the effects of environmental disasters and pollution ... · the effects of environmental disasters...

SCUOLA DI INGEGNERIA

Corso di Laurea Magistrale in Management Engineering

The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China

Master graduation thesis by:

Emilia Tona - 837649

SCUOLA DI INGEGNERIA INDUSTRIALE E

DELL’INFORMAZIONE



Master graduation thesis by:

837649

Supervisor: Prof. Giancarlo Giudici

Co-supervisors: Prof. Vikash Ramiah,

Academic Year 2016 - 2017

INDUSTRIALE E



Supervisor: Prof. Giancarlo Giudici

Ramiah, Dr. Huy Pham


ii

Dedicated to my family,

who always believed in me.


iii

Acknowledgments

I would like to thank Giancarlo Giudici for supporting me in writing this thesis and

for having give me the unique opportunity to spend part of my research period in

Australia, an highly formative experience.

I thank Vikash Ramiah for having welcome me in his research team at University of

South Australia and for the working time spent together. A special thank goes to Huy

Pham for his invaluable support with my research work. I’d like to further thank

Vikash and Huy for having invite me to present my thesis work at the International

Environmental Finance Conference at Ton Duc Thang University.

Moreover, I want to thank Minuha Yang, Xi Yu, Ammar Asbi, Yu He, Christa

Viljoen and Braam Lowies for their special contribute to this amazing experience. I

loved being part of “Vik's Team” and I enjoyed the family environment during our

Friday meetings.

Finally, I’d like to thank my family for their priceless support in every decision of

my life, and especially for always giving me the chance to decide about my future,

without any constrain. Last, but not least, a very special thank to Gianni, the one who

shared with me every tear and every smile, every failure and every success. The one

who has always been by my side, since the beginning, and today more than ever this

goal is also his goal.


iv

Abstract

Environmental disasters cause severe losses in human lives and wellbeing but also

have effects on the economic and industrial activity, impacting on firms’ future

performance and investors’ perception of risk. In 2015, a series of explosions at a

container storage station at the Port of Tianjin involved the detonation of about many

kinds of hazardous and highly toxic chemical, leading to hundreds of deaths and

injuries. Considering that China is one of the largest polluters around the world, it

surely is a key case study to understand the relationship between environmental

disasters and economic activity.

In this thesis we study the effects of 18 chemical disasters, oil spills and pollution

alerts on the Chinese stock market from 2003 to 2015, with the scope to find out how

these catastrophic events affect investors’ behavior. We apply the event study

methodology to analyse how these events affect the stock prices of the different

industries in China. We supplement the methodology with various robustness tests in

order to find out whether these events generate abnormal returns (ARs).

Additionally, we estimate the change in systematic risk, applying GARCH, threshold

ARCH (TARCH), exponential GARCH (EGARCH) and power-ARCH (PARCH).

Our findings show that these events do generate significantly positive and negative

returns for different industries. Surprisingly, there is no clear pattern to state that

polluting industries are the most penalized. On the contrary, there is a clear pattern

showing that environmental disasters create uncertainty on the market and often

change the risk perceived by investors both in the short and long run.

Keywords: Environmental Disasters, Environmental Regulation, Event Study,

Systematic Risk


v

Abstract – Italiano

I disastri ambientali causano gravi perdite nella vita e nel benessere delle persone,

ma hanno anche effetti sull'attività economica e industriale, incidendo sul rendimento

futuro delle imprese e sulla percezione del rischio da parte degli investitori. Nel

2015, una serie di esplosioni presso una stazione di stoccaggio nel porto di Tianjin ha

comportato la detonazione di molteplici sostanze chimiche pericolose e altamente

tossiche, causando centinaia di morti e feriti. Considerando che la Cina è uno dei

paesi che inquina maggiormente al mondo, questo è sicuramente un caso studio

chiave per comprendere la relazione tra disastri ambientali e attività economica.

In questa tesi studiamo gli effetti di 18 disastri chimici, sversamenti di petrolio e

allarmi sull'inquinamento sul mercato azionario cinese dal 2003 al 2015, con lo

scopo di scoprire come questi eventi catastrofici influenzano il comportamento degli

investitori. Applichiamo la metodologia di studio degli eventi per analizzare come

questi influenzano i prezzi delle azioni delle diverse industrie in Cina. Integriamo la

metodologia con vari test di robustezza per scoprire se questi eventi generano

rendimenti anomali (AR). Inoltre, stimiamo la variazione del rischio sistematico,

applicando GARCH, threshold-ARCH (TARCH), exponential-GARCH (EGARCH)

e power-ARCH (PARCH).

I nostri risultati mostrano questi eventi generano rendimenti significativamente

positivi e negativi per diversi settori. Sorprendentemente, non esiste un modello

chiaro per affermare che le industrie inquinanti sono le più penalizzate. Al contrario,

vi è uno schema chiaro che dimostra che i disastri ambientali creano incertezza sul

mercato e spesso cambiano il rischio percepito dagli investitori sia a breve che a

lungo termine.


vi

Table of Contents

Acknowledgments .......................................................................................................... iii

Abstract ........................................................................................................................... iv

Abstract – Italiano .......................................................................................................... v

Table of Contents ........................................................................................................... vi

List of Figures ............................................................................................................... viii

List of Tables ................................................................................................................... x

1Introduction ................................................................................................................... 4

2Literature Review ....................................................................................................... 10

2.1 Environmental Regulation Literature .................................................................................11

2.1.1 Macroeconomic Effects of Environmental Regulations .............................................12

2.1.2 Microeconomic Effects of Environmental Regulations ..............................................20

2.1.3 The Financial Effects of Environmental Regulations .................................................25

2.1.4 Social and Environmental Accounting and Reporting ................................................32

2.2 Environmental Regulation in China ...................................................................................36

2.3 Environmental and Natural Disasters Literature ................................................................44

2.4History of Event Study Methodology .................................................................................48

2.4.1 The One-Factor Model ................................................................................................50

2.4.2 The Three-Factor Model .............................................................................................52

2.4.3 The Four-Factor Model ...............................................................................................55

2.4.4Event Study Methodology applied to Environmental Finance ....................................60

3Methodology ................................................................................................................ 70

3.1 Abnormal Return ................................................................................................................70

3.2 Robustness Tests ................................................................................................................74

3.3 Risk Analysis .....................................................................................................................79

4 Data and Empirical Findings .................................................................................... 85

4.1 Data and Background .........................................................................................................85


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4.2 Empirical Results of Event Study Analysis .......................................................................91

4.3 Empirical Results of Robustness Tests ............................................................................102

4.4 Risk Analysis: short- term and long-term change in risk .................................................106

5 Conclusions ............................................................................................................... 118

Bibliography ................................................................................................................ 122


viii

List of Figures

Figure 1: Kuznets curve graph .................................................................................. 18

Figure 2: Trends in Infant Mortality Rate. Tanaka, 2010 ......................................... 37

Figure 3: The impact response curve of Environmental regulation to FDI.Peng at al.

(2011) ................................................................................................................. 42

Figure 4: Impact response curves of FDI to environmental regulation. Peng at al.

(2011) ................................................................................................................. 42

Figure5: Long-Term Climate Change Risk in China. Ramiah et al. (2015a) ........... 44

Figure 6: China’s map where the provinces that failed in achieving the water quality

target are marked in red and the provinces that achieved the water quality target

are marked in blue .............................................................................................. 86

Figure 7: Number of statistically significant (95% level) positive reactions of the

106 stock indexes to environmental disasters and pollution alerts..................... 97

Figure 8:Number of statistically significant (95% level) negative reactions of the

106 stock indexes to environmental disasters and pollution alerts..................... 97

Figure 9: Risk analysis. Short term change in systematic risk following chemical

disasters. ........................................................................................................... 111

Figure 10:Risk analysis. Short term change in systematic risk following oil spills.

.......................................................................................................................... 112

Figure 11:Risk analysis. Short term change in systematic risk following pollution

alerts. ................................................................................................................ 112

Figure 12: Risk analysis. Long term change in systematic risk following pollution

chemical disasters. ............................................................................................ 113

Figure 13: Risk analysis. Long term change in systematic risk following oil spills.

.......................................................................................................................... 113


ix

Figure 14: Risk analysis. Long term change in systematic risk following pollution

alerts. ................................................................................................................ 114


x

List of Tables

Table 1: Chemical Disasters ..................................................................................... 89

Table 2: Oil Spills ..................................................................................................... 90

Table 3: Pollution Alerts ........................................................................................... 90

Table4: Reaction of the stock market to environmental disasters and pollution alerts

in China: statistics about the mean abnormal returns (AR) and mean cumulated

abnormal returns in five days (CAR5) and ten days (CAR10) around the event

dates. ................................................................................................................... 96

Table 5: Reaction of the stock market to environmental disasters and pollution alerts

in China: statistically significant abnormal returns(AR) under three benchmark

models. T-statistics in parentheses ..................................................................... 98

Table 6:Reaction of the stock market to environmental disasters and pollution alerts

in China: statistically significant cumulated abnormal returns (CAR) in five and

ten days after the event. T-statistics in parentheses .......................................... 100


in China: robustness tests on abnormal returns (AR). T-statistics in parentheses

.......................................................................................................................... 103

Table 8: Risk Analysis. Aggregate change in systematic risk ................................ 109

Table 9: Risk Analysis. Robustness Tests on Aggregate Risk Model .................... 110


2

Chapter 1

Introduction


3


4

1Introduction

On August 12th 2015, the blast of about 800 tons of ammonium nitrate and 500 tons

of potassium nitrate, as well as other 40 kinds of hazardous and highly toxic

chemicals, caused a series of explosions killing 173 people from burns and injuring

hundreds of others at a container storage station at the Port of Tianjin (China),with

shock-waves felt many kilometers away. Thousands of people were evacuated from

the area with water, soil and air having been heavily contaminated.

Environmental disasters cause enormous losses of life and wealth every year—a

threat that is recognized as a priority and addressed in public policies. A number of

these accidents occur as a direct consequence of human industrial activity (soil and

water contamination, oil and toxic material leakage, plant explosions) whilst other

events are believed to be indirectly provoked by the release of greenhouse gas

(GHG) emissions and the consequent global warming (droughts, floods and storms).

China is one of the largest polluters around the world, contributing to 19.5% of total

worldwide industrial output and 22.3% of total global of GHG emissions1 but is also

one the countries who is investing more on renewable energy2 and sustainability

(Bonzanini et al., 2016; Kutan et al., 2017). Therefore, China is a key case study to

understand the relationship between environmental disasters and economic activity.

This topic is important, because the growth of energy consumption and industrial

activity, as to progress and reduce poverty, generates a trade-off between

1Data are reported from the World Factbook issued by the US Central Intelligence Agency.

2Seehttp://www.businessinsider.com/china-green-energy-plan-2017-5


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sustainability and development, that must be addressed by policymakers and

regulators in their agenda.

The main objective of this study is to examine the effect of environmental disasters

and pollution alerts on the Chinese stock markets. Scant research exists that

investigates the relationship between natural disasters and stock market performance

of listed companies in fast-growing countries and therefore China’s pursuit to

balance economic growth and environment pollution remains a challenge (Yuan,

2016; Liu et al., 2017) that can benefit from academic research.

We collected information about chemical disasters and oil spills occurred in China

from 2003 to 2015. In the recent years, the Chinese government showed a high

interest in the minimization of the level pollution, through the introduction of the

Heavy Air Pollution Contingency Plan. With the aim to understand how this plan

impacts on the most polluting industries, we integrated our study with the analysis of

pollution alerts. In sum, our database is made up by 18 events. We analyse the effects

of the events on the Chinese stock markets, identifying 106 industry indexes and

computing the abnormal returns. We find significantly positive and negative returns

for different industries. Surprisingly, there is no clear pattern to state that polluting

industries are the most penalized. On the contrary, there is a clear pattern showing

that environmental industries create uncertainty on the market and often increase the

risk perceived by investors in some industries both in the short and long run.

Our contribution adds to the existing literature in a number of ways. This work is the

first to address the effect on stock markets of chemical disasters, oil spills and


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pollution alerts in a country like China which is driving the global economy growth

and where environmental issues are relevant. Second, we show abnormal returns on

stock markets may be interpreted as signals from the market about the expected

commitment from public authorities to leverage on environmental disasters to

introduce more tight regulation and requirements to manufacturing companies. In the

case of China, this signal is very weak. Third, we document environmental alarms

create uncertainty on the exchange, modifying investors’ risk perception; this raises

concerns about the capability of investors to estimate correctly the environmental

risk of polluting industries.

The work proceeds as follows. Chapter 2 firstly reviews the existing literature related

to environmental regulation. Secondly it focuses on the relationship between

environmental regulation and emergencies and stock prices. Thirdly if shows a brief

history the event study methodology. Chapter 3 shows the applied methodology,

while Chapter 4 describes the data, the empirical analysis and main findings. Chapter

5 concludes with our considerations.


7


8

Chapter 2

Literature Review


9


10

2Literature Review

The use of finance principles to examine environmental issuesis a relatively new

research area and at present, the definition of this area is not in unison. For instance,

it is called “environmental finance” in Australia (Ramiah, Martin and Moosa, 2013)

and “sustainable finance” in Europe (Heinrichs, Martens, Michelsen and Wiek,

2015). Sandor (2012), at Columbia University in the United States, assessed that we

can refer to “environmental finance” as (1) in terms of the use of financial

instruments to protect the environment and (2) when ecological economics

paradigms are applied to finance and investment. Furthermore, Ramiah et al. (2013)

contribute to this discussion by showing that when environmental regulations are

combined with financial markets, it falls under the umbrella of environmental

finance. More recently, Ramiah and Gregoriou (2016) expand this definition by

showing that environmental/sustainable finance covers other areas such as corporate

social responsibility (CSR), public environmental investments, carbon trading, green

bonds, socially responsible investment funds, water markets, corporate

environmental performance and crowd funding of renewable energy projects.

As discussed above, the definition of “environmental finance” is relatively dispersed

and thus this chapter reviews the concept of environmental finance as well as other

closely related fields such as environmental economics and environmental

accounting. Additionally, we show a potential gap in the current literature related to

the effects of environmental disasters.


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This chapter is then structured as follows. Section 2.1presents the effects of

environmental regulations on the economy from macroeconomic, microeconomic

and financial point of view, with a further focus on Chinese market. Given that the

main aim of this is to study the effects of environmental disasters on China’s

financial markets, section 2.2 turns its focus to environmental regulation in China.

Section 2.3 reviews the studies related to the impacts of environmental and natural

disasters showing the potential gap and Section 2.4 discusses the event study

literature and its application on environmental finance.

2.1 Environmental Regulation Literature

In this section, we discuss various effects of environmental regulations on the

economy. Firstly, we discuss the literature around the impact of environmental

regulations on macroeconomic indicators such as employment, export, import,

competitiveness and productivity. Within the economics discipline, many scholars

consider environmental regulation as one of the reasons for macroeconomic

disasters, while others claim that environmental degradation is the major cause.

Secondly, we discuss the effects of environmental regulations on microeconomics

factors including plant location, costs of production and productivity. These factors

influence the cost structure of a firm, which plays a fundamental role in the process

of profit maximization. Any new regulations, including environmental regulations,

might alter the production costs, influence a firm’s decision in locating its new plant,

or require the firm to hire more labour to attain the obligatory environmental

standards that in turn affects the productivity.


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Finally, we discuss the literature on the effects of environmental regulation in

finance, which cover several areas such as stock prices and returns, corporate

profitability, market value and risk. The overall conclusion is that the effect of

environmental regulations varies across industries and countries. Given that

environmental regulations are designed to achieve various objectives in each

different country, we observed a country effect of environmental regulations.

2.1.1 Macroeconomic Effects of Environmental Regulations

The effects of environmental regulation on employment have been examined

extensively in the literature and three outcomes can be found from the

macroeconomic literature that are negative, positive and no effect. The first outcome

is supported by Crandall (1981) who argues that excessive environmental regulations

and regulations in general cause an increase in inflation, a lag in GDP growth, a

reduction in productivity growth and the depreciation of the currency. Furthermore,

Walsh (2012) affirms that the President Obama’s refusal to tighten ozone standards,

which was suggested by the Environmental Protection Agency in 2011, saved

thousands of jobs. In addition, Greenstone, List, and Syverson (2012) argue that the

introduction and expansion of the U.S. environmental policy are the main reasons for

the decrease in the U.S. manufacturing workforce from 1970 to 2012.

On the other hand, Repetto (1995) claims that higher investments in more

environmentally-friendly equipment could limit the growth of employment but they

are not causing any job loss and, in fact, environmental protection creates more jobs.

In addition, Bezdek, Wendling, and Di Perna (2008) show the evidence of the


13

positive aggregate effect of investments in environmental regulations on

employment.

Other studies have shown that the relationship between employment and

environmental policies does not exist. Eberly (2011) and Sinclair and Vesey (2012),

for instance, claim that no empirical evidence demonstrates that a decline in

employment is caused by changes in regulation. Moosa and Ramiah (2014) support

this argument by showing cross-sectional scatter diagrams between unemployment

and environmental burden. Moreover, Morgenstern, Pizer and Shih (2000) argue that

the relationship between employment and environmental policies is insignificant.

Regarding international trade, many studies have examined the relationship between

trading activities and environmental risk. The literature we will discuss afterwards

shows that environmental regulations have no negative impact on international trade

apart from manufacturing industries. According to Tobey (1990), Walter (1982),

Pearson (1987) and Leonard (1988), there is no statistically significant effect of strict

environmental regulations on net exports.

By examining the relationship between importing activities and environmental costs,

Grossman and Krueger (1993) find no relationship between pollution control costs in

the U.S. and imports from Mexico and no cross-industry difference in environmental

costs. Furthermore, Jaffe, Peterson, Portney and Stavins (1995) contribute to this

debate by showing the small international difference of environmental costs

compared to differences in labour costs and productivity.


14

Kalt (1988) demonstrates that changes in environmental compliance costs do not

explain the change in net exports for the entire economy with an exception for

manufacturing industries where a negative effect is observed. Maitra (2003)

examines 78 industrial categories for the period between 1967 and 1977 and, similar

to Kalt (1988), fails to establish a relationship with the overall market but observes a

relationship within the manufacturing industry.

Moreover, the effects of environmental regulations on international trade have been

examined via comparative advantages in export amongst countries. For instance,

Low and Yeats (1992) analyse the export activities of “dirty” industries, which have

the highest pollution control costs, in multiple countries. They demonstrate that

developed countries reduce the proportion of “dirty” product exports, whereas

developing countries increase the proportion of “dirty” product exports during the

period between 1965 and 1988. Furthermore, Low and Yeats’s (1992) study shows

that there is an increase in comparative advantage for developing countries that

export pollution-intensive products. In a more recent study, Levy and Dinopoulos

(2016) find that environmentally-friendly (polluting) firms earn more profits and

engage in more exporting activities if the consumers have strong (weak) preferences

for environmental quality.

When we examine the relationship between environmental regulation and

competitiveness, we find that the direction between the two factors at country level is

uncertain. The adverse effect of environmental regulation on competitiveness in

international markets has been documented in the literature by Moosa and Ramiah

(2014) where the authors show that environmental regulations have three main


15

effects: rise in imports, decrease in exports and the tendency of regulated companies

to move overseas. Stewart (1993), however, suggests that the reduction in

international competitiveness due to stringent environmental policies is just one of

the possible outcomes but he argues that the contribution of a cleaner environment

and resource conservation should be considered.

On the other hand, according to Porter (1991), the international competitiveness may

be improved by environmental regulation. The Environmental Protection Agency

(1992) argues that the introduction of environmental regulations lead to a reduction

in emission and overall costs of businesses are achieved through more cost-effective

processes. Esty and Porter (2002) support this finding by showing that countries with

a more stringent and aggressive environmental regime tend to be more competitive.

Other studies argue that environmental regulations can boost competitiveness via

innovation. For instance, Gardiner (1994) affirms that it is beneficial for domestic

economy if more stringent policies are also imposed in other countries. Barbera and

McConnell (1990) indicates that environmental regulation can encourage companies

to invest more in research activities to invent new, less polluting and more efficient

production techniques that will subsequently improve competitiveness.

The relationship between environmental regulations and international

competitiveness can be expressed in an indirect way if we analyse the real effective

exchange rate, whose determinant factors are: the nominal exchange rate, the

domestic inflation, and the foreign inflation (Moosa and Ramiah, 2014). The

environmental regulations might cause an increase in the real exchange rate, due to


16

an appreciation of the nominal currency or an increase in domestic inflation relative

to foreign inflation, with a consequent decrease in the competitiveness level. By

applying the flexible-price monetary model, Moosa et al. (2014) suggest that the

general currency will depreciate if the environmental regulation unfavorably affects

the economic growth. However, the authors argue that there is no clear evidence that

the environmental regulation would increase inflation and shrink competitiveness.

This result is consistent with a study by Haveman and Christainsen (1981) in which

the authors shows how environmental regulation might cause a one-time rise in the

price of particular goods and services but it does not result in a continuous growth in

the price level or inflation rate.

The debate around the true effect of environmental regulations on economic growth

is still unsettled. Some authors sustain that there is a trade-off between economic

growth and environmental degradation. For instance, Moosa et al. (2014),argue that

the increase in economic activity requires more inputs that causes a larger amount of

environmental waste, and therefore environmental degradation. Daly (1991) sustains

that an increase in environmental waste and concentration of pollutants lead to the

degradation of environmental quality that will subsequently cause a greater decrease

in human welfare in comparison to surges in income.

The Jorgenson and Wilcoxen’s (1990) study shows that average growth rate of the

real Gross National Product (GNP) of the U.S. drops by approximately 0.2% per

year, over the period from 1974 to 1985, due to the effects of operating costs

associated to pollution control, pollution control investment, and compliance with

motor vehicle emission standards. The authors also find that GNP may have been


17

1.7% higher than the actual historical value in the absence of environmental

regulations.

On the other hand, Beckerman (1992) illustrates that, in the long run, becoming

wealthy certainly improves the environment, showing a positive correlation between

environmental improvement and economic growth. Meyer (1992) finds that the effort

to improve environmental quality doesn’t deter economic growth and development.

Munasinghe (1999) contributes to this discussion by showing that the adoption of

more environmentally sustainable regulations fosters higher development levels at a

lower environmental cost. Moreover, Esty and Porter (2002) argue that promoting

economic growth is one of the most important aspects to enhance environmental

results.

Selden and Song (1994), referring to the environmental Kuznets or inverted-U curve,

argue “while industrialization and agricultural modernization may initially lead to

increased pollution, other factors may cause the eventual downturn, at least for some

pollutants”. Xepapadeas (2005) suggests to include environmental factor in the

economic growth models in order to analyse a number of issues in environmental

economics. The author concludes that environmental pollution negatively affects the

utility of individuals.


Nevertheless, Zhang (2012) argues that is not cautious to use the environmental

Kuznets curve hypothesis to solve environmental problems in economic growth

because the true relationship can be

author claims that environmental quality can be a produ

growth.

Regarding the impact of environmental regulation on productivity, the literature give

us evidence of the existence of two main lines of thoughts,

negative relationship, the other one is against this theory. However, we can notice

how the first line of thoughts has been mostly supported by studies conducted before

the Kyoto Protocol (2005). Instead, after the Kyoto Protocol a

environmental regulation on productivity has been reported. Moreover,

levels always vary across industries because they adopt different technologies in their

production processes, and the productivity level of sectors/firms

differently by different categories of environmental regulations.

Figure 1


Zhang (2012) argues that is not cautious to use the environmental


because the true relationship can be N-shaped or more flexible shape.

claims that environmental quality can be a productive input for economic


us evidence of the existence of two main lines of thoughts, one is supporter of the



the Kyoto Protocol (2005). Instead, after the Kyoto Protocol a positive impact of

environmental regulation on productivity has been reported. Moreover,


production processes, and the productivity level of sectors/firms

differently by different categories of environmental regulations.

1: Kuznets curve graph


18

Zhang (2012) argues that is not cautious to use the environmental


shape. Moreover, the

ctive input for economic


one is supporter of the



positive impact of

environmental regulation on productivity has been reported. Moreover, productivity


production processes, and the productivity level of sectors/firms is also affected


19

The measure of the impact of environmental regulation on productivity can be

conducted using three main approaches: growth accounting, macroeconomic general

equilibrium models, and single-equation models. The first approach has been applied

by Denison (1979) who finds a loss between 13% and 20% in productivity due to

environmental regulations. However, Denison (1979) has been criticized by

Haveman and Christainsen (1981) because of his failure in explaining the large

residual factor and because his methodology ignores the effects of energy crisis.

Another critique comes from Moosa et al. (2014) regarding the lack in considering

how the change in labour and capital requirement for product redesign can shift to

more energy efficient products.

The macroeconomic general equilibrium model, that includes a long-term growth

component, has been applied by Jorgenson and Wilcoxen (1990) who find an

increase of 3.79% in capital stock and a raise of 2.5% in the GNP in the absence of

environmental regulations. Data Resources Incorporated (1979) discovers that the

pollution control investment leaves no room for alternative capital investments in

plant and equipment with a consequent decrease in labour productivity (more

employees are required to maintain the production at that level).

Thirdly, Haveman and Christainsen (1981) introduce the single-equation models to

show an adverse effect of environmental policies on productivity. Siegel (1979)

explains the structural breaks in productivity between 1967 and 1973 finding that the

reduction of pollution expenditure was a significant negative factor. According to

Gollop and Roberts (1983) and later to Gray (1987), the slowdown in productivity is

caused by regulations, especially environmental regulations, in the 1970s. In addition


20

to this discussion, Barbera and McConnel (1986, 1990) argue that the average

productivity of polluting industries is negatively affected by environmental

regulations However Berman and Bui (1999) sustain that the effects of

environmental regulations on productivity are not necessarily negative and can

instead be positive under certain conditions. Moosa and Ramiah’s (2014) study also

illustrate a positive relationship between environmental regulation and labour

productivity.

2.1.2 Microeconomic Effects of Environmental Regulations

According to Moosa and Ramiah (2014) the effect of environmental regulations on

the costs of production can be measured through various approaches, including the

survey approach and analytical cost function approach. Although the estimation

process is not perfect, the literature suggests us that compliance costs of

environmental regulations affect business activities with a possible reduction in

firms’ profits and shareholders’ benefits. Moreover, given that firms do not have

identical cost structures, the effect of environmental regulations on cost structure

may differ at a firm level.

In the U.S., the Census Bureau has been using the Pollution Abatement Cost and

Expenditure (PACE) survey to estimate the cost of environment protection to private

industry since 1973. However, Berman and Bui (1999) and later Becker and

Henderson (2001) provide clear indication that the survey approach is not ideal for

measuring costs of environmental regulations on production showing a clear concern

about possible mistakes the estimation of costs.


21

Through the application of analytical cost function approach, Becker et al. (2001)

study the costs of environmental regulations on firms in different industries and find

that production in heavily-regulated firms results in higher costs in comparison to

less-regulated firms. The authors also suggest that there is an higher vulnerability to

environmental regulation in young firms. Moreover, Becker et al. (2001) argue that

PACE underestimated the environmental expenditures.

From the literature, it’s evident that the debate about the difference between ex-ante

and ex-post costs of environmental regulations is still unsettled. For instance,

Oosterhuis (2006) illustrates how the ex-post realised costs of environmental

regulations are doubled up by the ex-ante estimation of costs. Crain and Crain (2010)

further highlight that the report commissioned by the Small Business Administration

uses different sources of data to deliver the total costs of federal regulations on firms.

Additionally, Sinclair and Vesey (2012) contribute to this debate arguing that the

limitation in the estimation of the Office of Management and Budget for major

federal regulations in the U.S. is partly due to the dependence on agencies’ ex-ante

estimates.

Apart from the cost of production, the choice on plant location plays a significant

role in the cost structure of a firm. According to the pollution haven hypothesis by

Levinson and Taylor(2008), firms tend to relocate to countries where environmental

policies are less stringent and in particular firms from developed countries tend to

move their polluting businesses to developing countries in order to avoid stringent

environmental regulations. This process, known as carbon shifting process, allows

the firms to reduce compliance costs and benefit from cheap labour with a


22

consequent reduction of production costs. Moosa et al. (2014) argue that the

maximisation of expected net present value plays one of the main roles in making a

decision on plant location and timing.

The effect of environmental regulations on plant location can be measured through

the use of the survey approach and the econometric approach. For instance, Stafford

(1985) and Lyne (1990) use the survey approach to interview business executives

who are involved in the decision-makingprocess of plant location and find that

environmental regulation is not one of the main determinants of plant location.

Moreover, Levinson (1996) shows some concern about the interpretation of survey

results and about the accurate measurement of the real effect of environmental

regulation on plant location.

The econometric approach to measure the effect of environmental regulations on

plant location has been applied for instance by Bartik (1988) who shows that the

effects of environmental variables on business locations are small within Fortune 500

companies during the period from 1972 to 1978. Furthermore, McConnell and

Schwab (1990) use the same econometric approach on the data from vehicle

assembly plants in the 1970sdemonstrating that environmental regulations appear not

to affect firm-location decisions. Additionally, Levinson (1996) in his study about

the effects of the stringency of state environmental regulations on plant location,

finds a weak relationship between differences in environmental regulations amongst

the states of the U.S. and location choices.


23

However, Becker and Henderson (2000) criticize the studies by McConnel and

Schwab (1990) and Levinson (1996) from many aspects. The authors argue that the

previous studies ignore differences within states’ regulations and group both

polluting and non-polluting industries together. Furthermore, they observe that no

specific regulatory process is used as a proxy for environmental regulation in those

studies. In their study of investigation of the effects of air quality regulation on plant

location, births, sizes and investment pattern decisions in polluting industries in the

U.S., Becker and Henderson (2000) show that the reason why polluting industries

relocate to less polluted regions is to evade stringent environmental regulations

which significantly affect timing of plant investments.

The foreign direct investment (FDI) is another factor that could be used to study the

effect of environmental regulations on firm-location decisions. For instance, Jaffe et

al. (1995) show that the effects of environmental regulations on firms’ investment

decisions can be examined via either change in FDI or decisions for domestic plants.

The authors conclude that the motivating factors for environmental regulations and

taxes are similar. However, Moosa et al. (2014) sustain that most literature on FDI

does not consider environmental regulation as a factor that determines changes in

FDI. Wheeler and Mody (1992) and Moosa and Cardak (2006), for instance, when

studying the determinants of FDI do not include the environmental factor.

Other studies have investigated the effects of environmental regulations on

investment decisions of businesses. Marcus and Kaufman’s (1986) study, for

instance, shows that firms are hesitant and cautious in response to new energy policy.

Yang, Burns and Backhouse (2004) contribute to this discussion arguing that


24

environmental uncertainty leads to the postponement of investment decisions. On the

other hand, Hoffman (2007)argue that the EU ETS results caused an increase in

investment in technology of German electricity industry, but that the technological

changes are moderate in comparison to the carbon emission targets of the EU ETS.

Furthermore, Hoffmann, Trautmann and Hamprecht (2009) sustain that investment

decisions in the German power industry are not deferred by regulatory uncertainty

caused by EU ETS.

The literature on the relationship between environmental regulations and productivity

at the firm and sectoral levels is relatively sparse. According to Moosa et al. (2014),

the effect of environmental regulations on productivity at thefirm and sectoral level

can be estimated through labour productivity (LP) or total factor productivity (TFP).

Labour productivity is calculated as an amount of unit produced by a unit of labour,

ignoring the contribution of capital, energy, and materials. The total factor

productivity is estimated as an amount of output produced by a unit of aggregate

inputs. Gray (1987) observes that the two techniques can lead to an incorrect

measurement of the effect of environmental regulations on productivity because of

their lack in differentiating the contribution of regulatory compliance costs from

other input costs. Additionally, Gray and Shadbegian (1993) argue that measurement

errors are caused by the use of observed productivity figures that lead to biased

results.

Berman and Bui (1999) apply micro-regulatory changes to provide variation between

regions and assess effects of regulatory changes on PACE directly to overcome

selection bias and measurement errors. The authors also show that environmental


25

regulations cause an increase in environmental operating costs which only affects

productivity in the short-term. Moreover, Greenstone et al. (2012) measure a

decrease of 2.6% in TFP due to stricter air quality regulations, in particular oriented

to manage the ozone levels.

Nevertheless, Graff and Neidel (2011) evaluate a negative correlation between the

productivity of farm workers and ozone levels and in particular they measure an

average labour productivity increases by 4.2% when the ozone level declines by 10

parts-per-billion. The authors further postulate the possibility to have additional

benefits if the government promulgates more stringent regulations on ozone

pollution.

2.1.3 The Financial Effects of Environmental Regulations

In the literature a number of studies confirm the presence of a relationship between

environmental issues and the stock market. According to Moosa and Ramiah (2014),

stock prices and returns will be determined by the investors’ opinions on whether the

information is good news or bad news for underlying companies. Ramiah, Moosa

and Martin (2013) argue that environmental regulations can produce three possible

stock market reactions: positive, negative and mixed. Feldman Soyka and Ameer

(1996) sustain that an increase in returns by approximately 5% tend to be

experienced by firms with environmental management and environmental

performance, since they have lower perceived risks. Klassen and McLaughlin (1996)

find a positive relationship between environmental news and abnormal returns when

firms win environmental awards. By studying 748 U.S. environmentally-friendly


26

firms through the Carhart 4-factor Model, Chan and Walter (2014) find that high

environmental performing firms create wealth for shareholders in the long run. In

their study of analysis of the relationship between environmental regulations and the

stock market, Ramiah Morris, Moosa, Gangemi and Puican (2016) find that the U.K.

stock market mostly reacts positively to announcements of environmental regulation.

“Green effect” is a new term emerging in this field by Pham, Ramiah and Moosa’s

(2015) study, which refers to abnormal return associated with environmental

regulations.

However, Moosa et al. (2014) argue that a negative reaction with a consequent

negative abnormal return is detected when environmental regulations are regarded as

bad news by investors. Moreover Muoghalu, Robinson and Glascock (1990) find that

hazardous waste lawsuits in the U.S. cause a statistically significant loss of 1.2% on

the stock market value corresponding to a loss of $33.3 million in equity value.

Hamilton (1995) finds that investors are likely to experience a statistically significant

negative abnormal returns if firms release higher pollution figures in Toxics Release

Inventory reports, with an average loss of $4.1 million in stock value when the

information arrives. Additionally, White (1995) detects a strong negative risk-

adjusted returns by environmentally-oriented mutual funds when firms have poor

environmental performance. Klassen and McLaughlin (1996) conclude that news

about an environmental crises leads to significantly negative abnormal returns.

Mixed reactions to environmental regulations can be detected in the stock market.

For instance, Flammer (2012) studies the relationship between announcements of

environmental CSR and stock market reaction using event study methodology and


27

finds firms that behave responsibly towards the environment experience an increases

in stock prices while firms that behave irresponsibly towards the environment have a

decrease in stock prices. The author concludes both positive stock market reaction to

eco-friendly events and negative stock market reaction to eco-harmful events of

firms that have higher levels of environmental CSR. In their study, Ramiah et al.

(2013) hypothesise that investors in polluting industries have to experience negative

abnormal returns whilst environmentally-friendly industries have to experience

positive abnormal returns, assuming that the environmental authority has the

objective to penalise polluters and encourage environmentally-friendly businesses.

Ramiah et al. (2013) detect no changes in the wealth of shareholders of industries

considered as heavy polluters, such as the electricity industry, in Australia after the

implementation of stringent environmental regulation. The authors explain this

results by the ability of electricity providers to pass the costs of environmental

regulations onto consumers. On the other hand, a value destruction is experienced by

shareholders of other industries that are not considered as the biggest polluters, such

as the beverage sector, as they experience an increase in the cost of production

originating from the rise of electricity cost. Due to these findings, Ramiah et al.

(2013) argue that green policies are not effective in their current forms.

By studying the relationship between EU ETS and stock markets Veith, Werner and

Zimmermann (2009),suggest that firms in European electricity industries

successfully pass environmental costs onto consumers and overcompensate for all

the costs originated by a rise in the price of emission allowances. The authors remark

the existence of a positive correlation between share prices of electricity providers

and rising prices for emission allowances. Furthermore, Oberndorfer (2009) finds


28

results that are consistent with Veith et al. (2009), by studying electricity

corporations in Italy, UK, Denmark, Finland, Portugal, Germany and Spain. The

author also shows a negative reaction of electricity providers’ stock prices to a

decrease in European Union Allowance (EUA) prices and these results vary across

countries. Additionally, Oestreich and Tsiakas (2015) show that German firms that

receive free EUA experience higher stock returns in comparison to firms that do not.

The authors also suggest that a higher carbon risk is associated with polluting firms,

hence they are likely to have higher expected returns.

The effects of the costs to be compliant with environmental regulations on firms’

financial performance have been largely studied in the literature. The literature we

analyse shows that environmental regulations tend to cause three possible effects on

corporate profitability: negative, positive and neutral. For instance, Spicer

(1978)shows that when U.S. firms in the pulp and paper industry have better

pollution-control records, they tend to have higher profitability and lower systematic

risk in comparison to firms that have poorer performance. Porter and Van Der Linde

(1995) further suggest that environmental regulations promote business innovations

which in turn reduces costs of compliance with a consequent increase in profitability.

Hart and Ahuja (1996) conduce a study on S&P500 firms and show that putting

some effort into reducing emissions through pollution prevention increases firms’

profitability within the two-year period after initiating the procedure. Waddock and

Graves (1997) find a positive relationship between corporate social performance

(CSP) and profitability. Additionally, Hart (1997) states that “in the industrialized

nations, more and more companies are ‘going green’ as the firms realize that they


29

can reduce pollution and increase profits simultaneously”. Analyzing the relationship

between environmental regulations and profitability in Egypt, Wahba (2008)finds a

statistically significant positive relationship between corporate environmental

responsibility and market value as measured by Tobin’s q ratio. The author further

concludes that if firms have a better corporate environmental responsibility

performance, it is likely that Tobin’s q ratio is higher than one and the firms will be

more profitable.

On the other hand, Chen and Metcalf (1980) sustain that firm management hesitates

to increase pollution abatement costs because it leads to lower reported earnings. The

authors also state that there is not enough evidence to claim a positive relationship

between the pollution control records and profitability due to the fact that high-

earning firms have higher pollution abatement costs, whereas low-earning firms have

lower abatement costs. Moreover, Wagner, Vanphu, Azomahou and Wehrmeyer’s

(2002) study uses dummy variables to spot the effects of sub-sectoral influences for

various sub-sectors of industrial sectors and find a significant negative relationship

between environmental performance and economic performance within the paper

industry in the UK, Italy, the Netherlands, and Germany.

Other studies show that the relationship between environmental compliance and

financial performance is not significant. Mahapatra (1984), for instance, concludes

that a relationship between the pollution abatement costs and profitability does not

exist due to the fact that pollution control costs do not produce income. Mill (2006)

fails to observe a relationship even after looking at mean risk-adjusted returns of

firms. Murray, Sinclair, Power and Gray (2006) further fail to link share prices with


30

environmental disclosures in a time series analysis after studying the relationship

between share prices and environmental and social disclosures by examining 100

largest firms in the U.K.. In a more recent study, Naila (2013) fails to establish this

relationship with manufacturing firms in Tanzania and has been criticised for having

a small sample bias. McWilliams and Siegel (2000) sustain that the reason for not

establishing a relationship is due to the failure to consider R&D costs.

Considering that the market value of a firm can be defined as the value of the

outstanding shares, which is calculated by either multiplying the number of shares by

stock prices, orby summing the value of debt and equity where required rate of return

or cost of funding is an important element, Moosa et al. (2014) suggest that the

environmental performance of firms can affect both stock prices and cost of funding

implying the effect of environmental performance on the market value of firms.

Among the studies that analyse the relationship between environmental regulations

and market value, Cohen, Fenn, and Naimon (1995), explain that the market returns

of S&P500 is generally met or exceeded by the return of well-balanced portfolios

that track S&P500 and include environmental leaders in the portfolios. Dowell, Hart,

and Yeung (2000) conduct a study on market value of U.S. multinational

corporations whose results show corporations with higher environmental standards

can have much higher market values. The authors further sustain that environmental

regulations create and not destroy the value of the firm and propose three factors to

support the statement. Firstly, there is no evidence of cost savings when firms

commit to lower environmental standards. Secondly, if firms do not adopt higher

environmental standards, new investment can be more costly. Finally, adhering to


31

higher environmental standards leads to several benefits for firms, including

heightening employee morale, that as a consequnce, increases productivity. Cahan,

Chen, Chen and Nguyen (2015) show that when firms have good CSR performance

and favourable media coverage, they experience an increase in firms’ value or lower

cost of capital. A new valuation model has been developed by Fatemi, Fooladi and

Tehranian (2015) with the purpose to evaluate the effects of CSR performance on the

value of firms and the results indicate a value creation for firms if they spend their

resources on the community, society or environment. However, Vernon (1992) and

Korten (1995) argue that it can be more costly for firms when recapitalising old

equipment that is not environmentally friendly, with a consequent decrease in

earnings which negatively affect the market value of firms.

The literature about the impact that environmental regulations have on risk is wide

spread. The fact that regulations including environmental regulations create

uncertainties on the market can lead to changes in stock prices and market volatility.

According to Ramiah et al. (2013), the Australian stock market can have mixed

reactions to environmental regulations and polluting (environmentally-friendly)

industries can become riskier (less risky). By studying 300 large public U.S. firms,

Feldman et al. (1996) examine the relationship between environmental management

and risk in order to understand how corporate environmental activities affect the firm

market value. Their results indicate that when firms invest in their environmental

management, they experience a significant reduction in perceived risk and an

increase in stock price of approximately 5%. Halkos and Sepetis (2007) further

analyse the stock prices of Greek firms and find that improvements in the


32

environmental management system and environmental performance cause a

reduction in firms’ beta.

Ramiah et al. (2013) observe that green policies affect both the short-term and long-

term systematic risk. The authors’ results show that, in case of stringent

environmental control, systematic risks of polluters increase and systematic risks of

environmentally-friendly industries decrease, and the reverse happens when the

policies are rejected. Ramiah et al. (2013) sustain that political uncertainties

surrounding a particular regulation cause changes in risks and the authors label it as

the diamond risk structure of environmental regulations.

Furthermore, Ramiah, Pichelli and Moosa (2015b) study the risk shifting pattern in

the U.S. and find an increase in short-term systematic risk of one of the leading

polluters (oil and gas refining industry). From their study it results that 47% of

industries are not affected by the announcements of environmental regulation, while

36% of industries experience an increase in short-term systematic risks, and 17% of

industries encounter a decrease in short-term systematic risks. In addition, Ramiah et

al. (2015b) claim that U.S. industries tend to be more responsive to environmental

regulations with respect to Chinese industries.

2.1.4 Social and Environmental Accounting and Reporting

The commitment of countries to reduce their carbon emission forces companies to

adopt a more socially responsible behavior. In the literature many researchers study

the relationship between environmental performance and environmental disclosures


33

and the results are quite heterogeneous. Numerous studies show poor environmental

performers tend to release more environmental disclosures while other studies find

firms tend to provide more disclosure if they have a high environmental performance

index. On the other hand, many articles argue that there is no relationship between

environmental performance and environmental disclosures.

Feldman, Soyka and Ameer (1996) focus their study on the importance of an

organization to be socially responsible. The authors find that when firms adopt better

environmental management and achieve higher environmental performance, they

have the tendency to experience lower risk and higher return. A study conduct by

Michelon, Pilonato, Ricceri and Roberts (2016) suggests that some firms may try to

cover up their environmental disasters, and corporate and financial frauds by

publishing their social and environmental reports.

Patten (2002) studies the relationship between environmental disclosures and

environmental performance of 131 U.S. companies by using the data obtained from

the Environmental Protection Agency’s Toxics Release Inventory (TRI) The author

finds that “higher levels of toxic releases (adjusted for firm size) are associated with

higher levels of environmental disclosure (measured using both content analysis and

financial report line counts)”. In other words, Patten (2002) highlights that firms tend

to release environmental disclosures if they are polluting more. Bewley and Li

(2000) arrived to similar conclusions studying the annual reports of 188 Canadian

manufacturing firms. The authors suggest a negative association between

environmental performance and environmental disclosures. Hughes, Anderson and

Golden (2001) analyse 51 U.S. manufacturing firms and they also find that


34

environmental disclosures are mostly released by poor environmental performers. In

their study, Freedman and Jaggi (2005) argue that the polluting firms in countries

that are committed to the Kyoto Protocol have relatively greater environmental

disclosures. The results are consistent also with Cho and Patten’s (2007) findings.

They conclude that “poorer environmental performance leads to higher levels of

disclosure”. Similarly, Farag, Meng and Mallin (2015) investigate the social

performance of Chinese listed non-financial companies in the Shanghai Stock

Exchange. They find that the high social disclosure is associated more with

environmentally sensitive industries and that little attention has been paid to ethical

issues. Interestingly, their findings show that the better the financial performance, the

worse the corporate social performance disclosure.

On the other hand, a positive correlation between environmental performance and

environmental disclosures has been spotted by a number of studies. Among them,

Al-Tuwaijri, Christensen and Hughes (2004) argue that there is a positive

relationship between environmental disclosures and environmental performance.

Similarly, in their study about environmental disclosures of 191 firms in 2003 from

pulp and paper, chemicals, oil and gas, metals and mining and utilities industries,

Clarkson, Li, Richardson and Vasvari (2008) find a positive relationship between

environmental performance and level of discretionary disclosures in environmental

and social reports.

Moreover, in the literature there are studies which find that the relationship between

environmental performance and environmental disclosures is not significant. For

instance, Ingram and Frazier (1980)study 40 firms supervised by the Council on


35

Economic Priorities (CEP) and their results do not show could any link between

environmental performance and environmental disclosures. Similar results are found

by Wiseman (1982), who analyses 26 of the largest U.S. firms that are monitored by

CEP. The author introduced an environmental index which covered economics

factors, environmental litigation, pollution abatement activities and other

environmental disclosures and classifies environmental disclosures based on the

nature of the disclosures (qualitative or quantitative). His finding show that the

relationship between CEP environmental performance rankings and the Wiseman

environmental disclosure index rankings is not statistically significant.

Similar results are obtained also by Freedman and Wasley (1990) who examine the

relationship between pollution disclosures and corporate pollution performance of

firms in steel, oil, pulp and paper, and electric utilities industries. The authors’

conclusion is that the relationship between pollution disclosures and firms’

environmental performance is not supported by any empirical evidence. Freedman

and Jaggi (2010) examine environmental performance of EU, Japanese and Canadian

firms and their environmental disclosures using GHG emission as a benchmark, and

indicate that firms with better environmental performance do not necessarily have

better environmental disclosures. More recently, Alrazi, De Villiers and Van Staden

(2016) study 205 firms from 35 countries and use CO2 emission intensity as a

benchmark for environmental performance, and claim that the level of overall

environmental disclosure is not influenced by environmental performance.


36

2.2 Environmental Regulation in China

“China is the world’s second largest economy, but the enormous costs of its growth

are becoming apparent. Residents of its boom cities and a growing number of rural

regions question the safety of the air they breathe, the water they drink and the food

they eat. It is as if they were living in the Chinese equivalent of the Chernobyl or

Fukushima nuclear disaster areas”. These are Wong’s words in his article Life in a

Toxic Country at The New York Times in 20133

Recent years have seen in the literature a growing number of studies related to

environmental regulations and their effects on economy in China. Moreover, the

mass media has been showing more and more attention on the environmental

situation in China and the effects on its citizens’ health. Given that this thesis is

focused on the analysis of China’s financial markets, in this section we review a

selection of papers and articles related to this topic.

Tanaka (2010) conducts a study to quantify the impacts of air pollution and related

regulations on infant mortality in China. The author exploits plausibly exogenous

variations in air quality generated by environmental regulations since 1995. These

legislations imposed stringent regulations on pollutant emissions from power plants.

The results of his study suggest that the regulations led to significant reductions in air

pollution and infant mortality rate (IMR). His estimations show that 25,400 fewer

infants died per year than would have died in the absence of the regulations,

3http://www.nytimes.com/2013/08/04/sunday-review/life-in-a-toxic-

country.html?pagewanted=all&_r=1&


37

corresponding to about a 21 percent decline in IMR. Moreover, his findings

highlights that the maternal exposure to pollution on fetal development plays a

crucial role. Tanaka’s (2010) study indicates that a one percent reduction in total

suspended particulates (TSP) results in a 0.95 percent reduction in IMR, whereas a

one percent reduction insulphur dioxide results in a 0.82 percent reduction in IMR.

The author also argues that the estimated impact of a unit change in TSP is of similar

magnitude to that found in the U.S., but the elasticity is substantially higher in China.

This further finding highlights the greater benefits associated with regulations when

pollution is already quite high.

Figure 2: Trends in Infant Mortality Rate. Tanaka, 2010

In this plot the author shows the general trend of infant mortality per 1,000

live births between the Two Control Zone (TCZ) and the non-Two Control

Zone (non-TCZ) localities. The annual mean is calculated using the total

population as the weight. The dotted vertical line indicates the timing of 1995

Pollution Prevention and Control Law (APPCL) amendment, and the solid

vertical line indicates the timing of TCZ policy implementation in January

1998. Note that the 1995 APPCL was amended in August. Because each

observation presents the annual average value, the dotted vertical line is


38

located at 1995, while the solid vertical line is located between 1997 and

1998. This is to clarify the timing of the TCZ policy implementation.

Hills and Man (1998) study the relationship between environmental regulators and

industrial enterprises in China. With the aim to explain the ‘implementation gaps’,

they present a model of the implementation process and use case studies from the

industrial city of Foshan in Guangdong Province. The authors argue that a decisive

role is played by the ‘informal relationships’ between individuals and organizations.

They claim that given the ‘cultural predisposition to harmony and consensus-

building among key actors’ and the importance of decentralized implementation

responsibilities in China, the achievement of national environmental policy

objectives can be constrained by weaknesses in the system at the local level.

Shi and Zhang (2006) adopt a multi-actor environmental governance model to

examine and understand the reason why a China's state-dominated system of

industrial pollution control has fallen in mitigating the environmental impacts of

rapid industrialization. According to the author, the initial failure of China’s

environmental regulation can be attributed to several factors. Firstly, China

developed its environmental regulation in the 1970s, with low experience and

essentially no institutional capacity. Secondly, China didn’t have a strong

environmental state, with large and effective monitoring and enforcement capacity.

Furthermore, the regime was mainly designed to target large state-owned enterprises

within a centrally planned economy via direct command-and-control interventions.

Finally, industry experienced constant and rapid change in the 1990s, both in in

terms of quantity and in terms of structure (quality).


39

According to Shi and Zhang (2006), in recent years the Chinese environmental state

has been changing along three parallel strategies:

• modernizing the existing environmental regulatory networks, in order to

enable the central regulatory agencies and institutions to adapt better to the

new circumstances of a globally integrated market economy;

• decentralizing environmental policy and strengthening local governments to

fulfill their environmental responsibilities;

• adopting a proactive approach to involve non-state actors, institutions and

mechanisms in environmental governance in pollution control.

Shi and Zhang (2006) conclude that in the long term, greater openness and

integration will be beneficial to the modernization of China’s industrial

environmental governance. However, they state that the question remains whether it

will be enough to protect China’s (and the global) environment; but there seems to be

little alternative.

Qi’s (2008):study aims at describing the environmental governance system in China;

at formulating a theoretical framework to explain the institutional constraints that

lead to environmental degradation; and finally at evaluating the effectiveness of

China’s environmental governance. The author compares common features of the

China and U.S. environmental governance systems that shape both each country’s

choice of environmental governance concepts and tools, and the way and

effectiveness which they are applied. The paper concludes by suggesting areas in

which further comparative understanding may be of value, including: (1) focusing on

better understanding of the role of plan and law in China’s governance system; (2)


40

comparing American Federal-state agreement system for implementation of

environmental law with China central-local system of target responsibility

agreements for implementation of the plan; (3) improving understanding of the

nongovernmental, as well as civil service, resources needed to assure compliance

with environmental laws and plans; (4) finding and adopting legal and institutional

means to resolve current difficulties in central-local and cross-border environmental

governance.

According to Mol (2009), China's system of environmental governance is changing

rapidly, resulting in new environmental institutions and practices. State authorities

rule increasingly via laws and decentralize environmental policymaking and

implementation. The author states that non-state actors – both private companies and

(organized) citizens – are given and taking more responsibilities and tasks in

environmental governance and this results in new relations between state, market and

civil society in environmental governance, with more emphasis on efficiency,

accountability and legitimacy.

One of the most contentious debates today is whether pollution-intensive industries

from rich countries relocate to poor countries with weaker environmental standards,

turning them into “pollution havens.”. Dean, Lovely and Wang (2009) estimate the

strength of pollution-haven behavior by examining the location choices of equity

joint venture (EJV) projects in China. A location choice model is derived from a

theoretical framework that incorporates the firm’s production and abatement

decision, agglomeration and factor abundance.


41

Dean at al. (2009) analyse a sample of 2,886 manufacturing joint venture projects

during 1993-96 and show that EJVs from all source countries go into provinces with

high concentrations of foreign investment, relatively abundant stocks of skilled

workers, concentrations of potential local suppliers, special incentives, and less state

ownership. Their findings show that environmental stringency does affect location

choice, but not as expected. In particular the authors argue that low environmental

levies are a significant attraction only for joint ventures in highly-polluting industries

with partners from Hong Kong, Macao, and Taiwan. In contrast, joint ventures with

partners from OECD sources are not attracted by low environmental levies,

regardless of the pollution intensity of the industry.

Peng, Tian, Tian and Xiang (2011) apply the impulse response function of VAR

model and the estimation variance decomposition method to investigate the two-way

dynamic relationship between environmental regulation and FDI during 1985 to

2009. Their findings show that the generalized impulse response of the impact effects

of environmental regulation on FDI become less and less in long-term, verifying

“hypothesis of pollution haven”. Furthermore, Peng at al. (2011) argue that the

inverse U-shape curve of “environmental regulation - FDI” depends on the choice of

regulation indicators. Through the analysis of the positive impulse response, the

authors illustrate that the inflows of FDI would cause the deterioration of ecology

and the intervene of governments, which gives pressure to the transformation of

environmental regulation standard.


42

Figure 3: The impact response curve of Environmental regulation to FDI. Peng at al.

(2011)

Figure 4: Impact response curves of FDI to environmental regulation. Peng at al. (2011)

Zheng and Shi (2016) conduct a study to investigate pollution haven hypothesis at

domestic level in China. Using panel data of 30 provincial level regions for the

period 2004 to 2013, this paper empirically examines to what extent multiple


43

environmental policies affect intra-country relocation of polluting industries in

China. The authors found that the implementation of both economic policy

instrument like pollution discharge fee and public participation like letter complaints

on environmental problems encourages industrial relocation, whereas the

implementation of environmental legal policy instrument like laws, regulations and

rules prevents polluting industries from relocating to other regions. Moreover, their

study demonstrate that the relocation effect of environmental policies varies with

industrial characteristics. In particular the authors argue that, compared with water

pollution-intensive industry, air pollution-intensive industry dominated by stated-

owned capitals are insensitive to legal policy instruments. Zheng and Shi (2016)

finally suggest that the validity of pollution haven hypothesis is jointly associated

with the type of environmental policy as well as industrial characteristics.

Ramiah, Pichelli and Moosa (2015a) study the effects of environmental regulation

announcements on corporate performance in China and their results show that

several polluting industries experience an increase in short-term systematic risk due

to the announcements of environmental regulations. However, from their study there

is no evidence of firms experiencing a decrease in short-term systematic risk due to

environmental regulations. Moreover, Ramiah et al. (2015a) observe no changes in

short-term systematic risk for the 81% of industries in China and they suggest three

possible outcomes for long-term systematic risk including increase, decrease and no

change in risk.


44

Figure5: Long-Term Climate Change Risk in China. Ramiah et al. (2015a)

2.3 Environmental and Natural Disasters Literature

Environmental disasters cause enormous losses of life and property every year, a

threat that is recognized and addressed in both the Sendai Framework for Disaster

Risk Reduction4 and the 2015 Sustainable Development Goals5. Organizations from

both the risk reduction and development fields are working to design programs that

build risk understanding and risk perception to encourage protective action in

communities that are often at risk from multiple, overlapping threats.

The empirical evidence shows natural disasters may have significant impact on stock

exchanges. Wang and Kutan (2013) analyse the impact of 84 Japanese natural

disasters on the domestic stock market for the period 1982 to 2011 and concluded

4http://www.unisdr.org/we/coordinate/sendai-framework

5http://www.un.org/sustainabledevelopment/sustainable-development-goals/


45

that natural disasters have an indirect impact on changing the volatility of stock

returns. The authors also show that an inefficient market response might be caused

by the delayed information due to the death and loss of the disasters.

Barton (2005) examined the US stock market performance after the Hurricane

Katrina and reported that stock markets reacted positively after the storm. The same

result was found after hurricanes Andrew, Hugo and Camille. Nevertheless,

Weiderman and Bacon (2008)test efficient market theory by examining the effect of

Hurricane Katrina on oil companies' stock prices. They conduct an event study

analysis on 15 firms with interests in the Gulf of Mexico and examines the effect of

Hurricane Katrina on stock price's risk adjusted rate of return before and after August

30, 2005. Their results show stock returns dropping significantly prior to Hurricane

Katrina reaching land. Weiderman and Bacon (2008) support semi-strong market

efficiency, reflecting that the market rapidly anticipated the devastation of Hurricane

Katrina. The authors conduct proper statistical tests for significance and the results

show that oil company stock price returns started a significant downturn up to 25

days prior to the hurricane event on August 30, 2005.

Worthington and Valadkhani (2004) investigated the impact of 42 natural disasters

(severe storms, floods, cyclones, earthquakes and bushfires) on the Australian stock

market. They used the daily price and accumulation returns from 1982 to 2002 for

the All Ordinaries Index (AOI).Applying autoregressive moving average (ARMA)

models, the authors’ findings indicate that different kinds of natural disasters lead to

mixed impacts on market returns and in particular bushfires, cyclones and

earthquakes have a major effect on market returns, unlike severe storms and floods.


46

Worthington et al. (2004) argue that net effects can be positive and/or negative with

most effects being felt on the day of the event and with some adjustment in the

following days.

The literature proposes several possible explanations to the correlation between

natural disasters and stock prices on exchanges. Fama (1970) sustains that markets

are semi-strong-form efficient and therefore prices react to public information

including the announcement of a firm, changes in economic policy, breaking through

of a new technology, regime change, and natural disasters.

Focusing on disasters caused by industrial activity, whenever a company is found to

be responsible and liable for environmental damages, injured parties and public

authorities are deemed to be compensated, according to the court decisions. Cash

costs and reputation damages will severely affect the company. Yet, the effects of

environmental pollution are not limited to fathomless and enduring damages at the

local level, but they spread to the whole economy, with effects on expectations about

growth, productivity, firm profitability and business risk (Jorgenson and Wilcoxen,

1990; Barbera and McConnell, 1986; Esty and Porter, 2002; Moosa and Ramiah,

2014). Changes in expectations are fatally going to induce stock price adjustments on

stock exchanges, and this will regard both companies directly involved in the

accident, and companies that might be indirectly affected.

Sullivan-Wiley and Short Gianotti (2017) address environmental hazard risk

perception in a multi-hazard context in eastern Uganda, with particular attention paid

to the role that risk reduction and development organizations (RDOs) play in shaping


47

risk perceptions, as well as their potential to influence protective action. To better

understand risk prioritization, the authors used survey data from farming households

to generate four indices reflecting several components of risk perception and to

predict holistic risk perception through multivariate regression analysis.

Sullivan-Wiley et al. (2017) find out that the factors shaping smallholder risk

perception vary among hazards within the study population and that characteristics of

both hazards and individuals are important. Furthermore, their results reveal a

relationship between risk perception, self-efficacy, and protective action, which

suggest that risk reduction and development programs can play an important role in

affecting both risk perception and the capacity of smallholders to respond to

environmental threats.

According to a great number of studies (Muoghalu et al., 1990; White, 1995;

Feldman et al., 1996; Klassen and McLaughlin, 1996; Oberndorfer, 2009; Flammer,

2012; Chan and Walter, 2014; Oestreich and Tsiakas, 2015; Pham et al., 2015;

Ramiah et al., 2013; Ramiah et al., 2015a; Ramiah et al., 2015b; Ramiah et al.,

2016), disasters’ outcry might lead policymakers to introduce more severe

regulations and binding requirements for manufacturing companies, causing a

reduction in profit margins and an increase in idiosyncratic risk

On the other hand, Neto, Da Silva Gomes, Bruni and Filho, (2017) investigate the

impact that environmental disasters have on the volume of socio-environmental

disclosure and investments of Brazilian companies from 1997 to 2012. They set the

level of socio-environmental disclosure and investment before the occurrence of the


48

accident and then compare it to the level of disclosure and investment after the

accident. Their results show that the companies reported a higher volume of socio-

environmental disclosure in the two years after the occurrence of the accidents – with

statistical significance of 2.9%. Statistically significant variations of 8.2% and 0.7%

were found in the totals of contributions to society and in environmental investments,

respectively. On the other hand, there was no statistically significant variation in the

internal social indicators

2.4History of Event Study Methodology

Recent years have seen a growing body of literature related to event study

methodology and its development Myers and Bakay (1948), Barker (1956, 1957,

1958), Ashley (1962), Ball and Brown (1968), Fama et al. (1969), Brown and

Warner (1980, 1985), Fama and French (1993, 2015), Carhart (1997), Ramiah, Cam,

Calabro, Maher and Ghafouri (2010), Ramiah (2012, 2013), Ramiah and Graham

(2013), Ramiah, Martina and Moosa (2013), Ramiah, Moosa, Pham, Scundi and

Teoh (2015), Ramiah, Regan-Beasley and Moosa (2016), Pham, Ramiah, Moosa and

Nguyen (2016) and Ramiah, Pham and Moosa (2016). However event study

methodology was first introduced in finance by Dolley (1933) who studied 95 stock

splits from 1921 to 1931.

Event study methodology is used not only to examine the effects of firm-specific

events, but also to analyse non-firm-specific announcements, such as earthquakes,

tsunamis, terrorist attacks, regulatory announcements, and many others. For instance,

Binder (1983, 1985) uses monthly and daily data to study 20 regulatory changes


49

between 1887 and 1978 and finds weak evidence that announcements of the

regulations affect the wealth of shareholders.

Binder (1998) observes that anticipation of the market to regulations and

uncertainties about event dates are the two major difficulties of event study of

regulation. However, the author argues event study remains a powerful tool to

inspect the effects of regulations when event dates around a policy are known.

Binder (1998) further introduces five methods to calculate abnormal return (AR) in

event study methodology: (1) mean adjusted returns, (2) market adjusted returns, (3)

market model (Fama, Fisher, Jensen and Roll, 1969), (4) the CAPM and (5) the

multifactor model—Arbitrage Pricing Theory (Ross, 1976). Brown and Warner

(1980) apply the event study methodology to monthly stock data and conclude that

multifactor models do not work better than the market model. According to Cam and

Ramiah (2014), event studies can have a wide range of results, depending on the

estimation techniques used. The authors observe fewer and smaller abnormal returns

than an evaluation based on Brown and Warner (1985), after controlling for

systematic risk factors.

In this chapter we review the development of event study methodology, analysing the

One-factor Model (Brown and Warner, 1985), the Three-Factor Model (Fama and

French, 1992) and the Four-Factor Model (Carhart, 1997). Secondly, we discuss how

event study methodology has been applied to environmental finance.


50

2.4.1 The One-Factor Model

Brown and Warner’s (1985) study is focused on the analysis of the characteristics of

daily stock returns with the aim to find out the effects they can have on event study

methodology. The authors first use various models to measure excess returns and

examine the statistical properties of both daily stock returns and excess returns. They

then build the samples by randomly selecting securities and event dates. After

postulating that no abnormal returns should be detected if they are measured

correctly, they estimate the probability of discovering a given level of abnormal

performance.

Brown and Warner (1985) highlight that one of the potential issues with using daily

data instead of monthly data is the risk of having a significant departure from

normality. Fama (1976) suggests that distributions of daily returns are fat-tailed

relative to a normal distribution. Similar finding for the distribution of daily excess

returns are shown by Brown and Warner (1985). Moreover, using the OLS method to

estimate market model parameters can create severe bias and inconsistency due to

non-synchronous trading between the security and the market (Dimson, 1979 and

Scholes and Williams, 1977).

A number of issues related to variance estimation has been also detected. For

instance, non-synchronous trading can lead to serial dependence on daily excess

returns (Ruback, 1982), cross-sectional dependence of the security-specific excess

returns (Brown and Warner, 1980; Beaver, 1981 and Dent and Collins, 1981), and

the stationarity of daily variances in which the variance of stock returns rises around


51

announcement dates such as earnings announcements (Beaver, 1968 and Patell and

Wolfson, 1979).

Abnormal returns are estimated by the authors using various measurements, whose

procedures are described here below.

Mean adjusted return

��,� = ��,� − �� (1)

ℎ� � �� = �� ∑ ��,�� (2)

and ��,� is the excess return of firm i at time t, ��,� is the arithmetic return of firm i

at time t and�� is the simple average daily returns of firm i in the (-244,-6) estimation

window.

Market adjusted return

��,� = ��,� − ��,� (3)

where ��,�is the market return at time t.

OLS market model

��,� = ��,� − �� − ��,� (4)

where ��and �� are estimated using OLS from the estimation period.

The authors then test the null hypothesis (zero abnormal returns on event day) using

test statistic which is calculated as follows:

��/ ��(��) (5)


52

where �� = �!"

∑ ��,�!"�� (6)

��(��) = #∑ (�� − ��$$$$)�� /238 (7)

��$$$$ = �� ∑ �� (8)

�� is the mean excess return of the sample at time t, (� is the number of firms at

time t, ��(��) is the standard deviation of mean excess returns of the sample at

time t and ��$$$$ is the simple average of mean excess returns over the estimation

period.

Scholes and Williams (1977) argue that a problem with this test statistic calculation

is that the degree of non-synchronous trading can simultaneously affect both average

returns and variance estimators, however it is still widely used in event studies

(Masulis, 1980; Dann, 1981; Holthausen, 1981; Leftwich, 1981; Ramiah et al., 2013;

Cam and Ramiah, 2014 and Ramiah et al., 2015). Furthermore, Brown and Warner

(1985) assume the test statistic is unit normal since the degrees of freedom surpass

200. They suggest the test statistic takes into account cross-sectional dependence in

the security-specific excess returns due to the use of time-series of average excess

returns such as portfolio excess returns, but it overlooks any time-series dependence

in excess returns.

2.4.2 The Three-Factor Model

Fama and French (1992) argue that there are other factors which can affect abnormal

returns, such as size and book-to-market equity, and show that these factors are

linked to economic fundamentals in which firms that have high (low) BE (the ratio of


53

a firm’s book value to its common stocks)/ME (stock price times number of shares)

tend to have low (high) earnings. Applying cross-section regressions of Fama and

MacBeth (1973), the authors find that the size and book-to-market equity can explain

the cross-section of average stock returns and common time-series variation in stock

returns on NYSE, Amex and NASDAQ stocks from 1963 to 1990.

Furthermore, Fama and French (1993) apply the time-series regression approach of

Black, Jensen, and Scholes (1971) to test various asset pricing models. Their findings

show that size and book-to-market equity are proxies for sensitivity to common risk

factors in stock returns. In their regression models, monthly stock returns are

regressed on excess market returns, size (SMB) and book-to-market equity (HML).

Fama and French (1993) use six portfolios formed from sorts of stocks on ME and

BE/ME to simulate the underlying risk factors in returns related to size and book-to-

market equity. The authors then rank all NYSE stocks on Center for Research in

Security Prices (CRSP) according to their sizes (multiplying number of shares by

price) in June of each year t from 1963 to 1991. They use median NYSE size to split

NYSE, Amex and NASDAQ stocks into two groups: small (S) and big (B) and break

NYSE, Amex and NASDAQ stocks into three book-to-market equity groups based

on the cut-off points for the bottom 30% (Low), middle 40% (Medium) and top 30%

(High) of the ranked values of BE/ME of NYSE stocks. Book-to-market equity,

BE/ME, is calculated as book common equity for the fiscal year ending in calendar

year t - 1 divided by market equity at the end of December of t – 1. Fama and French

(1993) note that they only include firms with ordinary common equity (as classified

by CRSP) in the tests. According to the authors, the reason for having three groups

on BE/ME and two groups on ME is because book-to-market equity has more


54

explanatory power in average stock returns in comparison to size. They then

construct six portfolios (S/L, S/M, S/H, B/L, B/M, B/H) from the intersections of the

two ME and the three BE/ME groups. The monthly value-weighted returns in these

portfolios are calculated from July of year t to June of t + 1 and the authors reform

the portfolios in June of t + 1. The returns are calculated at the beginning of July of

year t in order to make sure that book equity for year t – 1 is known. Fama and

French (1993) note that the conditions for a firm to be included in the tests are (1)

availability of CRSP stock prices for December of year t – 1 and June of year t, (2)

COMPUSTAT book common equity for year t – 1, and (3) availability of data on

COMPUSTAT for two years.

According to Fama and French (1993), the SMB (small minus big) portfolio mimics

the risk factor in returns related to size and it is the monthly difference between

simple average of the returns on three small-stock portfolios (S/L, S/M and S/H) and

simple average of the returns of three big-stock portfolios (B/L, B/M, and B/H).

Since these small-stock and big-stock portfolios have about the same weighted-

average book-to-market equity, the authors claim the SMB portfolio’s returns are

largely free of the influence of BE/ME and that the SMB portfolio concentrates on

the difference of return behaviors between small and big stocks. Fama and French

(1993) also explain that the HML (high minus low) portfolio mimics the risk factor

in returns related to book-to-market equity in which HML is the monthly difference

between simple average of the returns of two high BE/ME portfolios (S/H and B/H)

and simple average of the returns of two low BE/ME portfolios (S/L and B/L). The

returns from high and low BE/ME portfolios have about the same weighted-average

size, hence the authors argue that the HML factor is largely free of the size factor in


55

returns and mainly focuses on the difference in return behaviors of high and low

BE/ME firms. The last factor is the market factor and the proxy for the market factor

is the excess market return, RM-RF. In their study, the authors calculate market

return, RM, using the value-weighted portfolio of the stocks in the six size-BE/ME

portfolios together with the negative-BE stocks which are excluded from the

portfolios earlier and use the one-month bill rate as the risk-free rate, RF. The

regression model is then as follows:

R(�)– RF(�) = α + b/RM(�) − RF(�)1 + sSMB(�) + hHML(�) + �(�) (9)

where R(�) is the return on the 25 stock portfolios at time t, RF(�) is one-month

Treasury bill rate at time t, RM(�) is the market return at time t, SMB is the size

factor, HML is the book-to-market factor, �(�) is the error term, α is the intercept and b, s, h are coefficients of market risk premium (RM(�) − RF(�)), SMB(�) and HML(�)

respectively.

Fama and French (1993) conclude their methods can be used to select portfolios, to

evaluate portfolio performance, to measure abnormal returns (which is of interest to

this thesis) and to estimate the cost of capital. They also argue the three factors, RM-

RF, SMB and HML, are able to explain the common time-series variation in stock

returns and the cross-section of average stock returns, and the inclusion of SMB and

HML in the regressions can isolate firm-specific components of returns.

2.4.3 The Four-Factor Model

In the following years many researchers attempt to improve Fama and French’s

(1993) cross-sectional analysis method by introducing a financial momentum factor


56

(MOM)into the 3-factor model. The most notable works are conducted by Jegadeesh

and Titman (1993), Chan, Jegadeesh and Lakonishok (1996) and Carhart (1997).

Jegadeesh and Titman (1993). Forinstance, Jegadeesh and Titman (1993) analyse the

profitability of 16 different strategies with different holding periods from 1 to 4

quarters (the portfolios are constructed using stock returns over the past 1, 2, 3 and 4

quarters) to study the efficiency of the stock market and suggest that profitable

trading strategies based on historical stock performance exist if there is overreaction

or underreaction on the market. The authors also include portfolios with overlapping

holding periods in their strategies to increase the power of their test in which the

strategies hold a selected number of portfolios in month t as well as in previous K – 1

months where K is the holding period. Jegadeesh and Titman (1993) specifically

focus on a J – month/K – month strategy that consists in selecting stocks based on

their returns over the past J months and holding them for K months. In order to

construct the strategy, securities are ranked in ascending order using their returns in

the past J months at the beginning of month t. The authors then use the rankings to

form ten decile portfolios which contain equally weighted stocks in every decile and

label the top decile as “losers” and the bottom decile as “winners”. In this strategy,

the winner (loser) portfolio is bought (sold) and held for K months in each month t

and it closes out the position initiated in month t – K. They also revise the weights on

�8of the securities of the portfolio in any month t and carry over the rest from the

previous month.

Jegadeesh and Titman (1993) use these strategies to examine stock returns around

quarterly earnings announcement dates which occur within the last 36 months and


57

document positive average returns around quarterly earnings announcement dates

following favourable earnings surprises in the previous quarter. Moreover their

findings show negative returns four quarters after a positive earnings surprise and

negative returns around announcement dates from months 11 to 18. Jegadeesh and

Titman (1993) state that stock prices are altered temporarily from their long-run

values due to investors who buy past winners and sell past losers and it causes

overreaction in stock prices6. The authors also suggest that there is market

underreaction to information about short-term forecasts and overreaction to

information about long-term prospects.

Amore comprehensive study is then conducted by Chan, Jegadeesh and Lakonishok

(1996). They analyse stock returns around earnings surprises (earnings momentum

strategies) and confirm that “drifts in future returns over the next six and twelve

months are predictable from a stock's prior return and from prior news about

earnings”. The authors use a sample consisting of listed firms on NYSE, Amex and

NASDAQ during the period from 1977 to 1993 and exclude closed-end funds, Real

Estate Investment Trusts (REITs), trusts, American Depository Receipts (ADRs),

and foreign stocks from the sample. The sample also includes all firms with coverage

on both CRSP and COMPUSTAT and the analysts’ earnings forecasts are collected

from Lynch, Jones and Ryan Institutional Brokers Estimate System (I/B/E/S)

database. Chan et al. (1996) rank stocks based on their past returns or a measure of

earnings news at the start of every month in the sample period. After ranking the

6 See Ramiah and Davidson (2007), Xu, Ramiah, Moosa and Davidson (2016), Ramiah, Xu and

Moosa (2015), Anne and Ramiah (2016) and Anne, Ramiah and Moosa (2016) for extensive work on

underreaction and overreaction.


58

stocks, they construct ten decile portfolios using the ranked stocks (the stocks are

equally weighted) and use NYSE stocks which have reported earnings within the

prior three months as breakpoints. The authors use three different measures of

earnings news including standardised unexpected earnings (SUE), cumulative

abnormal return (CAR) around the most recent announcement date of earnings up to

month t and changes in analysts’ forecasts of earnings in their momentum strategies

and report buy-and-hold returns for each strategy. After examining the momentum

strategies, Chan et al. (1996) show that “price momentum effect tends to be stronger

and longer-lived than the earnings momentum effect” and conclude that using a

stock’s prior six-month return and the most recent earnings surprise can help to

predict future returns7. Finally, Chan et al. (1996) suggest their evidence gives rise to

a delayed reaction of stock prices to the past returns and earnings announcements.

Furthermore, Carhart (1997) adds a momentum factor capturing Jegadeesh and

Titman’s (1993) one-year momentum anomaly into Fama-French Three-Factor

Model. The author finds that his Four-Factor Model can explain time-series

variations in returns, and that estimations are not significantly affected by

multicollinearity. In the study, Carhart (1997) employs three models including

CAPM, Fama and French Three-Factor Model and his Four-Factor Model to explain

returns. The models are respectively:

�� = ��9� + ��9:�;�� + �� (10)

7 See Ramiah, Cheng, Orriols, Naughton and Hallahan (2011), Ramiah, Mugwagwa and Naughton

(2011), Mugwagwa, Ramiah, Naughton and Moosa (2012), Ramiah, Li, Carter, Seetanah and Thomas

(2016), Mugwagwa, Ramiah and Moosa (2015) for extensive work on contrarian and momentum

strategies.


59

where t = 1, 2, …, T, �� is the excess return of the portfolio in one month using

CAPM, :�;�� is the excess return on CRSP value-weighted portfolio of all

NYSE, AMEX and NASDAQ stocks at time t,�� is the error term, ��9� is the

intercept and ��9 is the coefficient of :�;�.

�� = ��9� + <�9�=�;� + >�9�=?� + ℎ�9@=A� + �� (11)

where �� is the excess return of the portfolio in one month using Fama and French

Three-Factor Model, �=�;� is the market excess return at time t, SMB and HML

are returns on value-weighted, zero-investment, factor-mimicking portfolios for size

and book-to-market equity,�� is the error term, ��9� is the intercept and <�9 , >�9 , ℎ�9

are the coefficients of �=�;�, SMB, HML respectively

�� = ��9� + <′�9�=�;� + >′�9�=?� + ℎ′�9@=A� + C�9D�1F�� + �� (12)

where �� is the excess return of the portfolio in one month using Carhart Four-

Factor Model, D�1F�� is the returns on value-weighted, zero-investment, factor-

mimicking portfolio for one-year momentum in stock returns at time t, �� is the

error term, ��9� is the intercept and <′�9 , >′�9 , ℎ′�9 , C�9 are the coefficients of �=�;�, SMB, HML,D�1F�� respectively.

In his study, Carhart (1997) collects SMB and HML values from Gene Fama and

Ken French and constructs PR1YR as “the equal weight average of firms with the

highest 30 percent eleven-month returns lagged one month minus the equal-weight

average of firms with the lowest 30 percent eleven-month returns lagged one month”.

The portfolios consist of all NYSE, AMEX, and NASDAQ stocks and are re-formed

monthly. He also finds pricing errors of the CAPM and the Fama and French three-

factor model are significantly improved by the four-factor model by examining

pricing errors on 27 portfolios which are taken from Carhart, Krail, Stevens and


60

Welch (1996). Finally, he suggests that the four-factor model eliminates almost all of

the patterns in pricing errors and explains the cross-sectional variation in average

stock returns.

2.4.4Event Study Methodology applied to Environmental Finance

Ramiah, Martin and Moosa (2013) employ event study methodology to examine the

effects of 19 environmental policies on the Australian equity market and find mixed

reactions from the Australian industries. They calculate AR using CAPM, then group

them into industries to obtain the average industry (I) abnormal returns at time t,

ARIt. The standard t statistic for an industry’s abnormal return is calculated to

examine if the result is statistically different from zero. The authors propose three

possible industry reactions when an environmental policy is announced and the

outcomes are:

1. ARIt = 0

2. ARIt> 0

3. ARIt< 0

They explain that the first outcome arises when the introduction of green policies

does not cause changes in revenues or costs of the industries or government subsidies

offset the decrease in revenues of the industries. The second outcome occurs when

the green policies positively affect the industries such as renewable energy or

environmentally-friendly businesses. The positive effect can be also explained by the

ability polluting industries to pass the extra costs onto the consumers. The third


61

outcome occurs if the demand for a product is elastic or the polluting industries are

not able to pass the costs onto the consumers.

Cumulative abnormal return (CAR) of five trading days after the announcement date

are examined by the authors in order to control for the delayed reaction to the

announcements of green policies. Furthermore, the authors calculate and use at-test

to determine the statistical significance of cumulative abnormal returns. Ramiah et al.

(2013) conduct a number of robustness tests including the Corrado (1989) non-

parametric ranking test, Chesney et al. (2011) non-parametric conditional distribution

approach, the removal of firm-specific information, and estimation of abnormal

returns that integrates market risk premia representing Asia ( ��HI�J − K�HI�J), Europe ( L��MNOPQR − K�

MNOPQR) and the U.S. ( L��ST − K�ST).

In their study, the authors also estimate changes in systematic risk following

announcements of green policies. They incorporate interaction variables into the

CAPM to capture the average changes in which they create an aggregate dummy

variable (AD) which takes a value of one on the announcement date and zero

otherwise for 19 announcement dates. The regression model is described here below:

U� − K� = �UV + �U�/ �� − K�1 + �U�/ �� − K�1 ∗ �� + �U� ∗ �� + XU� (13)

where U� is the return of industry I at time t, K� is the risk-free rate at time t, �� is

the market return at time t, AD is the dummy variable which takes a value of one on

the announcement date and zero otherwise, �UV is the intercept of the regression

equation in which E(�UV) = 0, �U� is the average short-term systematic risk of industry


62

I, �U�captures the change in systematic risk of industry I, �U� captures the change in

intercept and X� is the error term.

Ramiah et al. (2013) suggest that the outcomes of each announcement can cancel

each other in the aggregate risk model, hence they use the disaggregate model to

capture the effects of each announcement. The authors create an individual dummy

(ID) for each announcement (g) which has a value of one and zero otherwise and

then multiply each dummy variable by the market risk premium to acquire 19

interaction variables whose coefficients are the short-term change in systematic risk.

The model is written as follows:

U� − K� = �UV + �U�/ �� − K�1 + ∑ �U,Y� / �� − K�1 ∗ Z�[��[�� + XU� (14)


the market return at time t, ID is the individual dummy variable which takes a value

of one on the announcement date and zero otherwise, �UV is the intercept of the

regression equation in which E(�UV) = 0, �U� is the short-term systematic risk of

industry I, �U�captures the change in systematic risk of industry I following each

individual announcement and ε]^ is the error term.

Ramiah et al. (2013) remove the additive dummy which captures the change in the

intercept in equation (17) in order to address the multicollinearity caused by high

correlation amongst the individual dummy variables,. Graham and Ramiah (2012)

propose an alternative model in which the value of individual dummy variables is

one for the first 15 days and zero otherwise and the model is rewritten as follows:


63

U� − K� = �UV + �U�/ �� − K�1 + ∑ �U,Y� / �� − K�1 ∗ Z�[��[�� + ∑ �U,Y� ∗��[��

Z�[� + XU� (15)


the market return at time t, ID is the individual dummy variable which takes a value

of one on the first 15 days from the announcement date and zero otherwise, �UV is the

intercept of the regression equation in which E(�UV) = 0, �U� is the short-term

systematic risk of industry I, �U�captures the change in systematic risk of industry I

following each individual announcement, �U� captures the change in intercept

following each individual announcement and X� is the error term.

In order to study the effects of environmental regulations on the long-term systematic

risk, the authors re-estimate equations (16), (17) and (18) in which the aggregate

dummy variable has the value of zero prior to the first announcement and one for the

subsequent periods, whereas the individual dummy variables has a value of zero

prior to each announcement and one thereafter. Ramiah et al. (2013) use Wald test to

check for redundant variables when there is multicollinearity, AR and MA terms are

used to control for autocorrelations and apply different GARCH specifications to

correct ARCH effects.

Furthermore, Ramiah, Pichelli and Moosa (2015a) examine the effects of Chinese

environmental regulations on corporate performance and find mixed results for

industries’ returns but little evidence on changes in systematic risk. They hypothesise

that announcement of green policies has a negative (positive) impact on the wealth of

the investor in polluting (environmentally-friendly) industries while no change in


64

abnormal returns should be observed if the industries have no major exposure to

pollution. The authors apply different asset pricing models, such as the rolling

average model, market model, CAPM, Fama and French Three-Factor Model, and

Carhart Four-Factor Model to estimate the AR and the differences amongst these

asset pricing models lie in a number of risk factors which the models control for. The

models are written as follow:

��J,_U = �JU − `(�J,_U ) (16)

where ��J,_U is the abnormal return of industry I on announcement a using asset

pricing model v, �JU is the return of industry I on announcement a and `(�J,_U ) is the expected return of industry I on announcement a using asset pricing model v. The

expected return is calculated using the following asset pricing models:

Rolling Average Model

`/�J,_��U 1 = ��V ∑ ��U��V (17)

where `/�J,_��U 1 is the expected return of industry Ion announcement a using rolling

average model and ��U is the daily return of industry I at time t.

Market Model

`/�J,_��U 1 = �V,J_�� + ��,J_��( ��) (18)

where `/�J,_��U 1 is the expected return of industry Ion announcement a using market

model, �� is the market return at time t, �V,J_�� is the sum of risk-free rate and the

intercept � (which is expected to be zero) and ��,J_�� is the coefficient of market

return.


65

CAPM

`/�J,_��U 1 = �V,J_�� + ��,J_��( �� − K� ) (19)

where `/�J,_��U 1 is the expected return of industry Ion announcement a using

CAPM, �� is the market return at time t, K� is the risk-free rate at time t, �V,J_�� is the

sum of risk-free rate and the intercept � (which is expected to be zero) and ��,J_�� is

the coefficient of market risk premium.

Fama and French Three-Factor Model

`/�J,_��U 1 = �V,J_�� + ��,J_��/ �� − K�1 + ��,J_��(�=?�) + ��,J_��(@=A�) (20)

where `/�J,_��U 1 is the expected return of industry Ion announcement a using Fama

and French three-factor model, , �� is the market return at time t, K�is the risk-free

rate at time t, �=?� is the size factor at time t, @=A� is book-to-market equity factor

at time t, �V,J_�� is the sum of risk-free rate and the intercept � (which is expected to be zero) and ��,J_��, ��,J_��, ��,J_�� are the coefficients of market risk premium, SMB and

HML respectively.

Carhart Four-Factor Model

`/�J,_�aU 1 = �V,J_�a + ��,J_�a/ �� − K�1 + ��,J_�a(�=?�) + ��,J_�a(@=A�) +��,J_�a(=b=�) (21)

where `/�J,_�aU 1 is the expected return of industry Ion announcement a using

Carhart four-factor model, , �� is the market return at time t, K�is the risk-free rate


66

at time t, �=?� is the size factor at time t, @=A� is book-to-market equity factor at

time t, =b=� is the financial momentum factor, �V,J_�a is the sum of risk-free rate and

the intercept � (which is expected to be zero) and ��,J_�a, ��,J_�a, ��,J_�a, ��,J_�a are the

coefficients of market risk premium, SMB, HML and =b=� respectively.

Ramiah et al. (2015a) use a standard t test to determine statistically significant

abnormal returns in which standard deviation of the abnormal returns is estimated

using a window of 260 days (244 days prior to the event date, event date and 15 days

after the event date). They also control for delayed reaction by estimating the

cumulative abnormal returns over the periods of 5, 10 and 20 days and calculate

CAR of 260 days and 520 days to capture the long-term effects of the green policies.

The authors estimate CAR of 5 and 10 days prior to the event to check for

information leakage and remove all firms which release firm-specific information

within 15 days prior and after the event date as a robustness test. Finally, Ramiah et

al. (2015a) assess changes in short-term and long-term systematic risk by applying

the same methodology as Ramiah et al. (2013).


67


68

Chapter 3

Methodology


69


70

3Methodology

In order to analyse the effects of the sample events on the Chinese stock prices,

following Brown and Warner’s (1985) average modified model, daily returns are

adjusted to obtain the ex post ‘abnormal’ returns, considering three different

benchmarks:

(i) the return predicted by the Capital Asset Pricing Model (Sharpe, 1964);

(ii) the Fama and French (1993) three-factor model,

(iii) the Carhart (1997) four-factor model.

In this chapter we firstly describe the Abnormal Return analysis. Secondly we show

how the robustness tests have been conducted in order to address different issues that

can raise with the abnormal return estimations. In particular, we conduct the Non

Parametric Ranking Test (NPRT) and the Non Parametric Conditional Distribution

approach (NPCD) to address the non-normality issue. Furthermore, we control for

the firm-specific information and market integration spillover effects.

Finally we estimate the change in systematic risk, applying GARCH, threshold

ARCH (TARCH), exponential GARCH (EGARCH) and power-ARCH (PARCH).

3.1 Abnormal Return

Daily returns (DRit) at time t for all securities in the sample are represented by the

first natural logarithmic difference of the underlying index value (RI) obtained from

Datastream:

DRd^ = ln g hijkhijklm

n (22)


71

The event ‘abnormal’ returns(ARd^) for each securityi = 1, 2, 3… N across t = 1, 2,

3… T time period (days) is calculated as

�� = �� − `(��) (23)

where E(Rd^)is the expected return of firm i at time t and is estimated according to

the three benchmarks adopted. The models are as follows:

Capital Asset Pricing Model (CAPM):

E(Rd^�) = βd^�V + βd^�� (Rr^ − Rs^) (24)

where E(Rd^�) is the expected return of firm i at time t using the CAPM, Rr^ is the

market return, Rs^ is the risk-free rate and βd^�V and βd^�� are the estimated parameters

from a rolling CAPM over a period of previous 260 days.

Fama and French Three-Factor Model:

E(Rd^�) = βd^�V + βd^�� (Rr^ − Rs^) + βd^�� (SMB^) + βd^�� (HML^) (25)

where E(Rd^�) is the expected return of firm i at time t using Fama and French three-

factor model, SMB^ is the size factor, HML^ is the book-to-market factor,βd^�V is the

sum of risk-free rate and the intercept α andβd^�� , βd^�� , βd^�� are the coefficients of

Rr^ − Rs^, SMB^ , HML^respectively.

Carhart Four-Factor Model:

E(Rd^�) = βd^�V + βd^�� (Rr^ − Rs^) + βd^�� (SMB^) + βd^�� (HML^) + βd^�� (MOM^) (26) whereE(Rd^�) is the expected return of firm i at time t using Carhart four-factor

model, Rr^ and Rs^ are the market return and the risk-free rate respectively, SMB^ is

the size factor, HML^ is the book-to-market factor, MOM^ is the financial momentum


72

factor, βd^�V is the sum of risk-free rate and the intercept α and βd^�� , βd^�� , βd^�� , βd^�� are

the coefficients of Rr^ − Rs^, SMB^, HML^, MOM^respectively. The size factor

(SMB), the book-to-market factor (HML) and the momentum factor (MOM) are not

readily available for China, hence we computed them following the instructions

available on Kenneth French’s official website8.

The abnormal returns are then grouped into industries to obtain the average industry

index (I) abnormal return at time t, ARi^. The latter is computed using the following

equation:

ARi^ = �u ∑ ARd^ud�� (27)

where N is the number of firms represented in the index.

Assuming that the abnormal returns of the portfolios are normally distributed, their

statistical significance is analysed through the standard t-statistics:

t = whxkyz(whxk) (28)

where SD(ARi^) is the standard deviation of the abnormal returns of industry I in a

window of 244 days prior to the event, the event t and 15 days after the event.

As we already mentioned in the previous chapter, Ramiah, Martin and Moosa (2013)

suggest that the estimation of AR gives rise to three possible outcomes:

1. ARIt = 0

2. ARIt> 0

8http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/six_portfolios.html

http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/det_mom_factor.html


73

3. ARIt< 0

where ��U� is the abnormal return of industry I at time t.

The implicit assumption is that the abnormal return of an industry is a function of

total revenue minus total cost. The first outcome of zero abnormal return occurs

when neither revenue nor cost change following an environmental disaster or

pollution alert. It may also occur if the industry experiences a decrease in revenue,

which is offset by a decrease in cost (or vice versa). Under this scenario, the wealth

of shareholders remains unchanged. The second outcome is that there is wealth

creation for shareholders represented by positive abnormal return. Under the third

scenario of negative abnormal return, there is wealth destruction for the shareholders.

Following the literature, we use this methodology to find out whether or not

environmental disasters an pollution alerts have an evident impact on the most

polluting industries. Our expectations are that these kind of events should negatively

affect shareholders’ wealth of the most polluting industries. On the other hand, we

don’t have clear expectations about their effects on environmentally-friendly

business

Under Efficient Market Hypothesis(EMH), the stock market reacts instantly to new

information arrival as prices reflect information content of the announcement

instantly. However, when EMH does not hold, we must estimate the CAR over j

trading days. To that end, Cumulative abnormal return (CARIT) are finally computed

for every index, across j = 1, 2, 3… T time period (days) where T=5 and T=10:

=ITCAR∑=

T

j

IjAR1 (29)


74

If we discard the EMH and assume that market participants tend either to over-react

or under-react, the CAR approach enables us to find out whether the market reverts

back to its mean price or continues to deviate. Similar to the abnormal return

approach, the t-test is used to determine the statistical significance of cumulative

abnormal returns.

3.2 Robustness Tests

The robustness of the abnormal return analysis is tested introducing a number of

alternative specifications. Daily abnormal returns in a time series tend to be relatively

small but they are significantly larger around events where they appear to be outliers,

with the capability of distorting the distribution of abnormal returns, thus resulting in

high kurtosis, positive skewness and non-normality (Chesney et al., 2011). The

standard errors used for the computation of the standard t-statistics exhibit these

potential characteristics, which makes it necessary to check the validity of our

results. Therefore we adopt the Non Parametric Ranking Test (Corrado, 1989) and

the Non Parametric Conditional Distribution approach (Chesney et al., 2011) to

address the non-normality issue. Furthermore, we control for the firm-specific

information and market integration spillover effects.

The Non Parametric Ranking Test (NPRT) requires the transformation of abnormal

returns into ranks over a combined period of 260 days which is split into 244 days

before the event and 15 days afterwards. The ranks are then compared with the

expected average rank under the null hypothesis of no abnormal returns. A non-

parametric t-statistic is accordingly calculated to test the null hypothesis. The


75

abnormal returns (ARit) of each firm are transformed into rankings (Kit) over the

combined testing period of 260 days (Ti). This is calculated as:

Kit=rank(ARit) (30)

where Kit represents the rank of each firm at time t and ARitrepresents the abnormal

returns of firm i at time t. This expected average rank is denoted as K| d and is

calculated as:

K| i=0.5+Ti

2 (31)

From here the non-parametric t-statistic (tnp) is calculated as:

tnp=1N

∑ (Ki-K| i)Ni=1

SD(K|) (32)

where SD(K|) represents the standard deviation of the average rank which in turn was calculated as:

SD(K|)=#1

T∑ i

N2Tt=1 ∑ (Kit-K| i )

2 (33)

where N is the number of firms.

We also use the Non Parametric Conditional Distribution approach (NPCD)

suggested by Chesney et al. (2011) as an alternative robustness test and here below

we describe the approach adopted by the authors. This methodology is used to assess

that the abnormal returns on the event dates are outliers, generally located in the tails

of a particular distribution. The kernel regression technique is applied to do the

estimations, because it does not assume any underlying distribution and for this

reason it is categorized as a non-parametric technique. Chesney et al. (2011) suggest

that this non-parametric estimation lets “the data speak for itself”.


76

Chesney et al. (2011) apply a local polynomial regression (LPR) to time-series stock

data in order to build the non-parametric conditional distribution of stock returns.

The authors do not “compute any test statistics to check the significance of negative

abnormal and/or extreme movements in the market due to the event”, they analyse

the value of conditional probability of a return for each event in which the return can

be less than or equal to the actual one on the event date. The conditional distribution

function for any AR time series is:

π(ar|ar^��) ≡ P(ARd ≤ ar|ARd^�� = ar^��) (34)

When the conditional cumulative probability of the return on the general index

(which is less than or equal to that on the event date) turns out to be less than 0.05,

we conclude that the event has an extreme effect on the market.

The authors then apply a local polynomial fitting to time-series data of returns, ��,

using the following expression:

∑ (Y�� F� − �V − ��(�� − �V))��(�� − �V) (35)

where F� = Z(�� ≤ �), � is the return on the event date t, i= (1,…, n) and n = 200

which is the sample size, �� = ��, �V = ��, ℎ is the bandwidth and �� is the

kernel function.

As Fan and Yao (2003) suggest, the authors use a normal reference bandwidth

selector to define an optimal bandwidth, ℎ�PQ�,Y, for the Epanechnikov kernel as

2.34�I�� a⁄ where �I is a standard deviation of a sample. Chesney et al. (2011)

claim the implementation of this model leads to point estimates ��V and �� in which:

�� = (��)��F (36)


77

where is the diagonal matrix whose ith element is ��(�� − �V) and � is a design

matrix with a first column of ones.

After doing their estimations, Chesney et al. (2011) sustain that a point estimate ��V

corresponds to a conditional probability of stock returns, which is less than or equal

to that empirically observed on the event day and when conditioning is completed on

the value of return on the previous day. The authors also consider the delayed

response of the market to an event by estimating a non-parametric conditional

distribution of non-overlapping 6-day CAR using the same logic.

Global exchanges are interrelated (Worthington and Higgs, 2004), so Chesney et al.

(2011) and Ramiah et al. (2015a; 2015b) highlight the need to control for

synchronicity, stock market integration, and spillover effects in event study. To this

extent, abnormal returns are adjusted by augmenting the asset pricing models with

three market risk premia (source: Datastream) representing Asia ,

Europe and the U.S. and the model is as follows:

ARdâ = Rdâ − βdâV + βdâ� (Rr^ − Rs^) + βdâ� /Rr^w�d� − Rsw�d�1 + βdâ� /Rr^�� −

Rs^��1 + βdâ� /Rr^�y − Rs�y1 (37)

where ARdâ is the abnormal return of firm i at time t using this model, Rdâ is the

daily return of firm i at time t,Rr^ − Rs^ is the local market risk premium, Rr^w�d� − Rsw�d� is the Asian market risk premium, Rr^

�� − Rs^��

is the European market

risk premium, Rr^�y − Rs�y is the U.S. market risk premium, βdâV is the sum of risk-

free rate and the intercept α and βdâ� , βdâ� , βdâ� , βdâ� are the coefficients of the local,

Asian, European and U.S. market risk premium respectively.

( )Asia

ft

Asia

mt rr ~~ −

( )Europe

ft

Europe

mt rr ~~ − ( )US

ft

US

mt rr ~~ −


78

Another problem that may be encountered is the influence of firm-specific

information on abnormal returns. For instance, if firm-specific information becomes

available on the day when a green policy is announced, the results will reflect a

combination of firm-specific information and the announcement of green policies. It

is not accurate to argue that the observed abnormal return is a result of the green

policy—at worst we cannot determine how much of the abnormal return is associated

with the green policy.

In order to isolate firm-specific effects, we exclude from the analysis any company

that publicly released specific announcements in the 15 days on either side of the

event day, since this signal may generate unexpected returns and interfere with our

empirical analysis. Firm-specific information is defined as any announcement made

by that firm on the stock exchange. Excluding firms that made specific

announcement around the event date, leads us to the outcomes described here below:

� ��U�K�O��IQR��K�� = ��U�

� ��U�K�O��IQR��K�� < ��U�

� ��U�K�O��IQR��K�� > ��U�

where ��U�K�O��IQR��K��

is the abnormal return of industry I at time t after firms with

firm-specific news are removed from the industry and ��U� is the abnormal return of

industry I at time t.


79

3.3 Risk Analysis

To test for changes in systematic risk, we adjust the CAPM by adding specific

dummy variables. An aggregate dummy variable (AD) is created to represent the

sample events. This dummy variable is multiplied by the market risk premium to

create the first interaction variable. The model is as follows:

IttItftmtIftmtIIftIt ADADrrrrrr εββββ ~*]~~[]~~[~~ 3210 ++−+−+=− (38)

where r]iîs industry I’s return at time t, r]sîs the risk-free rate at time t, r]rîs the

market return at time t, AD is a dummy variable that takes the value of one on the

event date and zero otherwise, ε]i^ is the error term, βiV is the intercept of the

regression equation (E(βiV) = 0), βi�is the average short-term systematic risk of the

industry, βi�captures the change in the industry risk, and βi�measures the change in

the intercept of Equation (15). The equation is estimated to calculate the aggregate

effect of the environmental events on the stock market.

One of the problems with the aggregate model is that effects of opposite outcomes

from different events may cancel each other. In order to disaggregate the effects into

specificevents we use another model which allows us to identify the exact

contribution of each event (g). An individual dummy variable (ID) is created and is

then multiplied by the market risk premium to obtain interaction variables whose

coefficients represent the short-term change in systematic risk originating from news

arrival. The model is written as follows:

it

N

g

gtftmtnIftmtIIftIt IDrrrrrr εβββ ~*]~~[]~~[~~

1

2

,

10 +−+−+=− ∑= (39)


80

Ramiah et al. (2015a; 2015b) show that announcements of new environmental

policies affect the long-term systematic risk of stock markets, therefore we analyse if

chemical disasters and pollution alerts have similar effects. Equation (37) is

calculated again and the individual dummy variables (ID) take the value of zero prior

to the event and one for the subsequent periods.

The daily data can lead to problems like autocorrelation and ARCH effects:

therefore, to control for autocorrelation, we introduce appropriate AR and MA terms

and to correct the ARCH effects we apply various GARCH specifications. In this

study, the GARCH (1,1) conditional variance equation is as follows:

σi� = ωiV + ωi�εi^�� + ωi�σi^�� (40)

where σi� is the conditional variance, ωiV is a constant term, ωiV > 0, ωi� > 0, ωi� >0 �¡ ωi� + ωi� < 1, εi^�� is the ARCH term and σi^�� is the GARCH term. The

ARCH term represents information about volatility in the previous period whereas

the GARCH term represents the previous period’s forecast variance.

The GARCH (1,1) model is designed to capture the volatility clustering that occurs

within the daily time series whereby relatively higher volatility occurs on Mondays

and Fridays rather than the rest of the week. A series of robustness tests are

undertaken. For instance, we apply the Threshold-ARCH (TARCH) to control for

another characteristic of financial markets where higher volatility is observed during

downturns than equivalent upturns. An Exponential-GARCH (EGARCH) model is

used to test for news in the form of leverage effects (assuming that negative returns

tend to be on average larger in absolute value than positive returns). A Power-ARCH


81

(PARCH) model that generalises the transformation of the error term in the models is

also used.


82


83

Chapter 4

Data and Empirical Results


84


85

4 Data and Empirical Findings

During the research work underlying this thesis we apply the models, whose all

aspects are described in Chapter 3, to a dataset that concerns Chinese stock prices.

This chapter can be summarised as follows. In Section 4.1, we provide a brief

description of the events studied in this thesis and we categorise them into chemical

disasters, oil spills and pollution alerts. In Section 4.2, we estimate the abnormal

returns using event study methodology and we show the results following the

different categories of events. Section 4.3 shows the results of the various robustness

tests that we used to verify our findings. Finally, in section 4.4 we conduct a risk

analysis in order to evaluate the changes in short-term and long-term systematic risk

following the analysed events.

4.1 Data and Background

After two months of air pollution inspections across 28 cities in the Beijing-Tianjin-

Hebei region and other nearby areas, the Ministry of Environmental Protection said

that a total of 13,785 companies, or 70.6% of those examined, violated

environmental standards. Moreover the report, which was published on June 11th

2017, state that more than 4,700 companies were in unauthorized locations, lacked

the proper certificates and failed to meet emissions standards9.

In 2015, at the end of China’s last Five-Year Plan Period, more than 85% of the

surface water in Shanghai was deemed unsafe to drink, while in Tianjin – a port city

9http://news.xinhuanet.com/english/2017-06/11/c_136356860.htm


86

home to 15 million people – that figure reached 95%.Over that period of time nearly

half of China’s mainland provinces – 14 of 31 – failed to meet their water quality

targets. In its new water quality report, Greenpeace East Asia details the state of

China’s urban water crisis — where the combination of lacking wastewater treatment

and low sewage discharge standard have made the water largely undrinkable. It

comes as provinces have been tasked with meeting fresh surface water quality targets

by the end of the decade10.

Figure 6: China’s map where the provinces that failed in achieving the water quality target are marked in red and the provinces that achieved the water quality target are marked in blue

As we already mentioned, on August 12th2015, a series of explosions at a container

storage station at the Port of Tianjin involved the detonation of about 800 tons of

ammonium nitrate and 500 tons of potassium nitrate, as well as other 40 kinds of

hazardous and highly toxic chemical. Fires induced eight additional explosions on

August 15thand caused 173 deaths, 8 missing, and 797 non-fatal injuries. Thousands

10https://unearthed.greenpeace.org/2017/06/01/china-water-quality-data-shanghai-beijing/


87

of people were evacuated from the area with water, soil and air having been heavily

contaminated. From this starting point, we analysed public news headlines and we

collected information about 18 major environmental disasters and pollution alerts

registered in China over the time period from 2003 to 2015. We classified the

analysed events into: chemical disasters (10 events), oil spills (4 events) and

pollution alerts (4 events).

Table 1 summarizes chemical disasters. Beyond Tianjin chemical disaster, the other

analysed events are the Chongqing gas disaster in 2003, the Sichuan ammonia and

nitrogen leak in 2004, the Jilin chemical plant explosions in 2005, the Xinjiang

explosions in 2006, the Guangxi chemical plant explosions in 2008, Shaanxi lead

poisoning scandal in 2009, Guangxi cadmium spill in 2012, Lanzhou benzene leak in

2014 and the Fujian chemical plant explosion in 2015.

Analysing the effects of environmental disasters in a country where water pollution

is one of the most critical issues, we wanted to isolate the effects of oil spills from

the other chemical disasters. We separately collected four of the most important oil

spills that occurred in China over analysed time window. The oil spills we studied

occurred in the Yellow River in 2009, in Xingang Port in 2009, in the Bohai Bay in

2011 and in the Chinese city of Qingdao in 2013. Oil spills are described in Table 2.

Pollution alerts are then summarised in Table 3. As we can see, they occurred only

starting from 2013. In fact, on October 22nd 2013 the city of Beijing released an

Heavy Air Pollution Contingency Plan which set out four alert levels: blue, yellow,

orange and red. The new plan requires mandatory actions to reduce the level of


88

pollution. For instance, when an orange alert is issued, companies will be forced to

halt or limit the production to 30 percents of the emissions, and fireworks,

firecrackers and open-air barbecue will be prohibited in the municipality under the

original provisions. When a red alert is issued, the cars running on streets will be

restricted by the odd-and-even license plate rule, and 30 percents of government-

owned cars must be taken off the roads.

We apply the event study methodology to assess the effect of the 18 events on the

performance of 106 industrial indexes of the Shanghai and Shenzen stock exchanges.

Daily stock price indices, risk-free rate, market return were downloaded from

Datastream. The event date (time 0) is defined as the date in which the fact occurs. In

four cases, as specified in Tables 1 and 2, the information is released later to the

public (or the disaster had further developments), therefore we also run alternative

analyses considering also the day on which the disaster is effectively known to the

mass audience.

We used China A index (Code: TOTMKCA) as a proxy for the market, which

includes class A shares of mainland Chinese companies traded on the Shanghai and

Shenzen exchanges and which are investable only by Chinese nationals daily data.

The interbank 3-month is used as a proxy for the risk-free rate. The size factor

(SMB), the book-to-market factor (HML) and the momentum factor (MOM) are not

readily available for China, hence we have to construct them following instructions

available on Kenneth French’s official website11.

11http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/six_portfolios.html

http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/det_mom_factor.html


89

Table 1: Chemical Disasters

No. Event Date Description

1 Chongqing gas disaster

23/12/2003 A gas well burst and released highly toxic hydrogen sulfide, causing 233 deaths and at least 9,000 injuries.

2 Sichuan ammonia and nitrogen leak

29/02/2004 Ammonia and nitrogen from a urea facility leaked in the Tuo River, depriving about 1m residents of drinking and bathing water for about three days.

3 Jilin chemical plant explosions

13/11/2005 A series of explosions occurred in a petrochemical plant in Jilin city, creating an 80 km long toxic slick in the Songhua River, and then in the Amur River. The blasts caused 70 injuries and 6 deaths.

4 Xinjiang explosions

29/10/2006 A coal mine explosion in Dianchanggou Town killed 14 miners. The evening after the colliery disaster 12 more people were killed when an oil tank blew up in Kramay Town. The tank, which was still under construction, was a key part of the energy co-operation project between China and Kazakhstan.

5 Guangxi chemical plant explosions

26/08/2008 A series of explosions caused by an industrial accident occurred in a plant which mainly produces polyvinyl acetate (PVA). The leak of toxic substances caused at least 20 deaths, 60 injuries and 6 missing.

6 a-b Shaanxi lead poisoning scandal

17/08/2009 The lead plant poisoned more than 850 children in the surrounding area. Local villagers attacked the managers causing the closure of the plant. The disclosure of the scandal was on August 20, 2009 (alternative event date considered).

7 Guangxi cadmium spill

15/01/2012 A toxic cadmium spill, 80 times higher than the official limit, contaminated the Guangxi Longjiang river and water supply.

8 Lanzhou benzene leak

12/04/2014 A benzene leak into the Lanzhou section of the Yellow River left residents without running water for two days due to high level of water pollution.

9 Fujian chemical plant explosion

04/04/2015 An explosion has ripped through a chemical plant in Fujian province, leaving 15 injured, almost two years after a similar accident at the same plant.


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10

a-b-c-d

Tianjin explosions

12/08/2015 A series of explosions at a container storage station at the Port of Tianjin that involved the detonation of about 800 tons of ammonium nitrate. Fires induced eight additional explosions on 15 August and caused 173 deaths, 8 missing, and 797 non-fatal injuries. (We analyse also the following three days)

Table 2: Oil Spills


11 a-b Yellow River oil spill

30/12/2009 An oil spill in the Yellow River in Shaanxi, due to the rupturing of a segment of Lanzhou-Zhengzhou oil pipeline. The incident was not publicized until January 3, 2010 (alternative event day considered).

12 Xingang Port oil spill

16/07/2010 A rupture and subsequent explosion of two crude oil pipelines that run to an oil storage depot.

13 a-b Bohai Bay oil spill

04/06/2011 A series of oil spills that began on June 4, 2011 polluted 5,500 square kilometers of northeastern China’s Bohai Bay. The disaster was not publicly disclosed until a July 5, 2011 (alternative event day considered).

14 Qingdao oil pipeline explosion

22/11/2013 An oil pipeline in Chinese city of Qingdao leaked and caught fire and exploded. The blast killed at least 62 people.

Table 3: Pollution Alerts


15 Beijing pollution at hazard level

12/01/2013 The concentration of hazardous particles was forty times the level deemed safe by the World Health Organization (WHO). Beijing ordered government vehicles off the roads as part of an emergency response to ease air pollution.

16 Beijing orange pollution alert

29/11/2015 Beijing’s municipal government lifted the air-pollution alert to orange, according to the Beijing Municipal Environmental Monitoring Center.

17 Beijing first red pollution alert

07/12/2015 Beijing authorities upgraded the air pollution alert to red from orange for the first time since the emergency alert system was established.

18 Beijing second red pollution alert

18/12/2015 Beijing officials issued a second red alert for the city, the highest on a four-tier warning scale.


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4.2 Empirical Results of Event Study Analysis

Table 4 reports the aggregate statistics for the average abnormal returns on the event

date (AR) and average cumulated abnormal returns from the day of the event to 5

and 10 days after the event (CAR5 and CAR10, respectively) for the 106 selected

stock indexes. The CAR approach enables us to find out whether or not the market

reverts back to its mean process or continues to deviate from the mean price. Similar

to the AR approach, the t test is used to determine the statistical significance of

cumulative abnormal returns.

Analysing the average abnormal returns, computed through the three models

described in the previous chapter, we notice mixed results but the magnitude of the

reaction is on average significant in many cases. The only event that is not subject to

any relevant effect is event 3 (Jilin chemical plant explosions) and this confirmed

both by the AR (computed through the three different models) and by CAR5 and

CAR10 results. We encounter many cases where the general market reaction, on

average, is negative, but we find also disasters in which the reaction is positive

(events 11b, 12, 13b and 6a limited to the cumulated abnormal returns after 10 days).

Sometimes significantly positive returns are detected on the announcement day,

while they turn to be significantly negative in the next days (and the opposite, as

well). On average, the CAR10 values give us a more clear view of the market

reactions to the events. For instance, the Tianjin explosions, analysed in four days

(events 10a, 10b, 10c and 10d), shows an average CAR10 of roughly -3%,

considering the three models.


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With the aim to disentangle the effects on the different indexes, Figures 6 and 7 show

the number of industries, out of the 106 sample indexes, with a statistically

significant positive (or negative, respectively) reactions to the environmental

disasters and pollution alerts (the 95% significance level is adopted as a threshold).

Interestingly, events 1, 4, 10a and 10d are associated to significant positive reactions

in a number of stock indexes, while events 5, 7, 11a and 15 are associated to

significantly negative reactions of some industrial indexes. Another important thing

that we can notice, by analyzing figures 6 and 7, is that our results are quite coherent

using the three asset pricing models, they move on the same trend.

In order to analyse more deeply the results, we listed in Table 5 the industries

associated to the statistically significant reactions for every event. The abnormal

returns are detected by using the three different asset pricing models and the results

are not sensibly different across the benchmark models. In general terms, we can see

how the results are heterogeneous both in terms of impacted sectors and in terms of

magnitude of the abnormal return. We do not find a trend in the market reaction and

we observe that, depending on the event analysed, the indexes most impacted by the

disasters change. For instance, General mining industry shows a statistically

significant negative reaction to event 5 (-5.02% using CAPM) and to event 7 (-6.11%

using CAPM) whereas it has a statistically significant positive reaction to event 10b

(+8.03% using CAPM). A similar positive reaction to event 10a is observed for

Mining and Gold Mining (+4.44% and +5.56% using CAPM, respectively). Events 1

to 4, 8, 10b to 10d generate only significantly positive returns; the opposite happens

to events 5 and 7.


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The unexpected heterogeneity of the results may be explained by: (i) the fact that the

events are circumscribed to a specific geographical area, and (ii)the peculiarity of the

chemical components involved in each disaster. The latter factor may lead to a

‘balancing effect’ among industries, with companies producing substitutive materials

that can benefit from the event; the first factor may favor companies within the same

industry but located in other geographical areas.

In Table 5 we also listed statistically significant reactions of the sample indexes to oil

spills. Also in this case, our findings show mixed results. Events 11a and 14 show

only statistically significant negative abnormal returns, while events 11b, 12 and 13b

lead to positive significant abnormal returns. If we look more deeply into the

industries which had a statistically significant reaction, we can see surprising

behavior. For instance, industries like Consumer goods, Food and beverages,

Personal goods reacted negatively to event 11a (reaction lower than -2%) and

industries like Food producers, Personal goods had a positive reaction to event 11b

(reaction higher than +2%). We suppose that these curious results, showing quite

similar industries reacting in totally different way to the same event can be explained

by the capability of the initial censorship to “distort” the investors’ behavior.

Furthermore, always referring to Table 5, we observe no statistically significant

negative reactions from the most polluting sectors to pollution alerts after October

2013.The only event which leads negative reaction, even if with quite low

magnitude, is event 15. For instance, we observe around -1.5%, -1.8% and -2.2% on

Basic Materials, Mining and Building Materials and Fixtures, respectively. The most

unexpected result in this case is that we don’t observe statistically significant


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negative reactions following the events 16, 17 and 18. This finding leads us to

suppose that the Heavy Air Pollution Contingency Plan introduced by the

Government failed to change investors’ expectations about the effects that a pollution

alert can have on polluting industries.

With the aim to capture delayed reactions, we report in Table 6 the cumulated

abnormal returns (CAR) in two different time frames: 5 days and 10 days after the

event that are found to be significantly different from zero. If we discard the EMH

and assume that market participants tend either to over-react or under-react, the CAR

analysis allows us to find out whether in the long run the market reverts back to its

mean price or continues to deviate from the mean process.

Our results show that the percentage of industries that had a statistically significant

negative delayed reaction to chemical disasters is higher than the positive ones (57%

vs. 43%), with a maximum loss of 19.50% and 30.24% for the 5-day and 10-

daycumulated return respectively, for the Semiconductor sector, following event 10d.

Surprisingly, all the industries that exhibit a positive AR following events 1, 2, 3, 4

reverted back to their mean process and they don’t experience any further reaction

either 5 days or 10 days after the event date. On the other hand, many industries

(such as Basic resources, Industrial metals & mines, electronic/electrical equipment,

Industrial machinery and Health care) experienced statistically significant negative

CAR5 and CAR10, following the first four chemical disasters. Similar behavior has

been found following the other chemical disasters. For instance, the instantaneous

reaction of Mining, Gold mining and General mining to the Tianjin chemical disaster

was positive, but this effect was not confirmed by CAR5 and CAR 10 results.


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Focusing on the oil spills, we notice that only one industry (Household goods, home

consumer) exhibits a statistically significant negative CAR5 and CAR10 following

event 11a, with -8.61% and -10.26% respectively. One of the most interesting results

is the positive CAR of Oil & equivalents service/distribution industry to the Xingang

Port oil spill (event 12). This result seems to validate our hypothesis of a ‘balancing

effect’ that in this case leads the competitors within the same industry to potentially

enjoy a benefit12.

If we look at pollution alerts, the results show that Alternative energy and Beverages

experienced statistically significant CAR5 and CAR 10 following event 15. The

former with a magnitude of -7.35% and -8.92% respectively after 5 and 10 days, the

latter with a magnitude of -8.84% after 5 days and -19.73% after 10 days. The result

we want to underline is that no statistically significant delayed reaction, either

positive or negative, are following the events 16, 17 and 18. This finding seems to

confirm our hypothesis that the Heavy Air Pollution Contingency Plan introduced by

the Government failed to change investors’ expectations about the effects that a

pollution alert can have on polluting industries.

12In unreported results, we document that the results do not significantly change if the index returns

are weighted by the market capitalization of the single companies.


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Table4: Reaction of the stock market to environmental disasters and pollution alerts in China: statistics about the mean abnormal returns (AR) and mean cumulated abnormal returns in five days (CAR5) and ten days (CAR10) around the event dates.


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*,**,*** = statistically significant at the 90%, 95%, 99% level respectively

Figure 7: Number of statistically significant (95% level) positive reactions of the

106 stock indexes to environmental disasters and pollution alerts.

Figure 8:Number of statistically significant (95% level) negative reactions of the

106 stock indexes to environmental disasters and pollution alerts


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Table 5: Reaction of the stock market to environmental disasters and pollution alerts in China: statistically significant abnormal returns(AR) under three benchmark models. T-statistics in parentheses

Event Industry index CAPM Fama& French Carhart

Chemical disasters

1 Speciality finance +4.96% (3.47) *** +4.81% (3.42) *** +4.89% (3.47) ***

2 Media agencies +5.52% (2.89) *** +5.39% (2.84) *** +5.50% (2.91) ***

3 Apparel retail +5.68% (4.43) *** +5.57% (4.34) *** +5.58% (4.32) ***

4 Iron & steel +4.38% (4.00) *** +4.38% (3.98) *** +4.36% (3.96) ***

Alternative electricity

+4.28% (2.15) ** +4.26% (2.14) ** +4.26% (2.12) **

5 General mining -5.02% (-2.07) ** -4.58% (-1.91) * -4.58% (-1.90) *

Commercial vehicles/trucks

-4.13% (-2.83) *** -4.02% (-2.78) *** -4.01% (-2.77) ***

Farming & fishing plant.

-5.59% (-2.13) ** -4.85% (-1.90) * -4.78% (-1.88) *

Apparel retail -8.96% (-2.96) *** -8.80% (-2.88) *** -8.91% (-2.91) ***

Publishing -5.78% (-2.11) ** -5.32% (-1.98) ** -5.25% (-1.96) **

Gs/Wt/ Multiutilities

-4.87% (-2.21) ** -4.53% (-2.00) ** -4.36% (-2.00) **

6a Personal products +8.51% (3.52) *** +8.68% (3.53) *** +8.71% (3.55) ***

Hotels +5.56% (2.06) ** +5.47% (2.03) ** +5.52% (2.03) **

Financial services +4.78% (2.03) ** +4.91% (2.07) ** +4.92% (2.07) **

Biotechnology -4.11% (-2.08) ** -3.92% (-1.98) ** -3.92% (-1.98) **

7 General mining -6.11% (-3.09) *** -6.12% (-3.01) *** -6.14% (-3.01) ***

Electronic equipment

-4.31% (-3.44) *** -4.30% (-3.39) *** -4.30% (-3.37) ***

Support services -5.19% (-3.96) *** -5.15% (-3.83) *** -5.14% (-3.81) ***

Beverages -4.25% (-3.80) *** -4.21% (-3.78) *** -4.19% (-3.77) ***

Footwear -8.26% (-3.22) *** -8.34% (-2.62) *** -8.34% (-2.62) ***

8 Furnishings +6.22% (2.49) ** +6.192% (2.46) ** +6.19% (2.45) **

10a Mining +4.44% (3.04) *** +4.45% (3.04) *** +3.30% (2.26) **

Gold mining +5.56% (2.33) ** +5.58% (2.39) ** +6.04% (2.58) ***

Speciality retail +6.48% (2.56) ** +6.69% (2.67) *** +9.96% (3.94) ***

Travel & tourism -4.24% (-2.20) ** -4.16% (-2.34) ** -4.03% (-2.26) **

10b General mining +8.03% (3.51) *** +7.77% (3.46) *** +7.76% (3.45) ***

Speciality retail +5.59% (2.21) ** +5.23% (2.09) ** +5.25% (2.08) **

10c Commodity chemicals

+6.12% (2.53) ** +5.83% (2.45) ** +5.81% (2.45) **

Speciality retail +6.47% (2.56) ** +6.38% (2.55) ** +6.38% (2.53) **

10d Support services +4.02% (2.20) ** +4.01% (2.19) ** +4.05% (2.21) **

Tires +9.50% (2.38) ** +9.61% (2.58) *** +9.55% (2.56) **

Airlines +6.48% (2.31) ** +6.66% (2.37) ** +6.71% (2.38) **


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Event Industry index CAPM Fama& French Carhart

Oil spills

11a Consumer goods -2.30% (-2.12) ** -2.28% (-2.11) ** -2.23% (-2.07) **

Food & beverages -2.52% (-2.09) ** -2.48% (-2.05) ** -2.43% (-2.01) **

Personal and H/H goods

-2.37% (-2.04) ** -2.33% (-2.02) ** -2.24 (-1.95) *

Personal goods -2.66% (-2.57) ** -2.63% (-2.53) ** -2.56% (-2.46) **

Consumer services -1.94% (-2.17) ** -1.89% (-2.10) ** -1.85% (-2.06) **

Technology -2.69% (-2.00) ** -2.64% (-1.95) * -2.63% (-1.94) *

Semiconductors -5.42% (-2.23) ** -5.23% (-2.16) ** -5.10% (-2.10) **

11b Food producers +2.54% (2.02) ** +2.43% (1.92) * +2.38% (1.88) *

Personal goods +2.33% (2.24) ** +2.19% (2.10) ** +2.12% (2.03) **

Specialty chemicals

+2.62% (2.02) ** +2.40% (1.86) * +2.33% (1.81) *

Publishing +3.57% (2.02) ** +3.00% (1.73) * +2.96% (1.70) *

12 Basic materials +1.33% (1.98) ** +1.34% (1.99) **

+1.36% (2.02) **

Specialty chemicals

+3.76% (3.16) *** +3.75% (3.18) ***

+3.79% (3.22) ***

13b General industrials +2.69% (1.98) ** +2.70% (2.00) ** +2.74% (2.01) **

14 Semiconductors -5.69% (-2.00) ** -5.55% (-1.97) ** -5.59% (-1.98) *

Pollution alerts

15 Basic materials -1.47% (-2.58) *** -1.46% (-2.56) ** -1.46% (-2.56) **

Mining -1.81% (-2.63) *** -1.79% (-2.58) *** -1.79% (-2.58) ***

Building materials / fixt.

-2.12% (-2.04) ** -2.18% (-2.09) ** -2.18% (-2.06) **

Aerospace & defence

+5.31% (3.19) *** +5.09% (3.14) *** +5.09% (3.13) ***

Banks +1.20% (2.04) ** +1.26% (2.16) ** +1.26% (2.16) **

Tch Hardware & equip.

+2.14% (2.29) ** +2.07% (2.21) ** +2.07% (2.20) **

Telecom equipment

+2.51% (2.29) ** +2.42% (2.21) ** +2.42% (2.22) **

17 Recreational services

+5.39% (1.99) ** +5.11% (1.88) * +5.12% (1.88) *

18 Travel & leisure +3.96% (2.19) ** +3.90% (2.17) ** +3.85% (2.14) **

Recreational services

+5.81% (2.14) ** +5.72% (2.09) ** +5.67% (2.07) **



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Table 6:Reaction of the stock market to environmental disasters and pollution alerts in China: statistically significant cumulated abnormal returns (CAR) in five and ten days after the event. T-statistics in parentheses

Event Industryindex CAR 5 days CAR 10 days

Chemicaldisasters

1 Basic resources -3.27% (-2.34) ** -3.98% (-2.15) **

Industrial metal & mines -4.32% (-2.46) ** -5.34% (-2.22) **

Cont. & packaging +7.21% (2.72) *** +9.28% (2.32) **

3 Aluminium +7.34% (2.19) ** +8.67% (2.13) **

4 Electr. / electricalequipment -7.37% (-2.47) ** -15.21% (-3.35) ***

Industrial machinery -7.83% (-2.60) *** -10.91% (-2.55) **

Health care -5.72% (-2.46) ** -10.77% (-3.27) ***

Retail -7.79% (-2.51) ** -9.22% (-2.21) **

Speciality finance -13.47% (-2.33) ** -15.85% (-1.98) **

Software & computer services -10.01% (-3.06) *** -12.28% (-2,81) ***

5 Specialityretail -14.02% (-2.93) *** -13.27% (-2.36) **

6a Personal products +17.70% (3.37) *** +16.73% (2.54) **

Pharmaceuticals +6.36% (1.98) ** +7.86% (2.03) **

Recreational services -9.91% (-2.07) ** -12.05% (-2.07) **

Real estate -10.24% (-2.45) ** -13.89% (-2.45) **

6b Biotechnology +16.72% (3.29) *** +18.00% (2.65) ***

Computer services +11.12% (2.24) ** +13.20% (2.20) **

7 Marine transport +5.43% (2.57) ** +6.96% (2.23) **

Footwear -21.91% (-3.08) *** -19.12% (-2.00)**

9 Cont. & packaging +9.18% (2.54) ** +10.48% (2.13) **

Commercial vehicles/trucks +12.22% (2.41) ** +37.77% (5.21) ***

Brewers +6.47% (2.79) *** +6.12% (2.06) **

Personal products +20.04% (2.59) *** +30.52% (2.69) ***

Medical equipment +24.75% (4.32) *** +20.35% (2.38) **

10a Travel & tourism -10.41% (-2.15) ** -20.64% (-3.26) ***

10d Personal goods -12.37% (-2.51) ** -15.85% (-2.32) **

Internet -16.59% (-2.09) ** -22.87% (-1.98) **

Semiconductors -19.50% (-2.11) ** -30.24% (-2.22) **


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Event Industryindex CAR 5 days CAR 10 days

Oilspills

11a Electronic equipment +8.14% (2.16) ** +13.77% (2.67) ***

H/H goods, home consumer -8.61% (-2.14) ** -10.26% (-2.19) **

11b Electronic equipment +12.78% (3.39) *** +17.23% (3.35) ***

Media +11.60% (2.97) *** +19.24% (3.81) ***

Software & computer services +9.02% (2.43) ** +18.20% (3.99) ***

Computer hardware +9.92% (2.20) ** +13.39% (2.58) ***

Semiconductors +12.19% (2.04) ** +21.24% (2.63) ***

12 Oil&equivalents service/distr. +7.55% (3.25) *** +11.73% (3.89) ***

Chemicals +5.04% (2.04) ** +8.90% (2.14) **

13b Media agencies +16.35% (3.14) *** +21.41% (3.40) ***

Pollutionalerts

15 Alternative energy -7.35% (-2.05) ** -8.92% (-2.00) **

Beverages -8.84% (-2.35) ** -19.73% (-3.90) ***



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4.3 Empirical Results of Robustness Tests

One of the problems with event study methodology is asset pricing model selection.

Therefore, as showed in the previous section, we calculate abnormal return using

three asset pricing models and these abnormal returns are used as robustness checks.

As discussed in Chapter 3, we employ other robustness tests to address several issues

related to event study methodology. For instance we introduce the Non Parametric

Ranking Test (NPRT) and the Non Parametric Conditional Distribution (NPCD)to

address the non-normality issue. As deeply described in Chapter 3, we also adjust

returns controlling for market integration and we exclude firm-specific information.

We observe that the results we obtained are confirmed in very few cases by either the

NPRT or the NPCD test. The control for market integration does not significantly

impact on the robustness of the findings. The removal of companies with firm-

specific information, in a time window of 30 days around the event day, in some

cases leaves a small number of firms in each portfolio and the test parameters cannot

be computed. In 60% of the cases where the test was carried out, the results are

robust.


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in China: robustness tests on abnormal returns (AR). T-statistics in parentheses


104


105

**,*** = statistically significant at the 95%, 99% level respectively


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4.4 Risk Analysis: short- term and long-term change in risk

Finally we perform the risk analysis, which provides empirical evidence of changes

in long-term and short-term systematic risk. Table 8 shows the sectors that

experienced a statistically significant change in the overall long term risk, either

positive or negative, at least at the 95% level. In general terms, our results show that

the magnitude of the change in the overall long term risk is very low, both in positive

terms and in negative terms.

In Table 8 we show the results after applying Equation (38), deeply described in

Section 3.3. We notice that 6 sectors(Aerospace, Biotechnology, Food & Beverage,

General Mining, Industrial Suppliers, Support Services) experienced an increase in

the overall risk, following chemical disasters. On the contrary, Brewers, Specialised

Chemicals and Speciality Finance sectors experience a decrease in the overall risk

following oil spills. Pollution alerts, finally generate an increase in the overall

systematic risk for Aeronautical/ Defence sector.

In Table 9 we analyse the change in the overall short term systematic risk,

introducing the robustness tests described in the previous section. Applying GARCH

(1,1),Exponential-GARCH, Threshold-ARCH, and Power-ARCH models, we

observe that all our findings from the OLS are always supported.

To identify the effects of each single event on the short-term systematic risk, we

estimate the disaggregate model introduced in Equation (39), and the results for all

industries are graphically represented in Figures 9, 10 and 11. Changes in the short-


107

term systematic risk following chemical disasters, oil spills and pollution alerts are

common. As we can see from Figure 9, there is a change in the risk pattern after

chemical disasters 4, 8, 9, 10c and 10d. In particular, we notice that roughly half of

sectors experience an increase of the short term systematic risk after events 4, 8, 9

and 10c, whereas more than 60% of sectors experience an increase following event

10d.

Going more in deep, we see that among the industries that experienced an increase in

the short term systematic risk following the event 4 (Xinjiang coal mine explosions)

there are Coal, Oil & Gas products, Utilities, Gold Mining, Electricity and Basic

Materials. Similarly, Biotechnology, Coal, Electricity, Mining and Utilities are some

of the industries which had an increase in short term systematic risk following event

8 (Lanzhou benzene leak). Event 9 (Fujian chemical plant explosion) lead an

increase in the short term systematic risk to industries like Coal, Basic Materials,

Commodity Chemicals, Oil & Gas and Utilities. Finally, Tianjin chemical disaster, in

both events 10c and 10d, caused uncertainty in the economy and in particular

Chemicals, Commodity Chemicals, Electricity, Food Products and Utilities

experienced a raise in the short term systematic risk. We can conclude that chemical

disasters had a huge impact on the short-term systematic risk of highly polluting

industries.

Analysing oil spills, Figure 10 shows that events 12 and 13b caused a structural

change in the risk pattern, where roughly 60% of industries experience an increase in

short-term systematic risk. Also in this case if we look at the industries involved we

can see that many polluting industries are impacted. Both the oil spills caused an


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increase in short term systematic risk to Basic Materials, Chemicals, Coal, Mining

and Utilities. A very unexpected result is that a decrease in short-term systematic risk

is spotted for the Oil & Gas industry following both event 12 and 13b. This result

confirms our hypothesis of a ‘balancing effect’ that may favor companies within the

same industry but located in different geographical areas.

Finally, we analyse the shape of short term change in systematic risk following

pollution alerts represented in Figure 11. We notice a structural change in the risk

pattern following event 18. The last pollution alert that we studied occurred in

December 2015, when Beijing officials issued a second red alert for the city, the

highest on a four-tier warning scale. A very surprising result is that this

announcement led to an increase in the short term systematic risk only to the 30% of

the analysed industries. Among them, we underline Food Products, Consumer

Goods, Health Care, Pharmaceuticals, Airlines and Travel and Tourism. On the other

hands, the most polluting industries (such as Chemicals, Coal, Oil & Gas,

Chemicals) experienced a decrease in the short-term systematic risk. This findings is

consistent with our hypothesis that the Heavy Air Pollution Contingency Plan

introduced by the Government did not have effects on the most polluting industries.

Dealing with the long-term systematic risk, we estimate the disaggregate model

described by Equation (39). Figures12, 13 and 14 graphically represent the change in

risk in the long run following chemical disasters, oil spills and pollution alerts

respectively. Interestingly, from the analysis of Figure 12, we find that long-term risk

experiences a drastic change after the last two days of Tianjin explosions (events 10c

and 10d), and a ‘diamond’ shape is observed, with a decrease in the risk of some


109

industries. The latter effect can be explained by investors ‘rebalancing’ their

portfolio according to risk expectations. Regarding oil spills, Figure 13 shows as

Yellow River oil spill (in both the two analysed days 11a and 11b) had a more

significant impact on the long term systematic risk, with respect to the other three

events. From Figure 14, we can see that pollution alerts created uncertainty with a

consequent change in the long term systematic risk, following all the events, that is

consistently lower compared to chemical disasters.

Table8: Risk Analysis. Aggregate change in systematic risk

Industry Beta Overall change in

systematic risk

t-stat

Chemical Disasters

Aerospace 1.00 0.58 5.03 ***

Biotechnology 0.71 0.6 2.39 ***

Financial services 1.29 -0.47 -4.19 ***

Food&Beverage 0.82 0.27 2.04 ***

General mining 1.20 0.71 3.01 ***

Industrialsuppliers 1.15 0.41 4.09 ***

Investmentservices 1.43 -0.73 -2.59 ***

Support services 1.15 0.46 5.00 ***

Transportservices 0.85 -0.29 -3.57 ***

Oil Spills ¤

Brewers 0.80 -1.06 -3.59 ***

Spec.Chemicals 0.98 -1.48 -2.27 **

Specialityfinance 0.01 -0.32 -11.58 ***

Toys 0.84 0.59 2.13 **

Pollution Alerts

Aero/Defence 0.01 0.01 8.06 ***

**,*** = statistically significant at the 95%, 99% level respectively


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Table9: Risk Analysis. Robustness Tests on Aggregate Risk Model

Industry OLS GARCH(1,1) TARCH EGARCH PARCH

Change z-stat Change z-stat Change z-stat Change z-stat Change z-stat

Chemical Disasters

Aerospace 0,14 0,53 0,58 4,97 0,59 5,07 0,57 3,88 0,58 5,24

Biotechnology 0,31 1,46 0,48 1,85 0,45 1,71 0,53 2,36 0,43 1,57

Financial Svs -0,51 -3,06 -0,47 -4,19 -0,48 -3,83 -0,48 -3,05 -0,48 -3,52

General Min. 0,69 3,04 0,70 2,93 0,71 2,97 0,69 2,63 0,71 2,94

Ind. Suppliers 0,39 2,01 0,42 4,14 0,41 4,04 0,40 3,51 0,40 3,61

Investment Svs

-0,58 -2,72 -0,75 -2,37 -0,74 -2,36 -0,68 -2,56 -0,70 -2,61

Support Svs 0,38 2,18 0,47 5,07 0,47 5,15 0,43 4,65 0,44 4,89

Transport Svs -0,05 -0,45 -0,30 -3,55 -0,30 -3,59 -0,23 -2,57 -0,24 -2,82

OilSpills

Brewers -0,29 -0,43 -0,90 -2,52 -0,98 -2,95 -0,94 -2,45 -0,16 -0,23

SpecChem -1,50 -2,89 -1,48 -2,13 -1,49 -2,11 -1,12 -2,79 -1,50 -2,26

Speciality Fin -0,45 -0,40 0,41 15,57 -0,17 -6,21 -0,42 -0,10 -0,35 -11,89

Toys -2,45 -2,02 0,58 2,02 0,07 0,21 -0,44 -1,15 -0,71 -1,89

Pollution Alerts

Aero/ Defence

0,01 0,01 0,00 7,63 0,00 8,27 0,00 -18,51 0,00 8,87


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Figure 9: Risk analysis. Short term change in systematic risk following chemical disasters.


112

Figure 10:Risk analysis. Short term change in systematic risk following oil spills.

Figure 11:Risk analysis. Short term change in systematic risk following pollution alerts.


113

Figure 12: Risk analysis. Long term change in systematic risk following pollution chemical disasters.

Figure13: Risk analysis. Long term change in systematic risk following oil spills.


114

Figure 14: Risk analysis. Long term change in systematic risk following pollution alerts.


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116

Chapter 5

Conclusions


117


118

5 Conclusions

One of the major consequences of China’s rapid growth has been severe air, water

and air pollution with huge effects on health. The evidence shows that pollution

causes premature death in the population. The “grow now, clean up later” approach,

which has been adopted in the early stages in China, encouraged rapid growth while

overlooking the environmental consequences. Although in recent years the approach

adopted by the policy makers seems to be changed, pollution remains one of the

biggest issues in China and environmental disasters continue to hit this country.

This thesis presents an empirical study aiming at exploring the effects of

environmental disasters and pollution alerts on corporate performance in China when

performance is represented by stock returns, by using event study methodology. Our

analysis shows that environmental disasters and pollution alerts that occurred in

China from 2003 to 2015 had significant effects on the return of equity stock on

domestic stock exchanges. Such effects widely characterize several industries, and

may be either positive or negative. In general terms we talk about “mixed effects”.

Moreover, we perform the risk analysis, which provides empirical evidence that

these events affect both the long-term and short-term systematic risk.

Although it is not easy to find a general pattern in the effects of the analysed events

on Chinese stock markets, we highlight three important issues. First, it is not

necessarily the case that environmental disasters and pollution alerts negatively

affect the most polluting industries(e.g. mining, oil & gas, chemicals). In fact, we see

that the reaction of the most polluting industries are rarely negative. This finding


119

may signal that investors are sceptical about the capability (or willingness) of the

regulators to leverage on the disasters’ effects to introduce more tight requirements

to polluting industries. Likely, some manufacturing industries are believed to be

‘strategic’ for the domestic economy and vital for the production of energy; the

probability of any significant limitation to the environmental risk in this fields is

estimated to be low.

Second, we hypothesise that balancing effects are at work; given the geographical

concentration of the effects of chemical disasters, oil spills and air pollution, local

companies may be hit by the consequences, but competitors in other regions may

indeed benefit. On the other hand, a similar balancing effect can be encountered by

industries that work on substitutive products, therefore disasters caused by a specific

product can push customers to rely on alternative materials.

Third, the sample events created uncertainty on the market and led to a significant

change in idiosyncratic risk in a number of industries. As we deeply described in

Chapter 4, after chemical disasters especially after the Tianjin explosion in 2015 we

notice that both long-term and short-term risk of the most polluting industries have

been affected. This finding raises concerns about the capability of investors to

estimate correctly the environmental risk of polluting industries in China. Of course,

the uncertainty on the exact time when an environmental disaster will occur leads the

investors to be unprepared about the consequences it may have on risk. Nevertheless,

we believe that the results our study provides a clearer insight on this topic and it can

be interesting for the investors in order to build a portfolio that mitigate their risk.


120

Furthermore, we believe that our work contributes to better understand the

relationship between environmental disasters and financial markets’ expectations,

supporting policymakers to solve the trade-off between sustainability and industrial

growth, maybe managing efficiently the risk of environmental disasters, and

providing investors with insights on the effects of environmental disasters on

financial investments.

Environmental regulation is a set of lows and rules designed to eliminate or reduce

the risk posed by environmental hazards on the individuals and the ecosystem. In the

last years, China demonstrated its willingness to clean the “mess” produced by the

fast growing. Surely this proactive approach brought to many achievements in terms

of improvement of pollution conditions, anyway, environmental disasters and the

health problems associated to pollution continue to be big issues. We believe that

policymakers should invest more in improving their monitoring system. Monitoring

the status of the most polluting industries and taking stringent preventing actions for

those who don’t respect the law can be a great tool to prevent environmental

disasters and to protect the environment.


121


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