higher turnover than
US investors. Chen et al. (2007) find that for Chinese investors, mean turnover is 3.27, significantly
higher than US investors, yet still lower than Turkish investors. Taking into account abnormally high
turnover investors and international benchmarks, it can be stated that overconfidence is common
among Turkish individual stock investors.
Table 5 shows that turnover is higher for male investors. Age is nonlinearly related to turnover,
increasing up to 30-34 age group, decreasing afterwards. Turnover decreases with wealth with only
exception at the lowest wealth group which has 2nd lowest turnover. This is probably mainly due to
low available funds to trade. Investors with annual buy and sell amounts up to 30,000TL constitute
66% of the lowest wealth group investors, reducing to ~52% for second lowest wealth group and
further decreasing to ~30% for all investors excluding lowest wealth group. This finding shows that
lowest wealth group investors buy and sell low amount of stocks, implying lower overconfidence.
Investors in Marmara region have lowest turnover and investors in Southeast Anatolia region have
highest turnover.
21 WORKING PAPER SERIES N. 42 - MAY 2013 ■
ii. Robustness Checks
1. ISE30 Ratio
ISE30 stocks constitute 30% of the mean investor portfolio as presented in Table 13. This seems to
be high and inconsistent with the overconfidence hypothesis. However as presented in Figure 5,
dispersion of ISE30 ratio indicates that 60,369 investor (25% of total investor base) do not have
ISE30 stocks in their portfolios and 107,616 (44% of total investor base) have 10% or less ISE30
stocks in their portfolios. Average diversification level of investors with 10% or less ISE30 stock in
their portfolios is 2.71. Number of investors who have only ISE30 stocks in their portfolios is very low
at 13,786 (6% of total investor base). These figures reveal that a significant portion of investors have
no or very low level of ISE30 stock in their portfolios. This finding supports the hypothesis that a
significant portion of investors prefers riskier stocks.
Table 14 shows that ISE30 ratio is lower for male investors. Age is nonlinearly related to ISE30 ratio,
decreasing up to 35-39 age group, increasing afterwards. ISE30 ratio increases with wealth and
experience. Investors in Marmara region have highest ISE30 ratio and investors in East Anatolia and
Southeast Anatolia region have lowest ISE30 ratio.
2. Small Mcap Ratio
Stocks with Mcap lower than US $100 million are labeled as small Mcap. As presented in Table 132,
on average, small stocks constitute 28% of investor portfolios. Although mean small stock ratio is not
very high, histogram in Figure 6 shows that there is high amount of investors holding small stocks.
68,361 investors do not have any small stock in their portfolios (75% of these 68,361 investors have
on average only 1 or less stock in their portfolios). 54,009 investors (22% of total investors) have 50%
or higher small stock ratio in their portfolios. Besides 6,346 (3% of total investors) have only small
stocks in their portfolios (with mean diversification of 0.73). These figures reveal that a significant
portion of investors have high level of small stocks in their portfolios.
Table 15 shows that small Mcap ratio is higher for male investors. Age is nonlinearly related to small
Mcap ratio, increasing up to 35-39 age group, decreasing afterwards. Small Mcap ratio decreases
with wealth and experience. Investors in Marmara region have lowest small Mcap ratio and investors
in Southeast Anatolia region have highest small Mcap ratio.
2 82 investors purchased only stocks with new ISIN code for existing stocks (due to reasons such as stock splits etc.), hence analysis based on
244,064 investors
22 WORKING PAPER SERIES N. 42 - MAY 2013 ■
3. Diversification
On average, investors diversify their portfolios with 3.43 stocks, as presented in Table 13. The median
investor holds 2 stocks. Chen et al. (2007) find that for Chinese individual investors, mean
diversification is 2.6, lower than Turkish individual investors. Goetzman and Kumar (2008) find that
mean diversification in US investors is in the range of 4.3-6.3, with a monotonic increase between
1991 and 1996, which is by far higher compared to Turkish investors. Barber and Odean (2000)
median investor holds 2.61 stocks for the same data set, higher than Turkish investors. 48% of
investors hold two or lower number of stocks in their portfolios indicating that a majority of investors
do not diversify their portfolios. Both standard deviation and histogram in Figure 7 show that
diversification is widely dispersed.
Table 16 shows that diversification is lower for male investors. Age is nonlinearly related to
diversification, decreasing up to 25-29 age group, increasing afterwards. Diversification increases with
wealth and experience. Investors in Marmara region have highest diversification and investors in
Southeast Anatolia region have lowest diversification.
Table 3 displays that turnover is negatively correlated to ISE30 ratio and diversification and positively
correlated to small stock ratio. Small stock ratio is by definition negatively correlated to ISE30 ratio
and not correlated to diversification. Correlation of diversification with other proxies is either low or
insignificant, implying that diversification is not as good as other proxies to measure overconfidence
or not necessarily measuring overconfidence.
Hence, further analysis for overconfidence robustness checks is based on portfolio riskiness
(measured by ISE30 ratio and small stock ratio).
iii. Regression Results
Results are presented in Table 6. As expected, overconfidence decreases with age. Male investors
are more overconfident than female investors, which confirm the vast majority of findings in literature.
Experience increases overconfidence contrary to expectations. However, this finding is valid only for
low wealth investors. Experience decreases overconfidence for high wealth investors. Hence, it is
probable that experience per se is not related to overconfidence and possible interactions with other
factors should be factored in the analysis. Another possible explanation is the definition of experience.
Account opening date does not necessarily imply high experience. An investor may gain experience
in a shorter period of time with high frequency trading. Hence, a better measure for experience is
needed to better understand the relation between experience and overconfidence. Wealth decreases
overconfidence. Wealth may be related to financial sophistication as wealthier investors have better
access to information and can leverage on professional portfolio management. Investors in Marmara
region have lower and investors in Southeast Anatolia region have higher overconfidence. Turnover
difference between regions is not related to gender, age, experience or wealth. Marmara region is
23 WORKING PAPER SERIES N. 42 - MAY 2013 ■
economically more developed than Southeast Anatolia region. Besides, percentage of university
graduates is higher in Marmara region (13% versus 6% in Southeast region)3. These two factors
indicate that financial literacy in Marmara region is most probably higher than that of in Southeast
Anatolia region, implying that increase in financial literacy decreases overconfidence. Wealth and
regional results imply that sophisticated investors are less prone to overconfidence.
Regression results are confirmed for sub samples (male only, female only, low / high age, low / high
experience, low / high wealth regressions). Our findings are also robust to different regression models
and different proxies, results of which are presented in Tables 17-20. Although not presented here,
results do not change for ISE30 and small Mcap regressions when data set is expanded to 358,034
investors.
Although our findings are robust to different measures and models, excluding return data from the
analysis imposes a limitation as high turnover does not necessarily imply overconfidence. Lower
returns should accompany turnover to confirm overconfidence. Yet, as Barber and Odean (2000) and
Barber et al. (2009) show, individual investors have poor trading performance. Besides, ISE30 and
small Mcap results which are independent of return data, confirm turnover results. These two factors
mitigate the limitation imposed by lack of return data.
4.2. Familiarity Bias
i. Previous Ownership
As presented in Table 4, our findings demonstrate that almost 50% of the stocks purchased by the
investors in 2012 have also been previously purchased by the same investors in 2011. Histogram in
Figure 2 shows that 42% of investors have 50% or lower previous ownership ratio. 32,628 (13% of
investor base) investor purchased stocks which they did not purchase in 2011 previously. However, of
these 32,628 investors, 71% made 1 to 5 purchase transactions, which shows that previous
ownership ratio for lower end of histogram should be read carefully. These findings imply that a
significant amount of investors purchase stocks that they are familiar with through previous
ownership.
Table 7 shows that previous ownership ratio is higher for male investors. Age is nonlinearly related to
previous ownership ratio, increasing up to 35-39 age group, decreasing afterwards. Previous
ownership ratio increases with wealth and experience. Investors in Marmara region have lowest
previous ownership ratio along with Black Sea and Aegean regions and investors in Southeast
Anatolia region have highest previous ownership ratio.
ii. Robustness Checks
3 Based on Turkish Statistical Institute data
24 WORKING PAPER SERIES N. 42 - MAY 2013 ■
1. Absolute Abnormal Return
Absolute abnormal return ratio is simply number of stock purchase transactions with absolute
abnormal return divided by total number of stock purchase transactions where abnormal return is
defined to be returns higher than 125% of ISE100 return. Our findings presented in Table 13 shows
that on average 59% of all purchase transactions has abnormal previous day absolute abnormal
return. Barber and Odean (2008) find that individual investor attention display attention-driven buying
behavior and net buyers of extreme negative and positive one day return stocks, which is in line with
our finding. 9,890 investors (4% of investors) have only purchased stocks with no previous day
abnormal return. However, of these 9,890 investors, 81% made 1 to 5 purchase transactions.
Besides, histogram in Figure 8 shows that 79% of investors have higher than 0.5 absolute abnormal
return ratio. These figures along with mean ratio show that a significant portion of investors buys
stocks which have high absolute abnormal previous day return.
Table 21 shows that absolute abnormal return ratio is higher for male investors. Age is nonlinearly
related to absolute abnormal return ratio, increasing up to 25-29 age group, decreasing afterwards.
Absolute abnormal return ratio decreases with wealth and experience. Investors in Marmara region
have lowest absolute abnormal return ratio and investors in Southeast Anatolia region have highest
absolute abnormal return ratio along with Black Sea and East Anatolia regions.
2. Abnormal Volume
We find that on average 42% of all purchase transactions have abnormal volume ratio, as presented
in Table 11. Histogram in Figure 9 shows that 29% of investors have 0.5 or higher abnormal volume
ratio. 77% of investors are concentrated in 0.2-0.6 region. Both descriptive statistics and histogram
shows that a significant portion of investors buy stocks who have previous day abnormal volume.
Table 22 shows that abnormal volume ratio is lower for male investors. Abnormal volume ratio
increases with age, wealth and experience. Investors in East Anatolia region have lowest abnormal
volume ratio and investors in Aegean region have highest previous day abnormal volume ratio, yet
means are not statistically different between any of the regions.
3. Analyst Coverage
We also analyze the analyst coverage of the stocks purchased by investors in our sample. Analyst
coverage data is obtained from Bloomberg. As presented in Table 11, our findings show that
maximum number of analysts covering a stock is 29 (DOCO) and minimum number of analyst
covering a stock is 0 (for 195 stocks). 152 of these 195 stocks have small MCap (lower than USD
100m). Besides, correlation between analyst coverage and size is 0.588, statistically significant and
high. These findings indicate that analysts are covering larger stocks as expected.
25 WORKING PAPER SERIES N. 42 - MAY 2013 ■
On average, stocks that were purchased by investors have been covered by 7.7 analysts. Histogram
in Figure 10 shows that, 10,315 (4% of investors) purchased stocks which have not been covered by
any analyst. However, of these 10,315 investors, 48% have made 5 or less purchase transactions.
29% of investors have purchased stocks covered by 10 or more analysts.
Table 23 shows that analyst coverage is lower for male investors. Age is nonlinearly related to analyst
coverage, decreasing up to 30-34 age group, increasing afterwards. Analyst coverage increases with
wealth and experience. Investors in Marmara region have highest analyst coverage and investors in
East Anatolia region have lowest analyst coverage followed by Southeast Anatolia region.
Familiarity bias may stem from any attention grabbing event, which are hard to capture with one
specific measure. Previous ownership is a direct indicator of familiarity. Hence, it is used for further
familiarity bias analysis. As a secondary measure, although not correlated much, absolute abnormal
return is used for robustness check as abnormal return changes is more likely to be attention
grabbing and seems more related to previous ownership compared to abnormal volume. Table 3
shows that correlation among proxies is either insignificant or too low.
Analyst coverage is low yet negatively correlated to previous ownership. Analyst coverage may be
increasing investor's information set about stocks and hence serve as a de-biasing tool rather than
triggering familiarity bias. Besides, messages shared with investors are important as negative
messages for a stock may lead investors refrain from the stock rather than purchasing it. Hence,
abnormal volume and analyst coverage are not used for further familiarity bias robustness checks.
iii. Regression Results
Results are presented in Table 8. As expected, familiarity bias decreases with age. Male investors
exhibit familiarity bias more than female investors. Experience increases familiarity bias contrary to
expectations. This is probably due to high correlation between age (0.458) and experience. When age
is omitted from the model, experience turns out to negatively and significantly affect familiarity bias.
Age confounds with experience as interaction term between age and experience is statistically
significant. Hence, age and experience may be measuring same underlying factor affecting familiarity.
Another possible explanation why experience positively affects familiarity bias is the definition of
experience. Account opening date does not necessarily imply high experience. An investor may gain
experience in a shorter period of time with high frequency trading.
Wealth increases familiarity bias contrary to expectations. Due to definition, previous ownership ratio
is positively correlated with number of buy transactions (0.607) which is also positively correlated with
wealth (0.304), leading to wealth positively affecting familiarity bias. When number of buy transactions
is added as a control variable, wealth turns out to negatively affect familiarity bias. Negative effect of
26 WORKING PAPER SERIES N. 42 - MAY 2013 ■
wealth on familiarity bias is also confirmed with absolute abnormal return regression analysis
presented in Table 24.
Investors in Marmara region have lower and investors in Southeast Anatolia region have higher
familiarity bias. Difference between these two regions is not related to gender, age, experience or
wealth. Marmara region is economically more developed than Southeast Anatolia region. Besides,
percentage of university graduates higher in Marmara region (13% versus 6% in Southeast Anatolia
region). Similar to findings in overconfidence, financial literacy decreases familiarity bias. Wealth and
region results imply that sophisticated investors are less prone to familiarity bias.
Regression results are confirmed for sub samples (male only, female only, low / high age, low / high
experience, low / high wealth regressions). Results are also fully confirmed for age, gender and
wealth and partially confirmed for experience and Southeast Anatolia region using different proxies
and regression models as presented in Tables 24-27. Although not presented here, results do not
change when data set is expanded to 358,034 investors.
Stock prices in buy transactions may affect familiarity bias, as investors’ perception to high price
stocks may be different than low price stocks. Hence omitting stock prices imposes a limitation on our
results.
Familiarity bias may also arise due to many different factors (investor being employee of the
company, investor living within proximity of the company, advertising & marketing efforts of the
company, word of mouth, stock specific or investor specific any other attention grabbing emotional or
rational factor). Hence, it is extremely difficult to find proxies to measure familiarity bias confirming
each other.
4.3. Representativeness Heuristic
Correlation among 30, 90 and 150 trading day positive return trends is presented in Table 3. All
correlations are statistically significant, positive and high. Hence, only results for 90 trading day
positive return trend are presented.
i. 90 Trading Day Positive Return Trend
Table 4 shows that on average, stocks purchased have positive returns 42% of the days in 90 trading
days prior to purchase.
Mean positive return trend is 43.2% for 30 trading days and 41.2% for 150 trading days, economically
not different from 90 trading day return trend, although statistically different. Histogram in Figure 3
shows that 72% of investors have purchased stocks whose returns in last 90 days prior to purchase
27 WORKING PAPER SERIES N. 42 - MAY 2013 ■
were positive between 40% and 50% of the time. ISE100 index is positive on 52.4% in last 90 trading
days for each trading day in 2011. These findings reveal that, investors are not very positive trend
chasers consistent with the findings of Bildik and Gülay (2007).
Table 9 shows that 90 trading day positive return trend is lower for male investors. Age is nonlinearly
related to 90 trading day positive return trend, decreasing up to 45-49 age group, increasing
afterwards. 90 trading day positive return trend increases with wealth (means are not significantly
different in lower wealth levels). 90 trading day positive return trend increases with experience
(decreasing for 30 day trend). Investors in Marmara region have highest 90 trading day positive return
trend and investors in Southeast Anatolia region have lowest 90 trading day positive return trend.
Although our findings are statistically significant, as means are very close to each other, they are not
economically significant.
ii. Regression Results
Results are presented in Table 10 show that representativeness heuristic increases with age. Male
investors exhibit representativeness heuristic less than female investors. Experience decreases
representativeness heuristic. Wealth increases representativeness heuristic. Investors in Marmara
region have higher and investors in Southeast Anatolia region have lower representativeness
heuristic. Difference between regions is not related to gender, age, experience or wealth.
Our findings are robust to different proxies and regression models as presented in Tables 28-31.
Although not presented here our results do not change when data set is expanded to 358,034
investors.
Relation between demographic factors and representativeness heuristic are just the opposite of
relation between overconfidence and familiarity bias, implying that proxies may not be measuring
representativeness heuristic. Besides, as means are not economically different from each other and
explanatory power of the regression models is very low, we strongly suggest using new measures in
further studies. Due to restrictions on access to data, it was not possible to perform further analysis.
Market adjusted 90 day positive return trends can be used to further analyze representativeness
heuristic. Additionally, consistent to findings of Bildik and Gülay (2007), Turkish individual investors
might be more myopic, implying that shorter time periods might perform better in explaining
representativeness heuristic.
28 WORKING PAPER SERIES N. 42 - MAY 2013 ■
4.3. Status Quo Bias
i. Portfolio Percentage Change
As presented in Table 4, mean daily portfolio change of investors is 4% (with a median of 2.17%).
Histogram in Figure 4 shows that 29% of investors (71,422) have daily portfolio change of 1% or less.
Excluding this group, daily portfolio change increases to 5.5%. Annual turnover is 2.96 for the lowest
daily portfolio change group whereas increasing to 34.27 for the highest daily portfolio change group
(10% of investors). Correlation between annual turnover and daily portfolio change is at 0.54, high
and statistically significant.
Table 11 shows that daily portfolio change is higher for male investors. Age is nonlinearly related to
daily portfolio change, increasing up to 25-29 age group, decreasing afterwards. Daily portfolio
change decreases with wealth and experience. Investors in Marmara region have lowest daily
portfolio change and investors in Southeast Anatolia region have daily portfolio change.
Average number of stocks investors have purchased or sold (stock pool) is 14.7 with a median of 9.0
(minimum is 1 and maximum is 393 stocks). Correlation between number of buys (sells) and stock
pool is 0.658 (0.645), statistically significant and high, indicating that the more an investor trades, the
more number of stocks he / she focuses on. However, 123,817 investors (50% of investors) have a
stock pool of 9 or less stocks. Hence, it can be inferred that investors tend to purchase and sell a very
limited number of stocks.
ii. Regression Results
Regression results are presented in Table 12. As expected, status quo bias increases with age. Male
investors exhibit status quo bias less than female investors. Experience and wealth increase status
quo bias. Investors in Marmara region have higher and investors in Southeast Anatolia region have
lower status quo bias. Difference between these two regions is not related to gender, age, experience
or wealth. Wealth and region results imply that sophisticated investors exhibit status quo bias more.
Results are totally consistent with overconfidence results.
Taking into account both regression results and descriptive statistics, it can be inferred that individuals
exhibiting status quo bias are in the opposite edge of overconfidence scale.
Regression results are confirmed for sub samples (male only, female only, low / high age, low / high
experience, low / high wealth and Marmara only and Southeast Anatolia only regressions). Our
findings are also robust to different regression models, results of which are presented in Tables 32-
34. Although not presented here, results do not change when data set is expanded to 358,034
investors. Results are also confirmed when percentage of active days (number of days buy or sell
29 WORKING PAPER SERIES N. 42 - MAY 2013 ■
transaction taking place divided by number of days account is open in 2011) and number of stocks
subject to buy and sell are taken into account.
Behavioral finance literature shows that trading is hazardous to wealth and suggests that investors
should not trade frequently. However, there is no consensus on optimum level of trading. Hence, too
little trading can be a bias as well. Although according to psychology literature status quo is a bias, it
needs to be related to trading performance in behavioral finance domain. Due to unavailability of data,
we are not able to show that too infrequent trading (status quo bias) is also hazardous to wealth,
imposing a limitation on our results.
5. Conclusion
Empirical studies in the behavioral finance literature find that individuals do not behave rationally. The
behavioral biases govern investor decisions and affect financial markets. However, these studies
mainly focus on US and Europe and are limited to the subsamples of the overall investor group in
these countries. In this study, we analyze how prevalent overconfidence, familiarity bias and
representativeness heuristic are among all the Turkish individual stock investors and how
demographic factors affect these biases using transaction and demographic data.
Overconfidence is highly common among Turkish individual stock investors. Turkish individual
investors are more overconfident compared to US individual investors. In line with literature, male are
more overconfident than female investors. Age and wealth decreases overconfidence. Investors in
financially high literate regions are less overconfident than those in financially low literate regions.
Wealth and region results imply that sophisticated investors are less prone to overconfidence. Results
are robust in terms of various subsamples, regression models and using different proxies. One
limitation to overconfidence results is that turnover data is not controlled for return. Yet, findings in
literature, more likelihood of individual investors to underperform and confirmation of findings with
different proxies (ISE30 ratio and small Mcap ratio measuring portfolio riskiness) mitigate this
limitation.
A significant portion of investors in Turkey exhibits familiarity bias. Male investors are more prone to
familiarity bias compared to female investors. Age and wealth decreases familiarity bias. Investors in
financially high literate regions are less prone to familiarity bias than those in financially low literate
regions. Wealth and region results imply that sophisticated investors are less prone to familiarity bias.
Results are robust in terms of various subsamples, regression models and using different proxies. It
should be kept in mind that it is extremely difficult to find a single proxy to measure familiarity bias as
it may arise due to many different factors.
30 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Turkish individual stock investors do not seem to be positive return chasers. Demographic factors
affect representativeness heuristic in just the opposite way they do overconfidence and familiarity
bias. Means of recent past positive return ratios of stocks purchased are not economically different
across different investor groups (male versus female, different age, wealth, experience groups and
region of residence). Explanatory power of regression models is also very low. Hence new measures
such as market adjusted return trends, shorter period return trends such as 10 day (to better
understand whether Turkish investors are more myopic) should be used before jumping to bold
conclusions.
Findings of status quo bias are totally consistent with overconfidence results and individuals exhibiting
status quo bias are in the opposite edge of overconfidence scale.
Although main data set used for analysis constitutes only 22% of total trading volume in ISE in 2011,
results are robust to different proxies and regression models as well as expanding the analysis to
include investors with abnormally high turnover (data set with 358,034 investors) which constitutes
76% of total trading volume in ISE in 2011. Hence, behavioral biases of investors used in this study
have significant impact on prices in stock market.
Although our findings confirm literature, Turkish individual stock investors have different
characteristics compared to US individual investors. The most pronounced difference is that Turkish
individual investors are more overconfident which significantly increases trading volume in Istanbul
Stock Exchange.
Further research should focus on new proxies to measure representativeness heuristic and familiarity
bias. Including disposition effect to the analysis would also help better profile Turkish individual stock
investors, which we are currently working on.
31 WORKING PAPER SERIES N. 42 - MAY 2013 ■
References
Acker, D., and Duck, N. G. (2008). Cross-cultural Overconfidence and Biased Self Attribution, Journal of Socio Economics, 37, 1815-1824.
Andreassen, P. B., and Kraus, S.J. (1990). Judgmental Extrapolation and the Salience of Change, Journal of Forecasting, 9, 347-372.
Barber, B. M., and Odean, T. (1999). The Courage of Misguided Convictions, Financial Analyst’s Journal, 55(6), 41-55.
Barber, B. M., and Odean, T. (2000). Trading is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors, Journal of Finance, 55 (2), 773-806.
Barber, B. M., and Odean, T. (2001). Boys Will be Boys: Gender, Overconfidence, and Common Stock Investment, Quarterly Journal of Economics, 116 (1), 261-292.
Barber, B. M., and Odean, T. (2002). Online Investors: Do the Slow Die First?, Review of Financial Studies, 15 (2), 455-487.
Barber, B. M., and Odean, T. (2008). All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors, Review of Financial Studies, 21(2), 785-818.
Barber, B. M., Odean, T., and Zheng, L. (2005). Out of Sight, Out of Mind: The Effects of Expenses on Mutual Fund Flows, Journal of Business, 78(6), 2095-2120.
Barber, B. M., Odean, T., and Zhu, N. (2009). Systematic Noise, Journal of Financial Markets, 12, 547-569.
Barber, B. M., Lee, Y., Liu, Y., and Odean, T. (2009). Just How Much Do Individual Investors Lose by Trading?, Review of Financial Studies, 22(2), 609-632.
Barberis, N., and Thaler, R. (2003). A Survey of Behavioral Finance, Handbook of the Economics of Finance, edited by G. M. Constantinides, M. Harris and R. Stulz, Chapter 18.
Barberis, N., Huang, M., and Santos, T. (2001). Prospect Theory and Asset Prices, Quarterly Journal of Economics, 116(1), 1-53.
Barberis, N., Shleifer, A., and Vishny, R. (1998). A Model of Investor Sentiment, Journal of Financial Economics, 49, 307-343. 24
Benartzi, S. (2001). Excessive Extrapolation and the Allocation of 401(k) Accounts to Company Stock, Journal of Finance, 56(5), 1747-1764.
Benos, A. V. (1998). Aggressiveness and Survival of Overconfident Traders, Journal of Financial Markets, 1, 353-383.
Bildik, R., and Gülay, G. (2007), Profitability of Contrarion Strategies: Evidence from the Istanbul Stock Exchange, 7(1-2), 61-87.
Black, F. (1986). Noise, Journal of Finance, 41(3), 529-543.
Campbell, J. D. (1990). Self-Esteem and Clarity of the Self-Concept, Journal of Personality and Social Psychology, 59(3), 538-549.
32 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Chan, W. S., Frankel, R., and Kothari, S. P. (2004). Testing Behavioral Finance Theories Using Trends and Consistency in Financial Performance, Journal of Accounting and Performance, 38, 3-50.
Chen, G., Kim, K. A., Nofsinger, J. R, and Rui, O. M. (2007). Trading Performance, Disposition Effect, Overconfidence, Representativeness Bias, and Experience of Emerging Market Investors, Journal of Behavioral Decision Making, 20, 425-451.
Chuang, W., and Lee, B. S. (2006). An Empirical Evaluation of the Overconfidence Hypothesis, Journal of Banking and Finance, 30, 2489-2515.
Coval, J. D., and Moskowitz, T. J. (1999). Home Bias at Home: Local Equity Preferences in Domestic Portfolios, Journal of Finance, 54(6), 2045-2073.
Coval, J. D., and Moskowitz, T. J. (2001). The Geography of Investment: Informed Trading and Asset Prices, Journal of Political Economy, 109(4), 811-841.
Daniel, K., Hirshleifer, D., and Subrahmanyam, A. (1998). Investor Psychology and Security Market Under and Over Reaction, Journal of Finance, 53(6), 1839-1885.
Daniel, K., Hirshleifer, D., and Teoh, S.H. (2002). Investor Psychology in Capital Markets: Evidence and Policy Implications, Journal of Monetary Economics, 49, 139-209.
De Bondt, J. B., and Thaler, R. (1995). Financial Decision Making in Markets and Firms: A Behavior Perspective. In: Jarrow, R.A., Maksimovic, V., Ziemba W.T. (Eds.), Handbooks of Operations Research and Management Science, Vol. 9, Finance, pp, 383-410
De Long, J. B., Shleifer, A., Summers, L. H., and Waldmann, R. J. (1990). Noise Trader Risk in Financial Markets, Journal of Political Economy, 98(4), 703-738.
Deaves, R., Lüders, E., and Schröder, M. (2010). The Dynamics of Overconfidence: Evidence from Stock Market Forecasters, Journal of Economic Behavior and Organization, 75. 402-412.
Ekholm, A. and Pasternack, D. (2007). Overconfidence and Investor Size, European Financial Management, 14(1), 82-98. 25
Fan, J. X., and Xiao, J. J. (2005). A Cross-cultural Study in Risk Tolerance: Comparing Chinese and Americans, SSRN Paper No: 939438.
Fischoff, B, Slovic, P. and Lichtenstein, S. (1977). Knowing with Certainty: The Appropriateness of Extreme Confidence, Journal of Experimental Psychology, 3(4), 552-564.
Fox, C. R., and Tversky, A. (1995). Ambiguity Aversion and Comparative Ignorance, Quarterly Journal of Economics, 110(3), 585-603.
Gervais, S., and Odean, T. (2001). Learning to be Overconfident, Review of Financial Studies, 14 (1), 1-27.
Gervais, S., Kaniel, R., and Mingelgrin, D. H. (2001). The High Volume Return Premium, Journal of Finance, 56(3), 877-919.
Glaser, M., and Weber, M. (2007). Overconfidence and Trading Volume, Geneva Risk Insurance Review, 32, 1-36.
Glaser, M., and Weber, M. (2009). Which Past Returns Affect Trading Volume, Journal of Financial Markets, 12, 1-31.
33 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Goetzmann, W. N., and Kumar, A. (2008). Equity Portfolio Diversification, Review of Finance, 12, 433-463.
Graham, J. R., Harvey, C.R., and Huang, H. (2009). Investor Competence, Trading Frequency and Home Bias, 55(7), 1094-1106.
Grether, D. M. (1980). Bayes Rule as a Descriptive Model: The Representativeness Heuristic, Quarterly Journal of Economics, 95(3), 537-557.
Griffin, D., and Tversky, A. (1992). The Weighing of Evidence and Determinants of Confidence, Cognitive Psychology, 24, 411-435.
Grinblatt, M., and Keloharju, M. (2001). How Distance, Language, and Culture Influence Stockholdings and Trades, Journal of Finance, 56(3), 1053-1073.
Grinblatt, M., and Keloharju, M. (2009). Sensation Seeking, Overconfidence and Trading Activity, Journal of Finance, 64(2), 549-578.
Harris, M., and Raviv, A. (1993). Differences of Opinion Make a Horse Race, Review of Financial Studies, 6(3), 473-506.
Heath, C., and Tversky, A. (1991). Preference and Belief: Ambiguity and Competence in Choice under Uncertainty, Journal of Risk and Uncertainty, 4, 5-28.
Hirshleifer, D. (2001). Investor Psychology and Asset Pricing, Journal of Finance, 56(4), 1533-1597.
Hirshleifer, D. A., Myers, J. N., Myers, L. A., and Teoh A.H. (2008). Do Individual Investors Cause Post-Earnings Announcement Drift? Evidence From Personal Trades, Accounting Review, 83(6), 1521-1550. 26
Hirshleifer, D., and Luo, G. Y. (2001). On the Survival of Overconfident Traders In a Competitive Securities Market, Journal of Financial Markets, 4, 73-84.
Hoffmann, A.O.I., Shefrin, H., and Pennings, J. M. E. (2010). Behavioral Portfolio Analysis of Individual Investors, SSRN Working Paper No: 1629786.
Hofstede, G. (2001). Culture’s Consequences: Comparing Values, Behaviors, Institutions and Organizations across Nations, 2nd Edition, Sage Publications.
Huberman, G. (2001). Familiarity Breeds Investment, Review of Financial Studies, 14(3), 659-680.
Jain, P. C., and Wu, J. S. (2000). Truth in Mutual Fund Advertising: Evidence on Future Performance and Fund Flows, Journal of Finance, 55(2), 937-958.
Josephs, R. A., Larrick, R.P., Steele, C. M., and Nisbett, R.E. (1992). Protecting the Self from the Negative Consequences of Risky Decisions, Journal of Personality and Social Psychology, 62(1), 26-37.
Kahneman, D., and Riepe, M. W. (1998). Aspects of Investor Psychology, Journal of Portfolio Management, 24 (4), 52-65.
Kahneman, D., and Tversky, A., (1979). Prospect Theory: An Analysis of Decision Under Risk, Econometrica, 47(2), 263-291.
Kahneman, D., and Tversky, A. (1982). The Psychology of Preferences, Scientific American, 246, 167-173.
34 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Kahneman, D., Knetsch, J. L., and Thaler, R. H. (1991). Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias, Journal of Economic Perspectives, 5(1), 193-206.
Kunda, Z. (1987). Motivated Inference: Self-Serving Generation and Evaluation of Causal Theories, Journal of Personality and Social Psychology, 53(4), 636-647.
Kyle, A. S., and Wang, F. A. (1997). Speculation Duopoly with Agreement to Disagree: Can Overconfidence Survive the Market Test?, Journal of Finance, 52(5), 2073-2090.
Lakonishok, J., Shleifer, A., and Visnhy, R. W. (1994). Contrarian Investment, Extrapolation, and Risk, Journal of Finance, 49(5), 1541-1578.
Langer, E. J. (1975). The Illusion of Control, Journal of Personality and Social Psychology, 32(2), 311-328.
Madrian, B. C., and Shea, D. F. (2001). The Power of Inertia in 401(k) Participation and Savings Behavior, Quarterly Journal of Economics, 116(4), 1149-1187.
Massa, M., and Simonov A. (2006). Hedging, Familiarity and Portfolio Choice, Review of Financial Studies, 19(2), 633-685.
Merton, R. C. (1987). A Simple Model of Capital Market Equilibrium with Incomplete Infromation, Journal of Finance, 42(3), 483-510. 27
Miller, D. T., and Ross, M. (1975). Self-Serving Biases in the Attribution of Causality: Fact or Fiction?, Psychological Bulletin, 82(2), 213-225.
Odean, T. (1998). Volume, Volatility, Price and Profit When All Traders are Above Average, Journal of Finance, 53(6), 1887-1934.
Odean, T. (1999). Do Investors Trade Too Much?, American Economic Review, 89(5), 1279-1298.
Ritov, I., and Baron, J. (1992). Status Quo and Omission Biases, Journal of Risk and Uncertainty, 5, 49-61.
Ritov, I., and Baron, J. (1995). Outcome Knowledge, Regret and Omission Bias, Organizational Behavior and Human Decision Processes, 64, 119-127.
Russo, J. E., and Shoemaker, P. J. H. (1992). Managing Overconfidence, Sloan Management Review, 33(2), 7-17.
Samuelson, W., and Zeckhauser, R. (1988). Status Quo Bias in Decision Making, Journal of Risk and Uncertainty, 1, 7-59.
Shleifer, A., and Vishny, R. W. (1997). The Limits of Arbitrage, Journal of Finance, 52(1), 35-55.
Sirri, E. R., and Tufano, P. (1998). Costly Search and Mutual Fund Flows, Journal of Finance, 53(5), 1589-1622.
Statman, M. (2010). The Cultures of Risk Tolerance, SSRN Paper No: 1647086.
Statman, M., Thorley, S. and Vorkink, K. (2006). Investor Overconfidence and Trading Volume, Review of Financial Studies, 19(4), 1531-1565.
Subrahmanyam, A. (2007). Behavioral Finance: A Review and Synthesis, European Financial Management, 14(1), 12-29.
35 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Svenson, O. (1981). Are We All Less Risky and More Skillful Than Our Fellow Drivers?, Acta Psychologica, 47, 143-148.
Tversky, A., and Kahneman, D. (1971). Belief in Law of Small Numbers, Psychological Bulletin, 76(2), 105-110.
Tversky, A., and Kahneman, D. (1974). Judgment Under Uncertainty: Heuristics and Biases, Science, New Series, 185(4157), 1124-1131.
Tversky, A., and Shafir, E. (1992). Choice Under Conflict: The Dynamics of Deferred Decision, Psychological Science, 3(6), 358-361.
Vissing-Jorgensen, A. (2004). Perspectives on Behavioral Finance: Does “Irrationality” Disappear With Wealth? Evidence from Expectations and Actions, NBER Macroeconomics Annual 2003, Volume 18. 28
Weinstein, N. D. (1980). Unrealistic Optimism About Future Life Events, Journal of Personality and Social Psychology, 39(5), 806-820.
Zhu, N. (2002). The Local Bias of Individual Investors, Yale ICF Working Paper No: 02-30.
36 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 1 "Transaction Data for Expanded Investor Set and Analysis Investor Set"
For expanded investor set, buy and sell data of investors whose total stock portfolio in any month in
2011 is above 5,000TL with at least 1 buy or sell transaction. Mean buy / sell value is TL value of an
average buy / sell transaction. For analysis investor set, buy and sell data of investors whose total
stock portfolio in any month in 2011 is above 5,000TL with at least 1 buy and sell transaction
excluding abnormally high turnover investors (investors who shift their portfolios more than 100 times
annually). Mean buy / sell value is TL value of an average buy / sell transaction.
37 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 2 "Demographic Data of Analysis Investor Set"
Demographics of 244,146 investors in the analysis investor set. Age is the age of investor as of 2011.
Wealth is the average of 12 end of month portfolios consisting of equity, funds, warrants and
corporate bonds. Experience is the account open date of investor (if more than one accounts
available, opening date of oldest account taken into account). Region is the geographical region of
residence of investor registered in CRA database. N/A indicates data not available.
38 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 3 "Correlations of Proxies" This table shows the correlation of turnover ISE30 ratio, Small Mcap ratio and diversification proxies
measuring overconfidence, correlation of previous ownership ratio, absolute abnormal return ratio,
abnormal volume ratio and analyst coverage ratio proxies for familiarity bias and correlation of 30, 90
and 150 trading day positive return trend proxies for representativeness heuristic. * indicates
correlation is significant at the 0.01 level (2-tailed).
39 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 4 “Descriptive Statistics”
This table displays descriptive statistics for annual turnover, previous ownership ratio, 90 day positive
return trend and portfolio percentage change.
40 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 5 "Turnover Means"
The mean annual turnover for each gender, age, experience, wealth and region based on the analysis
investor set (244,146 investors). Monthly turnover is calculated as the one half of the total buy and
sell amounts in any month based on beginning of month prices divided by beginning of month
portfolio value. Annual turnover is simply twelve times average monthly turnover.
41 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 6 "Regression Results for Turnover" Regression results for overconfidence. Dependent variable is annual turnover and independent
variables are age, gender (in the form of male dummy), experience, wealth (in the form of dummy
variables for low and high wealth categories) and geographical region of residence (in the form of
dummy variables for Marmara and Southeast Anatolia regions). * indicates coefficient significant at
1%.
42 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 7 "Previous Ownership Means" The mean previous ownership ratio for each gender, age, experience, wealth and region based on
analysis investor set (244,146 investors). Previous ownership ratio is the percentage of number of
purchase transactions where stock has been purchased previously in 2011 to total number of
purchase transactions in 2011
43 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 8 "Regression Results for Previous Ownership Ratio" Regression results for familiarity bias. Dependent variable is previous ownership ratio and
independent variables are age, gender (in the form of male dummy), experience, wealth (in the form
of dummy variables for low and high wealth categories) and geographical region of residence (in the
form of dummy variables for Marmara and Southeast Anatolia regions). * indicates coefficient
significant at 1%
44 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 9 "90 Trading Day Positive Return Trend Means" The mean 90 trading day positive return trend for each gender, age, experience, wealth and
region based on analysis investor set (244,146 investors). 90 trading day positive return
trend is the average of number of positive return days prior to purchase divided by 90 for all
stock purchases in 2011.
45 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 10 "Regression Results for 90 Trading Day Positive Return Trend" Regression results for representativeness heuristic. Dependent variable is 90 trading day positive
return trend and independent variables are age, gender (in the form of male dummy), experience,
wealth (in the form of dummy variables for low and high wealth categories) and geographical region of
residence (in the form of dummy variables for Marmara and Southeast Anatolia regions). * indicates
coefficient significant at 1%
46 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 11 "Portfolio Percentage Change Means" The mean portfolio percentage change for each gender, age, experience, wealth and region based on
analysis investor set (244,146 investors). Portfolio percentage change is the daily percentage change
in stock portfolio based on change in stock numbers due to purchases and sales.
47 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 12 "Regression Results for Portfolio Percentage Change" Regression results for status quo bias. Dependent variable is daily percentage change in portfolio and
independent variables are age, gender (in the form of male dummy), experience, wealth (in the form
of dummy variables for low and high wealth categories) and geographical region of residence (in the
form of dummy variables for Marmara and Southeast Anatolia regions). * indicates coefficient
significant at 1%
48 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Figure 1 "Turnover Histogram"
Figure 2 "Previous Ownership Histogram"
49 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Figure 3 "90 Trading Day Positive Return Trend Histogram"
Figure 4 "Portfolio Percentage Change Histogram"
50 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Appendix Table 13 “Descriptive Statistics for Secondary Proxies”
This table displays descriptive statistics for ISE30 ratio, small Mcap ratio, diversification, absolute
abnormal return ratio, abnormal volume ratio and analyst coverage.
51 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 14 "ISE30 Means" The mean ISE30 ratio for each gender, age, experience, wealth and region based on analysis
investor set (244,146 investors). ISE30 ratio is the twelve month average of percentage of ISE30
stocks in the month end portfolios in 2011.
52 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 15 "Small Mcap Means"
The mean small Mcap ratio for each gender, age, experience, wealth and region based on analysis
investor set (244,146 investors). Small Mcap ratio is the twelve month average of percentage of small
market capitalization stocks in the month end portfolios in 2011. Stocks with market capitalization
smaller than USD100m are categorized as small Mcap stocks.
53 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 16 "Diversification Means" The mean diversification level for each gender, age, experience, wealth and region based on analysis
investor set (244,146 investors). Diversification is the twelve month average of naive diversification
level of month end portfolios in 2011.
54 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 17 "Overconfidence Robustness Check Results With Different Proxies"
Regression results for overconfidence using ISE30 ratio and small Mcap ratio. Dependent variable is
ISE30 ratio in the second column and small Mcap ratio in the third column. Independent variables are
age, gender (in the form of male dummy), experience, wealth (in the form of dummy variables for low
and high wealth categories) and geographical region of residence (in the form of dummy variables for
Marmara and Southeast Anatolia regions). * indicates coefficient significant at 1%.
55 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 18 "Overconfidence Robustness Check Results With First Alternative
Regression Model"
Regression results for overconfidence using annual turnover, ISE30 ratio and small Mcap ratio with
an alternative regression model. Dependent variable is annual turnover in the second column, ISE30
ratio in the third column and small Mcap ratio in the fourth column. Independent variables are age,
gender (in the form of male dummy), experience, wealth (in the form of dummy variables for each
wealth category) and geographical region of residence (in the form of dummy variables for Marmara
and Southeast Anatolia regions). *, ** and *** indicate coefficients significant at 1%, 5% and 10%
respectively.
56 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 19 "Overconfidence Robustness Check Results With Second Alternative
Regression Model"
Regression results for overconfidence using annual turnover, ISE30 ratio and small Mcap ratio with
an alternative regression model. Dependent variable is annual turnover in the second column, ISE30
ratio in the third column and small Mcap ratio in the fourth column. Independent variables are age,
gender (in the form of male dummy), experience (in the form of dummy variables for each experience
category), wealth (in the form of dummy variables for each wealth category) and geographical region
of residence (in the form of dummy variables for Marmara and Southeast Anatolia regions). *, ** and
*** indicate coefficients significant at 1%, 5% and 10% respectively.
57 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 20 "Overconfidence Robustness Check Results With Third Alternative
Regression Model"
Regression results for overconfidence using annual turnover, ISE30 ratio and small Mcap ratio with
an alternative regression model. Dependent variable is annual turnover in the second column, ISE30
ratio in the third column and small Mcap ratio in the fourth column. Independent variables are age,
gender (in the form of male dummy), experience (in the form of dummy variables for each experience
category), wealth (in form of dummy variables for low and high wealth categories) and geographical
region of residence (in the form of dummy variables for Marmara and Southeast Anatolia regions). *,
** and *** indicate coefficients significant at 1%, 5% and 10% respectively.
58 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 21 "Absolute Abnormal Return Means" The mean absolute abnormal return ratio for each gender, age, experience, wealth and region based
on analysis investor set (244,146 investors). Absolute abnormal return ratio is the ratio of purchase
transactions where previous day absolute (positive or negative) return of the stock is more than 125%
of previous day ISE100 return to total number of purchase transactions in 2011.
59 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 22 "Abnormal Volume Means"
The mean abnormal volume ratio for each gender, age, experience, wealth and region based on
analysis investor set (244,146 investors). Abnormal volume ratio is the ratio of purchase transactions
where previous day trading volume change (versus 2 days ago) of the stock is more than 150% of
previous day ISE100 trading volume change (vesus 2 days ago) to total number of purchase
transactions in 2011.
60 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 23 "Analyst Coverage Means”
The mean analyst coverage for each gender, age, experience, wealth and region based on analysis
investor set (244,146 investors). Analyst coverage is the average number of analysts covering stocks
purchased in 2011.
61 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 24 "Familiarity Bias Robustness Check Results With Alternative Proxy"
Regression results for familiarity bias using absolute abnormal return ratio. Dependent variable is
absolute abnormal return ratio and independent variables are age, gender (in the form of male
dummy), experience, wealth (in the form of dummy variables for low and high wealth categories) and
geographical region of residence (in the form of dummy variables for Marmara and Southeast
Anatolia regions). * and ** indicate coefficient significant at 1% and 5% respectively.
62 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 25 "Familiarity Bias Robustness Check Results With First Alternative
Regression Model"
Regression results for familiarity bias using previous ownership ratio and absolute abnormal return
ratio with an alternative regression model. Dependent variable is previous ownership ratio in the
second column and absolute abnormal return ratio in the third column. Independent variables are age,
gender (in the form of male dummy), experience, wealth (in the form of dummy variables for each
wealth category) and geographical region of residence (in the form of dummy variables for Marmara
and Southeast Anatolia regions). *, ** and *** indicate coefficients significant at 1%, 5% and 10%
respectively.
63 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 26 "Familiarity Bias Robustness Check Results With Second Alternative
Regression Model"
Regression results for familiarity bias using previous ownership ratio and absolute abnormal return
ratio with an alternative regression model. Dependent variable is previous ownership ratio in the
second column and absolute abnormal return ratio in the third column. Independent variables are age,
gender (in the form of male dummy), experience (in the form of dummy variables for each experience
category), wealth (in the form of dummy variables for each wealth category) and geographical region
of residence (in the form of dummy variables for Marmara and Southeast Anatolia regions). * and
**indicate coefficients significant at 1% and 5% respectively.5% respectively.
64 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 27 "Familiarity Bias Robustness Check Results With Third Alternative
Regression Model"
Regression results for familiarity bias using previous ownership ratio and absolute abnormal return
ratio with an alternative regression model. Dependent variable is previous ownership ratio in the
second column and absolute abnormal return ratio in the third column. Independent variables are age,
gender (in the form of male dummy), experience (in the form of dummy variables for each experience
category), wealth (in form of dummy variables for low and high wealth categories) and geographical
region of residence (in the form of dummy variables for Marmara and Southeast Anatolia regions). *
and ** indicate coefficients significant at 1% and 5% respectively.
65 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 28 "Representativeness Heuristics Robustness Check Results With Alternative
Proxies"
Regression results for representativeness heuristic using 30 and 150 trading day positive return
trends. Dependent variable is 30 trading day positive return trend in the second column and 150
trading day positive return trend in the third column. Independent variables are age, gender (in the
form of male dummy), experience, wealth (in the form of dummy variables for low and high wealth
categories) and geographical region of residence (in the form of dummy variables for Marmara and
Southeast Anatolia regions). * and *** indicate coefficient significant at 1% and 10% respectively.
66 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 29 "Representativeness Heuristics Robustness Check Results With First
Alternative Regression Model"
Regression results for representativeness heuristic using 90, 30 and 150 trading day positive return
trend with an alternative regression model. Dependent variable is 90 trading day positive return trend
in the second column, 30 trading day positive return trend in the third column and 150 trading day
positive return trend in the fourth column. Independent variables are age, gender (in the form of male
dummy), experience, wealth (in the form of dummy variables for each wealth category) and
geographical region of residence (in the form of dummy variables for Marmara and Southeast
Anatolia regions). *, ** and *** indicate coefficients significant at 1%, 5% and 10% respectively.
67 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 30 "Representativeness Heuristics Robustness Check Results With Second
Alternative Regression Model"
Regression results for representativeness heuristic using 90, 30 and 150 trading day positive return
trend with an alternative regression model. Dependent variable is 90 trading day positive return trend
in the second column, 30 trading day positive return trend in the third column and 150 trading day
positive return trend in the fourth column. Independent variables are age, gender (in the form of male
dummy), experience (in the form of dummy variables for each experience category), wealth (in the
form of dummy variables for each wealth category) and geographical region of residence (in the form
of dummy variables for Marmara and Southeast Anatolia regions). *, ** and *** indicate coefficients
significant at 1%, 5% and 10% respectively.
68 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 31 "Representativeness Heuristics Robustness Check Results With Third
Alternative Regression Model"
Regression results for representativeness heuristic using 90, 30 and 150 trading day positive return
trend with an alternative regression model. Dependent variable is 90 trading day positive return trend
in the second column, 30 trading day positive return trend in the third column and 150 trading day
positive return trend in the fourth column. Independent variables are age, gender (in the form of male
dummy), experience (in the form of dummy variables for each experience category), wealth (in form of
dummy variables for low and high wealth categories) and geographical region of residence (in the
form of dummy variables for Marmara and Southeast Anatolia regions). * and ** indicate coefficients
significant at 1% and 5% respectively.
69 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 32 "Status Quo Bias Robustness Check Results With First Alternative
Regression Model"
Regression results for status quo bias with an alternative regression model. Dependent variable is
daily percentage portfolio change. Independent variables are age, gender (in the form of male
dummy), experience, wealth (in the form of dummy variables for each wealth category) and
geographical region of residence (in the form of dummy variables for Marmara and Southeast
Anatolia regions). * and ** indicate coefficients significant at 1% and 5% respectively.
70 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 33 "Status Quo Bias Robustness Check Results With Second Alternative
Regression Model"
Regression results for status quo bias with an alternative regression model. Dependent variable is
daily percentage portfolio change. Independent variables are age, gender (in the form of male
dummy), experience (in the form of dummy variables for each experience category), wealth (in the
form of dummy variables for each wealth category) and geographical region of residence (in the form
of dummy variables for Marmara and Southeast Anatolia regions). *, ** and *** indicate coefficients
significant at 1%, 5% and 10% respectively.
71 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Table 34 "Status Quo Bias Robustness Check Results With Third Alternative
Regression Model"
Regression results for status quo bias with an alternative regression model. Dependent variable is
daily percentage portfolio change. Independent variables are age, gender (in the form of male
dummy), experience (in the form of dummy variables for each experience category), wealth (in form of
dummy variables for low and high wealth categories) and geographical region of residence (in the
form of dummy variables for Marmara and Southeast Anatolia regions). * indicates coefficients
significant at 1%.
72 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Figure 5 "ISE30 Histogram"
Figure 6 "Small Mcap Histogram"
73 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Figure 7 "Diversification Histogram"
Figure 8 "Absolute Abnormal Return Histogram"
74 WORKING PAPER SERIES N. 42 - MAY 2013 ■
Figure 9 "Abnormal Volume Histogram"
Figure 10 "Analyst Coverage Histogram"
75 WORKING PAPER SERIES N. 42 - MAY 2013 ■
UniCredit & Universities
Knight of Labor Ugo Foscolo Foundation
Piazza Gae Aulenti – UniCredit Tower, Torre A
20154 Milan
Italy
Giannantonio De Roni – Secretary General
giannantonio.deroni@unicredit.eu
Annalisa Aleati - Scientific Director
annalisa.aleati@unicredit.eu
Sara Colnaghi - Assistant
sara.colnaghi@unicredit.eu
Info at:
unicreditanduniversities@unicredit.eu
www.unicreditanduniversities.eu
1
top related