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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
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:
837649
Supervisor: Prof. Giancarlo Giudici
Co-supervisors: Prof. Vikash Ramiah,
Academic Year 2016 - 2017
INDUSTRIALE E
Corso di Laurea Magistrale in Management Engineering
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
Supervisor: Prof. Giancarlo Giudici
Ramiah, Dr. Huy Pham
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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Dedicated to my family,
who always believed in me.
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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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.
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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
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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.
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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
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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
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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Figure 14: Risk analysis. Long term change in systematic risk following pollution
alerts. ................................................................................................................ 114
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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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
Table 7:Reaction of the stock market to environmental disasters and pollution alerts
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
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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Chapter 1
Introduction
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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.
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Chapter 2
Literature Review
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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
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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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.
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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
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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
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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
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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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.
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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 N-shaped or more flexible shape.
claims that environmental quality can be a productive input for economic
Regarding the impact of environmental regulation on productivity, the literature give
us evidence of the existence of two main lines of thoughts, one is supporter of the
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 positive impact of
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.
1: Kuznets curve graph
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Zhang (2012) argues that is not cautious to use the environmental
Kuznets curve hypothesis to solve environmental problems in economic growth
shape. Moreover, the
ctive input for economic
Regarding the impact of environmental regulation on productivity, the literature give
one is supporter of the
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
positive impact of
environmental regulation on productivity has been reported. Moreover, productivity
levels always vary across industries because they adopt different technologies in their
production processes, and the productivity level of sectors/firms is also affected
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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
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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.
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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
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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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.
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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
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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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
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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
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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
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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
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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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
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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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
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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
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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
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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
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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
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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.
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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&
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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
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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).
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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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)
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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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.
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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.
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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.
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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/
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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.
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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.
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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
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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)
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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.
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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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.
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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
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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
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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)
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, 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:
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U� − K� = �UV + �U�/ �� − K�1 + ∑ �U,Y� / �� − K�1 ∗ Z�[���[�� + ∑ �U,Y� ∗��[��
Z�[� + XU� (15)
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, 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
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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.
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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
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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).
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Chapter 3
Methodology
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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)
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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
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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
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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)
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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
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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”.
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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)
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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:
ARd^a = Rd^a − βd^aV + βd^a� (Rr^ − Rs^) + βd^a� /Rr^w�d� − Rsw�d�1 + βd^a� /Rr^������ −
Rs^������1 + βd^a� /Rr^�y − Rs�y1 (37)
where ARd^a is the abnormal return of firm i at time t using this model, Rd^a 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, βd^aV is the sum of risk-
free rate and the intercept α and βd^a� , βd^a� , βd^a� , βd^a� 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 ~~ −
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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.
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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]i^is industry I’s return at time t, r]s^is the risk-free rate at time t, r]r^is 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)
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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
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(PARCH) model that generalises the transformation of the error term in the models is
also used.
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Chapter 4
Data and Empirical Results
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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
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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/
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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
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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
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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
No. Event Date Description
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
No. Event Date Description
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) **
*,**,*** = statistically significant at the 90%, 95%, 99% level respectively
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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) ***
*,**,*** = statistically significant at the 90%, 95%, 99% level respectively
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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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Table 7:Reaction of the stock market to environmental disasters and pollution alerts
in China: robustness tests on abnormal returns (AR). T-statistics in parentheses
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The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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**,*** = 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-
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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
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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.
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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.
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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.
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Figure 14: Risk analysis. Long term change in systematic risk following pollution alerts.
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The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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Chapter 5
Conclusions
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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
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
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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.
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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.
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Bibliography
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
123
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
124
Anne, W. and Ramiah, V. (2016). Ecology and Finance: A Quest for Congruency.
Journal of Behavioural and Experimental Finance.
Anne, W., Ramiah, V. and Moosa, I. (2016). Is it possible to be too risk averse?
Considerations for financial management in the public sector. Applied
Economics Letters, Forthcoming.
Ashley, J. W. (1962). Stock Prices and Changes in Earnings and Dividends: Some
Empirical Results. Journal of Political Economy, 70(1), 82– 85.
Ball, R. and Brown, P. (1968). An Empirical Evaluation of Accounting Income
Numbers. Journal of Accounting Research, 6, 159-177.
Barbera, A.J. and McConnell, V.D. (1986). Effects of Pollution Control on Industry
Productivity: A Factor Demand Approach. Journal of Industrial Economics,
35, 161-172.
Barbera, A.J. and McConnell, V.D. (1990). The Impact of Environmental
Regulations on Industry Productivity: Direct and Indirect Effects. Journal of
Environmental Economics and Management, 18, 50-65.
Barker, C. A. (1956). Effective Stock Splits. Harvard Business Review, 34(1), 101–
106.
Barker, C. A. (1957). Stock Splits in a Bull Market. Harvard Business Review, 35(3),
72-79.
Barker, C. A. (1958). Evaluation of Stock Dividends. Harvard Business Review,
36(4), 99-113.
Bartik, T.J. (1988). The Effects of Environmental Regulation on Business Location
in the United States. Growth and Change, 19, 22-44.
Barton D.R.(2005). How Is the Stock Market Affected by Natural
Disasters?.Working paper.
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
125
Beaver, W.H. (1968). The information content of annual earnings announcements.
Empirical research in accounting: Selected studies. Supplement to the Journal
of Accounting Research, 67-92.
Beaver, W.H. (1981). Econometric properties of alternative security return methods.
Journal of Accounting Research, 19, 163-184.
Becker, R. and Henderson, V. (2000). Effects of Air Quality Regulations on
Polluting Industries. Journal of Political Economy, 108, 379-421.
Becker, R. and Henderson, V. (2001). Costs of Air Quality Regulation, in C. Carraro
and G.E. Metcalf (eds) Behavioral and Distributional Effects of
Environmental Policy, Chicago: University of Chicago Press.
Beckerman, W. (1992). Economic Growth and the Environment: Whose Growth?
Whose Environment? World Development, 20, 481-496.
Berman, E. and Bui, L.T.M. (1999). Environmental Regulation and Productivity:
Evidence from Oil Refineries. Working Paper, Boston University, Boston.
Bezdek, R.H., Wendling, R.M. and Di Perna, P. (2008). Environmental Protection,
the Economy, and Jobs: National and Regional Analyses. Journal of
Environmental Management, 86, 63-79.
Binder, J. (1983). Measuring the Effects of Regulation with Stock Price Data: A New
Methodology. Ph. D. dissertation, University of Chicago.
Binder, J. (1985). Measuring the Effects of Regulation with Stock Price Data. RAND
Journal of Economics, 16, 167–183.
Binder, J. (1998). The event study methodology since 1969. Review of Quantitative
Finance and Accounting, 11, 111–137.
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
126
Black, F., Jensen, C.M. and Scholes, M. (1971). The capital asset pricing model:
Some empirical tests, in: M. Jensen. ed. Studies in the theory of capital
markets, Praeger, New York, NY.
Brown, S. and Warner, J. (1980). Measuring security price performance. Journal of
Financial Economics, 8, 205–258.
Brown, S. and Warner, J. (1985). Using daily stock returns: the case of event studies.
Journal of Financial Economics, 14, 3–31.
Cahan, S.F., Chen, C., Chen, L. and Nguyen, N.H. (2015). Corporate social
responsibility and media coverage. Journal of Banking & Finance, 59, 409-
422.
Cam, M.A. and Ramiah, V. (2014). The influence of systematic risk factors and
Econometric adjustments in event studies. Review of Quantitative Finance
and Accounting, 42(2), 171-189.
Carhart, M. (1997).On persistence in mutual fund performance. Journal of Finance,
52, 57–82.
Carhart, M., Krail, R.J., Stevens, R.L. and Welch, K.D. (1996). Testing the
conditional CAPM. Working Paper, Graduate School of Business, University
of Chicago, Chicago, Ill.
Chan, L.K.C., Jegadeesh, N. and Lakonishok, J. (1996). Momentum strategies.
Journal of Finance, LI(5).
Chan, P.T. and Walter, T. (2014). Investment performance of “environmentally-
friendly” firms and their initial public offers and seasoned equity offers.
Journal of Banking & Finance, 44, 177-188.
Chen, K. and Metcalf, R. (1980). The Relationship between Pollution Control
Records and Financial Indicators Revisited. Accounting Review, 55, 168-180.
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
127
Chesney, M., Reshetar, G. and Karaman, M. (2011). The impact of terrorism on
financial markets: an empirical study. Journal of Banking & Finance, 35,
253–67.
Cohen, M., Fenn, S. and Naimon, J. (1995). Environmental and Financial
Performance. Washington, DC: IRRC.
Corrado, C. J. (1989). A non-parametric test for abnormal security price performance
in event studies. Journal of Financial Economics, 23, 385–95.
Crandall, R. (1981). Pollution Controls and Productivity Growth in Basic Industries,
in T.G. Cowing and R.F. Stevenson (eds) Productivity Measurement in
Regulated Industries, New York: Academic Press.
Crain, N.V. and Crain W.M. (2010). The Impact of Regulatory Costs on Small
Firms. Washington, DC: Small Business Administration.
Cropper, M.L. and Oates, W.E. (1992). Environmental Economics: A Survey.
Journal of Economic Literature, 30, 675-740.
Daly, H. (1991). Steady-State Economics (second edition), Washington, DC: Island
Press.
Dann, L. (1981). Common stock repurchases: An analysis of returns to bondholders
and stockholders. Journal of Financial Economics, 9, 113-138.
Data Resources Incorporated (1979). The Macroeconomic Impact of Federal
Pollution Control Programs: 1978 Assessment. Report Submitted to the
Environmental Protection Agency and the Council on Environmental Quality,
January.
Dean, J.M., Lovely, M.E. and Wang, H.(2009). Are Foreign Investors Attracted to
Weak Environmental Regulations? Evaluating the Evidence from China.
Journal of Development Economics, 90, 1–13.
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
128
Denison, E. (1979). Accounting For Slower Economic Growth: The U.S. in the
1970s. Brookings Institution, Washington, DC.
Dent, W.T. and Collins, D.W. (1981). Econometric testing procedures in market-
based accounting and finance research. University of Iowa, Iowa City, IA.
Dimson, E. (1979). Risk mismeasurement when share are subject to infrequent
trading. Journal of Financial Economics, 7, 197–226.
Dolley, J. (1933). Characteristics and Procedures of Common Stock Split-Ups.
Harvard Business Review, 11 (3), 316-327.
Dowell, G., Hart, S. and Yeung, B. (2000). Do Corporate Global Environmental
Standards Create or Destroy Value? Management Science, 46, 1059-1074.
Eberly, J. (2011). Is Regulatory Policy A Major Impediment to Job Growth? Office
of Economic Policy, Washington, DC.
Environmental Protection Agency (1992). The Clean Air Market Place: New
Business Opportunities Created by the Clean Air Act Amendments: Summary
of Conference Proceedings. Washington, DC: Office of Air and Radiation, 24
July.
Esty, D.C. and Porter, M.E. (2002). Ranking National Environmental Regulation and
Performance: A Leading Indicator of Future Competitiveness? In World
Economic Forum, Global Competitiveness Report 2001-2002, New York:
Oxford University Press.
Fama, E. (1970) "Efficient Capital Markets: A Review of Theory and Empirical
Work". Journal of Finance, 25 (2), 383–417.
Fama, E. (1976). Foundations of Finance. Basic Books, New York, NY.
Fama, E., Fisher, L., Jensen, M. and Roll, R. (1969). The Adjustment of Stock Prices
to New Information. International Economic Review, 10, 1–2.
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
129
Fama, E. and French, K. (1992). The economic fundamentals of size and book-to-
market equity. Working paper, Graduate School of Business. University of
Chicago, Chicago, IL.
Fama, E. and French, K. (1993). Common risk factors in the returns on stocks and
bonds. Journal of Financial Economics, 33, 3–56.
Fama, E. and French, K. (2015). A five-factor asset pricing model. Journal of
Financial Economics, 116(1), 1-22.
Fama, E. and MacBeth, J. (1973). Risk, return, and equilibrium: empirical tests.
Journal of Political Economy, 81, 607–636.
Fan, J. and Yao, Q. (2003). Nonlinear Time Series. Nonparametric and Parametric
Methods. Springer, New York.
Fatemi, A., Fooladi, I, and Tehranian, H. (2015). Valuation effects of corporate
social responsibility. Journal of Banking & Finance, 59, 182-192.
Feldman, S., Soyka, P. and Ameer, P. (1996). Does Improving a Firm’s
Environmental Management System and Environmental Performance Result
in a Higher Stock Price? Washington, DC: ICF Kaiser.
Flammer, C. (2012). Corporate Social Responsibility and Stock Prices: The
Environmental Awareness of Shareholders. Working Paper, MIT Sloan
School of Management.
Gardiner, D. (1994). Does Environmental Policy Conflict With Economic Growth?
Resources, Spring (115), 20-21.
Gollop, F.M. and Roberts, M.J. (1983). Environmental Regulations and Productivity
Growth: The Case of Fossil-Fuelled Electric Power Generation. Journal of
Political Economy, 91, 654-674.
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
130
Graham, M. and Ramiah, V. (2012). Global terrorism and adaptive expectations in
financial markets: evidence from Japanese equity market. Research in
International Business and Finance, 26, 97–119.
Graff, J.S. and Neidel, Z.M. (2011). The Impact of Pollution on Worker Productivity.
NBER Working Papers, No 17004, April.
Gray, W.B. (1987). The Cost of Regulation: OSHA, EPA and the Productivity
Slowdown. American Economic Review, 77, 998-1006.
Gray, W.B. and Shadbegain, R.J. (1993). Environmental Regulation and
Manufacturing Productivity at the Plant Level. NBER Working Papers, No
4321.
Greenstone, M., List, J.A. and Syverson, C. (2012). The Effects of Environmental
Regulation on the Competitiveness of U.S. Manufacturing. Working Paper,
September.
Grossman, G. and Kreuger, A. (1993). Environmental Impacts of a North American
Free Trade Agreement: The U.S.-Mexico Free Trade Agreement. Cambridge
(MA): MIT Press.
Halkos, G. and Sepetis, A. (2007). Can capital markets respond to environmental
policy of firms? Evidence from Greece. Ecological Economics, 63, 578-587.
Hamilton, J. T. (1995). Pollution as News: Media and Stock Market Reactions to the
Toxics Release Inventory Data. Journal of Environmental Economics and
Management, 28, 98-113.
Hart, S.L. (1997). Beyond Greening Strategic for Sustainable World. Harvard
Business Review, 75, 66-76.
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
131
Hart, S.L. and Ahuja, G. (1996). Does it Pay to be Green? An Empirical Examination
of the Relationship Between Emission Reduction and Firm Performance.
Business Strategy and the Environment, 5, 30-37.
Haveman, R.H. and Christiansen, G.B. (1981). Environmental Regulations and
Productivity Growth. Natural Resources Journal, 21, 489-509.
Heinrichs, H., Martens, P., Michelsen, G. and Wiek, A. (2015). Sustainability
science: An Introduction, Springer.
Hills, P. and Man, C.S. (1998). Environmental Regulation and the Industrial Sector
in China: The Role of Informal Relationships in Policy Implementation.
Business Strategy and the Environment, 7, 53-70.
Hoffmann, V.H., Trautmann, T. and Hamprecht, J. (2009). Regulatory Uncertainty:
A Reason to Postpone Investments? Not Necessarily. Journal of Management
Studies, 46, 1227-1253.
Holthausen, R. (1981). Evidence on the effect of bond covenants and management
compensation contracts on the choice of accounting techniques: The case of
the depreciation switchback. Journal of Accounting and Economics, 3, 73-
109.
Jaffe, A.B., Peterson, S.R. Portney, P.R. and Stavins, R.N. (1995). Environmental
Regulation and the Competitiveness of U.S. Manufacturing: What Does the
Evidence Tell us? Journal of Economic Literature, 33, 132-163.
Jegadeesh, N. and Titman, S. (1993). Returns to buying winners and selling losers:
Implications for stock market efficiency. Journal of Finance, 48, 65-91.
Jorgenson, D.W. and Wilcoxen, P.J. (1990). Environmental Regulation and U.S.
Economic Growth. RAND Journal of Economics, 21, 314-340.
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
132
Kalt, J.P. (1988). The Impact of Domestic Environmental Regulatory Policies on
U.S. International Competitiveness. In A.M. Spence and H.A. Hazard (eds)
International Competitiveness. Cambridge (MA): Harper and Row.
Klassen, R. D. and McLaughlin, C. P. (1996). The impact of environmental
management on firm performance. Management Science, 42, 1199-1199.
Korten, D. (1995). When Corporations Rule the World. San Francisco: Berrett-
Koehler Publishers.
Leftwich, R. (1981). Evidence on the impact of mandatory changes in accounting
principles on corporate loan agreements. Journal of Accounting and
Economics,3, 3-36.
Leonard, H.J. (1988). Pollution and the Struggle for the World Product. Cambridge:
Cambridge University Press.
Levinson, A. (1996). Environmental Regulations and Manufacturer’s Location
Choices: Evidence from the Census of Manufacturers. Journal of Public
Economics, 62, 5–29.
Levinson, A. and Taylor, M.S. (2008). Unmasking the Pollution Haven Effect.
International Economic Review, 49(1), 223–254.
Levy, T. And Dinopoulos, E. (2016). Global environmental standards with
heterogeneous polluters. International Review of Economics & Finance, 43,
482-498.
Low, P. and Yeats, A. (1992). Do Dirt Industries Migrate? World Bank Discussion
Paper Series, No 159, World Bank.
Lyne, J. (1990). Service Taxes, International Site Selection and the “Green” Moment
Dominate Executives. Political Focus, 5, 1134-1138.
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
133
Mahapatra, S. (1984). Investors Reaction to Corporate Social Accounting. Journal of
Business Finance and Accounting, 11, 29-40.
Maitra, P. (2003). Environmental Regulation, International Trade, and Trans
boundary Regulation. http://www.eolss.net/Sample-Chapters/C13/E1-23-06-
03.pdf.
Marcus, A. A. and Kaufman, A. M. (1986). Why it is so difficult to implement
industrial policies: lessons from the synfuels experience. California
Management Review, 28, 98-114.
Masulis, R. (1980). The effects of capital structure change on security prices: A
study of exchange offers. Journal of Financial Economics, 8, 139-177.
McConnell V.D and Schwab, R.M. (1990). The Impact of Environmental Regulation
on Industry Location Decisions: The Motor Vehicle Industry. Land
Economics, 66, 67- 81.
McWilliams, A. and Siegel, D. (2000). Corporate Social Responsibility and Financial
Performance. Strategic Management Journal, 21, 603-609.
Meyer, S.M. (1992). Environmentalism and Economic Prosperity: Testing the
Environmental Impact Hypothesis. MIT, Mimeo.
Mill, G. (2006). The Financial Performance of an Socially Responsible Investment
over Time and Possible Link with Corporate Social Responsibility. Journal of
Business Ethics, 63, 131-148.
Mol, A.P.J., 2009 Urban Environmental Governance Innovations in China, Current
Opinion in Environmental Sustainability, 1, 96,100.
Moosa, I.A. and Cardak, B.A. (2006). The Determinants of Foreign Direct
Investment: An Extreme Bounds Analysis. Journal of Multinational
Financial Management, 16, 199-211.
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
134
Moosa, I.A. and Ramiah, V. (2014). The costs and benefits of environmental
regulations. Cheltenham: Edward Elgar Publishing.
Morgenstern, R.D., Pizer, W.A. and Shih, J.S. (2000). Jobs versus The Environment:
An Industry Level Perspective. Working Paper, Resources For The Future,
Washington, DC.
Mugwagwa, T., Ramiah, V., Naughton, T. and Moosa, I. (2012). The Efficiency of
the Buy Write Strategy: Evidence from Australia. Journal of International
Financial Market, Institution and Money, 22(2), 305-328.
Mugwagwa, T. Ramiah, V. and Moosa, I. (2015). The Profitability of Option-Based
Contrarian Strategies: An Empirical Analysis. International Review of
Finance, 15(1), 1-26.
Muoghalu, M.I., Robinson, H.D. and Glascock, J.L. (1990). Hazardous Waste
Lawsuits, Stockholder Returns, and Deterrence. Southern Economic Journal,
57, 357-370.
Munasinghe, M. (1999). Is environmental degradation an inevitable consequence of
economic growth: tunnelling through the environmental Kuznets curve.
Ecological Economics, 29(1), 89-109.
Murray, A., Sinclair, D., Power, D. and Gray, R. (2006). Do Financial Markets Care
about Social and Environmental Disclosure? Auditing and Accountability
Journal, 19, 228-255.
Myers, J. and Bakay, A. (1948). Influence of Stock Split-Ups on Market Price.
Harvard Business Review, 26, 251–255.
Naila, D.L. (2013). The Effect of Environmental Regulations on Financial
Performance in Tanzania: A Survey of Manufacturing Companies Quoted on
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
135
the Dar Es Salaam Stock Exchange. International Journal of Economics and
Financial Issues, 3, 99-112.
Neto, J.V.F., Da Silva Gomes, S.M., Bruni, A.L. and J.M.D.Filho, (2017). Do
environmental disasters impact on the volume of socio-environmental
investment and disclosure of Brazilian companies? Advances in Environmental
Accounting and Management 6: 159-188.
Oberndorfer, U. (2009). EU Emission Allowances and the stock market: Evidence
from the electricity industry. Ecological Economics, 68, 1116 – 1126
Oestreich, A.M. and Tsiakas, I. (2015). Carbon emissions and stock returns:
Evidence from the EU Emissions Trading Scheme. Journal of Banking &
Finance, 58, 294-308.
Oosterhuis, F. (ed) (2006). Ex-post Estimates of Costs to Business of EU
Environmental Legislation: Final Report. Brussels: European Commission.
Patell, J. and Wolfson, M. (1979). Anticipated information releases reflected in call
option prices. Journal of Accounting and Economics, 1, 117-140.
Pearson, C.S. (ed) (1987). Multinational Corporations, Environment and the Third
World. Durham (NC): Duke University Press and World Resources Institute.
Peng, W-B., Tian, K., Tian,Y-H. and Xiang, G.C. (2011). VAR Analysis of Foreign
Direct Investment and Environmental Regulation: China’s Case. Business
and Economic Horizons, 5, 13-22.
Pham, H.N.A., Ramiah, V. and Moosa, I. (2015). Are European Environmental
Regulations Excessive? The6th Conference on Financial Markets and
Corporate Governance, Fremantle, Australia.
Porter, M.E. (1991). America’s Green Strategy. Scientific America, 264, 168.
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
136
Porter, M.E. and Van Der Linde, C.V. (1995). Green and Competitive: Ending the
Stalemate. Harvard Business Review, 73,120-134.
Qi, Y. (2008) China’s Challenges In Environmental Regulation. Working Paper,
School of Public Policy and Management, Tsinghua University.
Ramiah, V. (2012). The Impact of International Terrorist Attacks on the Risk and
Return of Malaysian Equity Portfolios. Review of Pacific Basin Financial
Markets and Policies, 15(4), 1-26.
Ramiah, V. (2013). The Impact of the Boxing Day Tsunami on the World Capital
Markets. Review of Quantitative Finance and Accounting, 40(2), 383-401.
Ramiah, V., Cam, M.A, Calabro, M., Maher, D. and Ghafouri, S. (2010). Changes in
Equity Returns and Volatility across Different Australian Industries
Following the Recent Terrorist Attacks. Pacific-Basin Finance Journal,
18(1), 64-76.
Ramiah, V., Cheng, K.Y., Orriols, J., Naughton, T. and Hallahan, T. (2011).
Contrarian Investment Strategies Work Better for Dually-Traded Stocks:
Hong Kong Evidence. Pacific-Basin Finance Journal, 19(1), 140-156.
Ramiah, V. and Davidson, S. (2007). An Information-Adjusted Noise Model:
Evidence of inefficiency on the Australian Stock Market. Journal of
Behavioural Finance, 8(4), 209-224.
Ramiah, V. and Graham, M. (2013). The International Political Economy of
Terrorism and the Stock Market: Evidence from Indonesia. International
Journal of Accounting and Information Management, 21(1), 91 – 107.
Ramiah, V. and Gregoriou, G.N. (2016). Handbook of Environmental and
Sustainable Finance. Academic Press, Australia.
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
137
Ramiah, V., Li, D.L., Carter, J., Seetanah, B. and Thomas, S. (2016). Explaining
Contrarian Profits with Finance Fundamentals. Advances in Investment
Analysis and Portfolio Management, Forthcoming.
Ramiah, V., Martin, B. and Moosa, I. (2013). How Does the Stock Market React to
the Announcement of Green Policies? Journal of Banking and Finance, 37,
1747–1758.
Ramiah, V. and Moosa, I. (2014). The Costs and Benefits of Environmental
Regulation. Edward Elgar, Australia.
Ramiah, V., Moosa, I., Pham, H. N. A., Scundi, A. and Teoh, W. H. (2015). The
effects of multilateral trading systems on risk and return in equity markets.
Applied Economics, 47(44), 4777-4792.
Ramiah, V., Morris, T., Moosa, I., Gangemi, M. and Puican, L. (2016). The effects
of announcement of green policies on equity portfolios: Evidence from the
United Kingdom. Managerial Auditing Journal, 31(2), 138-155.
Ramiah, V., Mugwagwa, T. and Naughton, T. (2011). Hot and Cold Strategies:
Australian Evidence. Review of Pacific Basin Financial Markets and
Policies, 14(2), 1-25.
Ramiah, V., Pham, H. N. A. and Moosa, I. (2016). The Sectoral Effects of Brexit on
the British Economy: Early Evidence from the Reaction of the Stock Market.
Applied Economics, Forthcoming.
Ramiah, V., Pichelli, J. and Moosa, I. (2015a). The Effects of Environmental
Regulation on Corporate Performance: A Chinese Perspective. Review of
Pacific Basin Financial Markets and Policies, 18(4).
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
138
Ramiah, V., Pichelli, J. and Moosa, I. (2015b). Environmental regulation, the Obama
effect and the stock market: some empirical results. Applied Economics,
47(7), 725-738.
Ramiah, V., Regan-Beasley, J. and Moosa, I. (2016). The Black Friday Effect,
Advances in Investment Analysis and Portfolio Management, 7, 143-160.
Ramiah, V., Xu, X. and Moosa, I. (2015). Neoclassical Finance, Behavioural Finance
and Noise Traders. International Review of Financial Analysis, 41, 89-100.
Repetto, R. (1995). Jobs, Competitiveness, and Environmental Regulation: What are
the Real Issues? Washington, DC: World Resources Institute.
Ross, S. (1976). The Arbitrage Theory of Capital Asset Pricing. Journal of Economic
Theory, 13, 341–360.
Ruback, R. (1982). The effect of discretionary price control decisions on equity
values. Journal of Financial Economics, 10, 83-105.
Sandor, R.L. (2012). Good Derivatives: A Story of Financial and Environmental
Innovation. Wiley, U.S.
Scholes, M. and Williams, J. (1977). Estimating betas from non synchronous data.
Journal of Financial Economics, 5, 309–327.
Selden, T. and Song, D. (1994). Environmental Quality and Development: Is There a
Kuznets Curve for Air Pollution Emissions? Journal of Environmental
Economics and Management, 27, 147-162.
Sharpe, W. (1964). Capital asset prices: a theory of market equilibrium under
conditions of risk. Journal of Finance, 19, 425–442.
Shi, H. and Zhang, L.(2006) China’s Environmental Governance of Rapid
Industrialization, Environmental Politics, 15, 271-292.
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
139
Siegel, R. (1979). Why Has Productivity Slowed Down? Data Resources U.S.
Review, 8(1), 59-65.
Sinclair, T. and Vesey, K. (2012) Regulation, Jobs, and Economic Growth: An
Empirical Analysis. Working Paper, George Washington University,
Regulatory Studies Centre.
Spicer, B. (1978). Investors’ Corporate Social Performance and Information
Disclosure. Accounting Review, 33, 94-111.
Stafford, H.A., (1985). Environmental Protection and Industrial Location. Annals of
the Association of American Geographers, 5, 227-241.
Stewart, R. (1993). Environmental Regulation and International Competitiveness.
Yale Law Journal, 102, 2039-2106.
Sullivan-Wiley, K. A., and Gianotti, A. G. S. (2017). Risk Perception in a Multi-
Hazard Environment. World Development.
Tanaka, S., (2010). Environmental Regulations in China And Their
Effects on Air Pollution and Infant Mortality, POPOV Research Network.
Tobey, J. (1990). The Effects of Domestic Environmental Policies on Patterns of
World Trade: An Empirical Test. Kyklos, 43, 191-209.
United Nations (1998). Kyoto Protocol to the United Nations Framework on Climate
Change.
Veith, S., Werner, J. R., Zimmermann, J. (2009). Capital market response to
emission rights returns: Evidence from the European power sector. Energy
Economics, 31, 605-613.
Vernon, R. (1992). Transnational Corporations: Where are They Coming From,
Where are They Headed. Transnational Corporations, 1, 7-35.
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
140
Waddock, S. and Graves, S. (1997). The Corporate Social Performance and Financial
Performance Link. Strategic Management Journal, 18, 303-319.
Wagner, M., Vanphu, N., Azomahou, T. and Wehrmeyer, W. (2002). The
Relationship between the Environmental and Economic Performance for
Firms: An Empirical Analysis of European Paper Industry. Corporate Social
Responsibility and Environmental Management, 9, 113-146.
Wang, L. and Kutan, A.M. (2013). The Impact of Natural Disasters on Stock
Markets: Evidence from Japan and the US. Comparative Economic Studies
55 no. 4: 672-686.
Wahba, H. (2008). Does the Market Value Corporate Environmental Responsibility?
An Empirical Examination. Corporate Social Responsibility and
Environmental Management, 15, 89-99.
Walsh, D. (2012). Economic Growth by Stricter Regulation, 30 April.
http://environment.yale.edu/yer/article/economic-growth-by-stricter-
regulation
Walter, I. (1982). Environmentally Induced Industrial Relocation to Developing
Countries. In S.J. Rubin and T.R. Graham (eds) Environment and Trade: The
Relation of International Trade and Environmental Policy, Totowa (NJ):
Allanheld Osmun.
Wang, L. and Kutan, A.M. “The Impact of Natural Disasters on Stock Markets:
Evidence from Japan and the US”,Comparative Economic Studies 55 no. 4
(2013): 672-686.
Weiderman, I., and Bacon, F. (2008). Hurricane Katrina's Effect on Oil Companies
Stock Prices. Academy of Strategic Management Journal, 7, 11.
The Effects of Environmental Disasters and Pollution Alerts on Stock Markets: Evidence from China
141
Wheeler, D. and Mody, A. (1992). International Investment Location Decisions: The
Case of U.S. Firms. Journal of International Economics, 33, 57-76.
White, M. (1995). Does it Pay to be Green? Corporate Environmental Responsibility
and Shareholder Value. Working Paper, University of Virginia.
Worthington, A. and H. Higgs (2004). Transmission of Equity Returns and Volatility
in Asian Developed and Emerging Markets: a Multivariate GARCH analysis.
International Journal of Finance and Economics9 71-80.
Worthington, A. C. and Valadkhani,M.(2004): “Measuring the impact of natural
disasters on capital markets: an empirical application using intervention
analysis”. Applied Economics 36 no.19, 2177-2186
Xepapadeas, A. (2005). Economic Growth and the Environment. In K.G. Mäler and
J. Vincent (eds) Handbook of Environmental Economics, Amsterdam:
Elsevier.
Xu, X., Ramiah, V., Moosa, I. and Davidson, S. (2016). An application of the
Information-adjusted Noise Model to the Shenzhen Stock Market.
International Journal of Managerial Finance, 12 (1), 71-91.
Yang, B., Burns, N. D. and Backhouse, C. J. (2004). Management of uncertainty
through postponement. International Journal of Production Research, 42,
1049-1064.
Zhang, J. (2012). Delivering Environmentally Sustainable Economic Growth: The
Case of China. Working Paper, Asia Society, September.
Zheng, D. and Shi, M. (2016). Multiple environmental policies and pollution haven
hypothesis: Evidence from China's polluting industries. Journal of Cleaner
Production.