The COVID-19 Outbreak and Affected Countries Stock Markets Response

Abstract

This paper evaluates the short-term impact of the coronavirus outbreak on 21 leading stock market indices in major affected countries including Japan, Korea, Singapore, the USA, Germany, Italy, and the UK etc. The consequences of infectious disease are considerable and have been directly affecting stock markets worldwide. Using an event study method, our results indicate that the stock markets in major affected countries and areas fell quickly after the virus outbreak. Countries in Asia experienced more negative abnormal returns as compared to other countries. Further panel fixed effect regressions also support the adverse effect of COVID-19 confirmed cases on stock indices abnormal returns through an effective channel by adding up investors’ pessimistic sentiment on future returns and fears of uncertainties.

Full text

International Journal of
Environmental Research
and Public Health
Article
The COVID-19 Outbreak and Affected Countries
Stock Markets Response
HaiYue Liu 1, Aqsa Manzoor 1,*, CangYu Wang 1, Lei Zhang 2,* and Zaira Manzoor 3
1Business School, Sichuan University, Chengdu 610064, China, seamoon@scu.edu.cn (H.L.);
2017141081028@stu.scu.edu.cn (C.W.)
2School of Computer Science, Sichuan University, Chengdu 610064, China
3Economics Department, Shandong University, Jinan 250100, China; zairamanzoor@yahoo.com
*Correspondence: 2017521080012@stu.scu.edu.cn (A.M.); zhanglei@scu.edu.cn (L.Z.)
Received: 9 February 2020; Accepted: 16 April 2020; Published: 18 April 2020


Abstract:
This paper evaluates the short-term impact of the coronavirus outbreak on 21 leading stock
market indices in major affected countries including Japan, Korea, Singapore, the USA, Germany,
Italy, and the UK etc. The consequences of infectious disease are considerable and have been directly
affecting stock markets worldwide. Using an event study method, our results indicate that the stock
markets in major affected countries and areas fell quickly after the virus outbreak. Countries in
Asia experienced more negative abnormal returns as compared to other countries. Further panel
fixed effect regressions also support the adverse effect of COVID-19 confirmed cases on stock indices
abnormal returns through an effective channel by adding up investors’ pessimistic sentiment on
future returns and fears of uncertainties.
Keywords: COVID-19; investor sentiment; abnormal returns; stock market indices
1. Introduction
On 31st December 2019, the World Health Organization (WHO) identified the first case of
COVID-19 in Wuhan China (https://www.who.int/emergencies/diseases/novel-coronavirus-2019).
In early and mid-January 2020, the virus started to spread to other Chinese provinces, supported by a
huge movement of people towards their hometowns to celebrate Chinese New Year which turned the
outbreak into a national crisis. Although Wuhan officials announced a complete travel ban in terms of
its residents on January 23, the virus still spread quickly. The WHO declared a global emergency due
to the rapidly spreading of COVID-19 on January 30, 2020. It’s only the sixth time that such type of
global emergency has been announced, with past examples including that of the Democratic Republic
of Congo Ebola outbreak and the Zika virus. Chinese scientists linked this disease to a virus family
known as coronaviruses, which includes both the severe acute respiratory syndrome (SARS) virus
and the Middle East respiratory syndrome (MERS). According to the Centre of Disease Control and
Prevention (CDC), the COVID-19 symptoms may occur within as few as 2 days or as long as 14 days
after exposure or contact with an already affected person, which makes it even harder to confirm and
control during early stages. By assessing the risk of spread and severity of COVID-19 outside China
WHO declared this virus as a pandemic on March 11, 2020. The fatality rate of COVID-19 as compare
to other known viruses is quite low, but its infection rate is relatively high (Table 1). As of March 23,
China, Italy, and the United States have most of the number of confirmed cases of COVID 19,81601,
59,138, and 31,573 respectively (WHO situation report–63, Figure 1). According to CDC and many
other researchers at the moment, the source of COVID-19 is unknown and there is no specific vaccine
and treatment [1–3].
Int. J. Environ. Res. Public Health 2020,17, 2800; doi:10.3390/ijerph17082800 www.mdpi.com/journal/ijerph

Int. J. Environ. Res. Public Health 2020,17, 2800 2 of 19
Table 1. Fatality Rates and Infection Rates of COVID-19 and Other Epidemics.
Epidemics Fatality Rate (Deaths/Cases) Infection Rate (Per Infected Person)
Ebola 50% 1.5–2.5
MERS 34.30% 0.42–0.92
SARS 10% 3
COVID-19 1%−3.4% 1.5–3.5
Seasonal flu 1%−3.4% 1.3
Source: Asian development bank report No. 128(https://www.adb.org/publications/economic-impact-covid19-
developing-asia).
Int. J. Environ. Res. Public Health 2020, 17, x 2 of 19
Table 1. Fatality Rates and Infection Rates of COVID-19 and Other Epidemics.
Epidemics
Fatality Rate (Deaths/Cases)
Infection Rate (Per Infected Person)
Ebola
50%
1.5–2.5
MERS
34.30%
0.42–0.92
SARS
10%
3
COVID-19
1%–3.4%
1.5–3.5
Seasonal flu
1%–3.4%
1.3
Source: Asian development bank report No. 128(https://www.adb.org/publications/economic-
impact-covid19-developing-asia).
Figure 1. World Health Organization (WHO) situation report 63
(https://www.adb.org/publications/economic-impact-covid19-developing-asia).
The WHO and public health officials performed the role of mediator to communicate the risk of
an outbreak to the investors and it shapes the investors’ sentiments towards the disease [4]. Investor’s
sentiments influence the stock markets significantly. When the market is trending upwards and there
is less perceived risk then investor behaves more optimistically. When the market is trending
downwards then investors’ sentiments become relatively pessimistic and investors will tend to wait
to enter the market until a revival begins [5,6]. Such situations lead to short term investor
overreaction. Shu [7] studied how mood affects financial market behavior. The study shows how the
fluctuations in investor mood directly affect prices for equilibrium assets and projected returns.
Researchers suggest that media coverage also affects the actions of investors, the higher the number
of articles relating to unexpected events, the greater the number of withdrawals [8–10]. Globalization
has linked economies worldwide and increased the interdependence of global financial markets in
recent years. This increased interdependence among the global stock markets may have an impact
on global investors’ decisions on asset allocation and on economies as well as economic policies to
ensure economic stability [11]. By using a vector auto regression model, In, Kim, and Yoon [12]
examined the dynamic linkages and interactions between the Asian stock markets and their results
showed that the markets became more closely linked during the financial crisis, except Malaysia. For
any global financial market analyst, it is obvious that stock markets continue to move in the same
direction in different countries. There are some variations, however, in the sense that some stock
markets appear more correlated with each other than others [13]. Although globalization brings
many significant economic advantages, it also plays an important role during infectious global crises
[14]. The planet has recently been hit by increasing numbers of infectious diseases such as Crimean
Congo hemorrhagic fever, Ebola virus, MERS CoV, SARS, Lassa fever, Nipah Virus, avian flu, Rift
Figure 1.
World Health Organization (WHO) situation report 63 (https://www.adb.org/publications/
economic-impact-covid19-developing- asia).
The WHO and public health officials performed the role of mediator to communicate the risk of
an outbreak to the investors and it shapes the investors’ sentiments towards the disease [
4
]. Investor’s
sentiments influence the stock markets significantly. When the market is trending upwards and there is
less perceived risk then investor behaves more optimistically. When the market is trending downwards
then investors’ sentiments become relatively pessimistic and investors will tend to wait to enter the
market until a revival begins [
5
,
6
]. Such situations lead to short term investor overreaction. Shu [
7
]
studied how mood affects financial market behavior. The study shows how the fluctuations in investor
mood directly affect prices for equilibrium assets and projected returns. Researchers suggest that
media coverage also affects the actions of investors, the higher the number of articles relating to
unexpected events, the greater the number of withdrawals [
8
–
10
]. Globalization has linked economies
worldwide and increased the interdependence of global financial markets in recent years. This increased
interdependence among the global stock markets may have an impact on global investors’ decisions
on asset allocation and on economies as well as economic policies to ensure economic stability [
11
].
By using a vector auto regression model, In, Kim, and Yoon [
12
] examined the dynamic linkages and
interactions between the Asian stock markets and their results showed that the markets became more
closely linked during the financial crisis, except Malaysia. For any global financial market analyst, it is
obvious that stock markets continue to move in the same direction in different countries. There are
some variations, however, in the sense that some stock markets appear more correlated with each other
than others [
13
]. Although globalization brings many significant economic advantages, it also plays
an important role during infectious global crises [
14
]. The planet has recently been hit by increasing

Int. J. Environ. Res. Public Health 2020,17, 2800 3 of 19
numbers of infectious diseases such as Crimean Congo hemorrhagic fever, Ebola virus, MERS CoV,
SARS, Lassa fever, Nipah Virus, avian flu, Rift Valley fever, Zika virus. The spread of contagious
disease not only affects people’s health and lives but also induces a decline in economic growth.
Explaining why market participants make decisions contrary to rational market participants’
assumptions is one of the central issues in the behavioral finance studies. There are major challenges of
COVID-19 to personal lives, including lockdowns (or lockdown-like situations) for a large number
of people. Besides the extreme occurrences of death and disease, many people across the globe are
panicking because of this fast-spreading infectious disease. Such external and unexpected shocks can
bring down economic trends and suddenly change investor’s sentiments. Kaplanski and Levy [
15
]
suggest that investment decisions can be affected by bad mood and anxiety and that anxious individuals
may be more pessimistic about future returns and therefore tend to take fewer risks. Anxiety creates a
negative feeling which can impact investment decisions and the subsequent returns on assets.
The unusual situation developed by COVID-19 offers us an opportunity to assess the pandemic’s
impact on the stock markets of affected nations due to an unforeseen and feared disease. In this paper,
we discuss the effect of COVID-19 on major affected countries’ stock markets as measured by their
leading stock indices in Japan, Singapore, Korea, Thailand, Indonesia, Russia, Malaysia, the USA and
Germany, etc. Due to the short time of the virus outbreak, an event study is conducted to examine the
impact of the unexpected outbreak of COVID-19 on stock market indices performances.
The remainder of the paper is organized in the following sections: Section 2includes the related
theoretical and empirical literature, the data and methodology are discussed in Section 3, followed by
the empirical evidence in Section 4, and Section 5includes a conclusion.
2. Literature Review
The impact of the COVID-19 is of crucial importance, especially since its first outbreak happened
in China, which is the main hub of foreign investment in Asia. Researchers believe that COVID-19 and
SARS belong to the same family, but these two epidemics differ significantly. Many previous studies
related to the economic effects of the infectious virus epidemic could be referred to as we discuss the
impact of COVID-19.
2.1. Economic Impact of Virus Outbreak
Existing literature concentrates on illness-associated costs of medical or economic effects arising
from morbidity as well as mortality due to disease. Siu and Wong [
16
] studied the spread of Hong
Kong’s SARS epidemic, and addressed its economic impact and suggested that the most serious
negative impacts were seen on the consumer side, with the short term severely affected by local
consumption and the export of tourism and air travel-related services. The economy did not face any
supply shock, as the manufacturing base present in the Delta of the Pearl River was unaffected and
products were usually exported to Hong Kong. By using the G-Cubed (Asia Pacific) model Lee and
McKibbin [
14
] evaluated the global economic impacts of the severe acute respiratory syndrome (SARS)
and according to them the effect of the SARS epidemic on human society all over the world is severe,
not only because the disease spreads rapidly through countries by global travel, but also because of
financial integration and globalization, any economic shock to one country spreads rapidly to others.
Ichev and Marinˇc [
17
] investigated whether the geographical proximity of information disseminated
by the 2014 Ebola outbreak, coupled with widespread media coverage, has affected US asset prices.
The results show that the effect on stock prices is generally negative, while local media reporting also
has a significant impact on local trading, and the effect is more pronounced in smaller and more volatile
stocks and less stable industries.
2.2. Impacts on Stock Market Performances
Looking at the effect on stock markets, DeLisle [
18
] proposed that the cost of the 2003 SARS
outbreak resulted in losses as high as in the financial crisis of Asia, estimated at $3 trillion value in GDP

Int. J. Environ. Res. Public Health 2020,17, 2800 4 of 19
and $2 trillion value in financial markets equity. Nippani and Washer [
19
] examined the effect of SARS
on Canada, China, the particular administrative region of Hong Kong, Indonesia, China, Singapore, the
Philippines, Vietnam and Thailand and concluded that SARS only affected the stock markets of China
and Vietnam. Del and Paltrinieri [
9
] evaluated the 78 mutual equity funds geographically based in
African countries with observed monthly flows and results for the 2006–2015 period and suggested that
Ebola and the Arab Spring seriously affect the funds flows, controlling the performance of the funds,
spending, and returns of the market. Macciocchi et al., [
20
] studied the short-term economic impact
of the Zika virus outbreak on Brazil, Argentina and Mexico, and their results showed that, with the
exception of Brazil, the market indices of these three Latin American and Caribbean Countries (LCR)
did not show large negative returns the day after each shock. The average return was
−
0.90 percent
but on different occasions and countries it ranged from 0.90 percent to
−
4.87 percent. Ming-Hsiang
Chen, Shawn, and Gon [
21
] checked the SARS outbreak impacts on the efficiency of Taiwanese hotel
stocks using an event study approach and found that during the SARS outbreak period, seven publicly
traded hotel companies experienced steep declines in income and stock price. Taiwanese hotel stocks
showed significant negative cumulative mean abnormal returns on and after the day of the SARS
outbreak, indicating a significant impact of the SARS outbreak on performance in hotel stock. Mei-ping
Chenet al., [
22
] analyzed the effect of the SARS epidemic on China’s long-term relationship with four
Asian stock markets their findings support the existence of a time-varying co-integration relationship
in aggregate stock price indices, and they also found that the SARS epidemic has weakened China’s
long-term relationship with the four markets. Wang, Yang, and Chen [
23
] suggested that infectious
disease outbreaks have a major impact on the performance of biotechnology stock in Taiwan. According
to Bai [
24
] Baker, Wurgler, and Yuan [
25
] investors may feel pessimistic about investment prospects in
a given market, selling offthat market’s stocks under communicable disease outbreak.
2.3. Linkages between Stock Markets during Crisis
Stocks markets are interlinked and interdependent. Researchers have discovered the close
cross-market correlations during the crisis. Chiang, Nam, and Li [
26
] examined the daily stock return
for nine Asian markets for the period of 1996 to 2003 and found that there was a high correlation
among sample Asian countries during the period of crises. Sun and Hou [
27
] found that in Southeast
Asia, Malaysia, Vietnam, and Thailand were most financially integrated with China. According to
Morales and Callaghan [
28
] the global stock markets were becoming more interdependent and crisis in
one country would soon spread to another. Stock market movements become increasingly correlated.
Events like infectious disease outbreaks can induce negative changes in investors’ sentiment that
strongly affects their investment decisions and, consequently, stock market prices. In countries that are
culturally more susceptible to herd-like actions and overreaction or countries with low institutional
participation, the effect of investor sentiment on stock markets is more pronounced [29,30].
3. Event Study Method
Mackinlay [
31
] believed that the idea of event study method was first embodied in research by
Dolley [
32
] before that Ball and Brown [
33
] and Famaet al., [
34
] first proposed the method systematically.
According to the theory of the event study method, when an efficient market hypothesis is valid,
the influence of a particular event will be reflected in the change of stock price, to explain the effect
on the return of stocks and reaction to information disclosure. Therefore, the event study method
is widely used in economics and finance empirical studies to identify the impact of specific events.
For example, Agrawa and Kamakura [
35
] studied the effect of celebrity endorsement through the
analysis of abnormal stock returns. Gaver, K. M., and Battistel [
36
] studied stock market responses to
the adoption of long-term compensation agreements for top management. Thompson [
37
] analyzed
the impact of anticipated sectoral adjustments to the Canada–United States Free Trade Agreement
on industry-level stock returns and proposed that the overall impact of trade liberalization on the

Int. J. Environ. Res. Public Health 2020,17, 2800 5 of 19
economy was positive. Additionally, other studies on the impact of sudden diseases on the stock
market have applied the event study method as well.
Wang et al., [
23
] investigated how outbreaks of infectious diseases affected the performance of
biotechnology stocks, showing that Taiwan’s biotechnology industry had significant abnormal returns
due to statutory infectious diseases.
Based on existing literature, event study methodology is chosen to investigate the abnormal
returns (ARs) and cumulative abnormal returns (CARs) of the leading stock indices of affected countries
under the COVID-19 outbreak.
3.1. Data and Methodology
3.1.1. Data of the Selected Stock Indices and Benchmark Index for Estimation
The following 21 stock indices in Table 2, which are the most representative indices of the stock
markets in affected countries and areas, were chosen to assess the impact of the COVID-19 outbreak.
Table 2. Selected indices for affected countries and areas.
Definition Abbreviation Country/Area
Abu Dhabi Securities Exchange (ADX) Composite Index ADX Abu Dhabi
Cotation Assistée en Continu (CAC) 40 Index CAC40 France
Deutsche Aktien Xchange (DAX) Performance Index GDAXI Germany
Dow Jones Industrial Average Index DJIA The USA
Financial Times Stock Exchange (FTSE) 100 Index FTSE100 The UK
FTSE Bursa Malaysia Kuala Lumpur Composite (KLCI) Index KLSE Malaysia
Jakarta Composite Index JKSE Indonesia
Korea Composite Stock Price Index KOSPI Korea
Moscow Exchange (MOEX) Russia Index IMOEX.ME Russia
Nikkei 225 Index N225 Japan
S&P/Australian Securities Exchange (S&P/ASX) 200 Index AXJO Australia
S&P/Toronto Stock Exchange Composite Index (S&P/TSX) Composite Index GSPTSE Canada
Straits Time Index STI Singapore
Taipei (TPE), Taiwan Stock Exchange (TAIEX) Index TPE TAIEX Taiwan
iShares Morgan Stanley Capital International (MSCI) All Country Asia ex
Japan Exchange Traded Fund (ETF) AAXJ Asia ex Japan
Stock Exchange of Thailand (SET) 50 Index SET50 Thailand
HangSeng Index HSI Hong Kong
Shanghai Composite Index SSEC Shanghai
Shenzhen Composite Index SZCS Shenzhen
FTSE Milano Indice di Borsa (MIB) Index FTMIB Italy
National Stock Exchange (NIFTY) 50 Index NSEI India
Dow Jones Global Index, an international index reflecting the overall performance of stock markets
across the world, is selected as the benchmark index to calculate the abnormal returns of composite
indexes listed above. We collected daily closing prices of these indexes from 21 February, 2019 to
18 March, 2020. The data sources used for this study are the China Stock Market & Accounting Research
(CSMAR) database and website Investing.com (a website offering free real time quotes, portfolio,
streaming charts, live stock market data, etc.).
3.1.2. Event Study Set-up
In this paper, we examine the impact of the unexpected outbreak of COVID-19 on stock markets of
affected countries. According to several COVID-19 news sources, in late December 2019 a new disease
outbreak was recorded in Wuhan. Later, on Dec. 31, the virus was first identified to the WHO. But it
was not until 20 January, 2020, when the National Health and Fitness Commission of the People’s
Republic of China high-level expert group leader Zhong Nanshan proposed in an interview that the
new coronavirus could be transmitted among people, that the disease attracted wide public attention.
Right after the interview, the infectious coronavirus began to appear in the press over the world, which

Int. J. Environ. Res. Public Health 2020,17, 2800 6 of 19
grabbed the headlines of the major media. Thus, 20 January, 2020, when the news broke out causing
a stir, is selected as the event day. To study the influence in different periods, we set up five event
windows consisting of 35 trading days after the event day: (0, 6), (7, 13), (14, 20), (21, 27), (28, 34).
Referring to related researches [
23
,
38
], we define the estimated window of 90 trading days before the
event day when studying the influence of infectious diseases on the market behavior. As there is a
lot of uncertainty in the stock market, too long a window period may not be accurate. To test the
sensitivity of our results, we also use (-1,–120), (
−
1,–150) and (
−
1,–180) as the estimated windows to
compute the abnormal returns. We use the T-test to test the significance of the results and change the
event window and estimated window to strengthen the robustness. Moreover, results from event
windows of different lengths reflect the various response speeds and changing trends of the stock
market. The expected returns are derived using the market model, and the ordinary least square (OLS)
based on the following regression model:
Ri,t=αi+βiRmt +εi,t(1)
R
i,t
is the return of index i and R
mt
is the market return on day t (as the event day is day 0) within
the estimated window, with
εi,t
as the statistic disturbance. After obtaining the estimated coefficients,
ˆ
αi
and
ˆ
βi
, the following formulas are applied to calculate the expected return and abnormal return (AR):
E(Ri,t)=ˆ
αi+ˆ
βiRmt (2)
ARi,t=Ri,t−E(Ri,t)(3)
E(R
i,t
), R
i,t
and AR
i,t
are the expected return, real return and abnormal return of index i on day t
within the event window. The average abnormal return of sample indices on day t is calculated as:
AARt=1
N
N
X
i=1
ARi,t(4)
where t =(0,1,2
. . .
32,33,34), and N is the total number of observations. Abnormal return and
average abnormal return can be accumulated over time. Cumulative abnormal return (CAR) of index i
over a while from t
0
to t
1
and cumulative average abnormal return (CAAR) are calculated based on
Equations (5) and (6):
CARi(t0,t1)=
t1
X
t=t0
ARi,t(5)
CAAR(t0,t1)=
t1
X
t=t0
AARt(6)
4. Empirical Results of Event Study on AR and CAR
The mean and standard deviation of the composite index return before and after the event are
given in Table 3. As the basic statistic description, where Panel A shows the data from 21 February,
2019 to 19 January, 2020 and Panel B shows the data from 20 January, 2020 to 18 March, 2020, Table 3
indicates that after 20 January, 2020, all the mean returns decreased and most standard deviations
increased compared with the previous ones. The indices for France, Germany, Russia, Italy, Thailand,
the UK, Canada, Japan, the USA, India, Abu Dhabi and Australia decreased the most in mean return,
by 0.01 approximately, while those for Singapore, Thailand, Korea, Indonesia, and Hong Kong fell the
most (by 325.245%, 274.619%,115.163%, 64.345%, and 49.086%, respectively) by percentage. Whereas,
the mean returns of SSEC and SZCS, which represent the market of the mainland of China, fell the
least in percentage. It appears that COVID-19 reduces the stock market returns in all affected countries
and increases their volatility, showing not only a greater impact on the stock markets in Asia but also
an inescapable influence on those in countries out of Asia.

Int. J. Environ. Res. Public Health 2020,17, 2800 7 of 19
Table 3. Differences in mean returns of sample indices.
Index Number of Trading Days Event Group’s Mean Event Group’s Std. Dev.
Panel A: Pre-event period from 2-21-2019 to 1-19-2020
AAXJ 230 0.0004542 0.0094672
ADX 180 0.0005975 0.0086469
AXJO 230 0.0006648 0.0069479
CAC40 231 0.0007271 0.0079969
DJIA 230 0.0005609 0.0072776
FTMIB 228 0.0007997 0.0089854
FTSE100 229 0.0002862 0.0070348
GDAXI 227 0.0007878 0.0083612
GSPTSE 228 0.0004087 0.0043428
HSI 225 0.000131 0.0097464
IMOEX.ME 228 0.0011242 0.0069037
JKSE 222 −0.0001297 0.0072075
KLSE 224 −0.0003385 0.0049151
KOSPI 225 0.0000722 0.0078631
N225 218 0.0005636 0.008545
NSEI 220 0.0006775 0.0089491
SET50 221 −0.0000365 0.0070016
SSEC 225 0.0005439 0.0114002
STI 234 0.0000212 0.0059575
SZCS 225 0.0010874 0.0145107
TPE TAIEX 227 0.000739 0.0064854
Panel B: Post-event period from 1-20-2020 to 3-18-2020
AAXJ 41 −0.0075615 0.0329298
ADX 35 −0.0083242 0.027362
AXJO 42 −0.0080026 0.0287038
CAC40 43 −0.0108071 0.0284906
DJIA 41 −0.0086318 0.0399845
FTMIB 43 −0.0100961 0.0372304
FTSE100 43 −0.009224 0.0251664
GDAXI 43 −0.0105135 0.0275578
GSPTSE 42 −0.0088958 0.0366424
HSI 41 −0.0062992 0.0170684
IMOEX.ME 41 −0.0097904 0.0227542
JKSE 43 −0.0084753 0.0186309
KLSE 42 −0.0059079 0.0141128
KOSPI 41 −0.0082426 0.0189679
N225 41 −0.0086462 0.0180879
NSEI 41 −0.0082748 0.023342
SET50 42 −0.0100601 0.0302894
SSEC 37 −0.003025 0.0201833
STI 43 −0.006874 0.0158945
SZCS 37 −0.001663 0.0254438
TPE TAIEX 35 −0.0075659 0.0175156
Table 4illustrates the ARs of the sample indices on and after 20 January, 2020. On the day of
the event, the representative composite indices for France, London, Malaysia, Indonesia, Hong Kong,
Singapore, Thailand, Italy and India react most rapidly with negative ARs. On the following day,
ARs of ADX, DJIA, FTSE100, KOSPI, IMOEX.ME, N225, AXJO, STI, TPE TAIEX, AAXJ, SET50, HSI,
SSEC, SZCS, FTMIB and NSEI are negative. It can be seen that the actual returns of Asian countries
were further away from the expectation than that of other regions, with indices representing markets
in Taiwan, Hong Kong, Shanghai and Shenzhen indexes decreasing most significantly on day 1.
It indicates that the Chinese stock market suffered a serious negative impact when the news of the
coronavirus was firstly widely reported by the international media.
Figures 2and 3give ARs and CARs of the main indices in Asia from day 0 to 34, showing that
most ARs become negative during 1 day after the event. The pandemic broke out in Korea and Italy on
19 February, 2020 and 21 February, 2020, respectively, indicating another two big events of COVID-19;
we marked the two outbreaks in the figures to show the specific reaction of stock markets on the

Int. J. Environ. Res. Public Health 2020,17, 2800 8 of 19
timeline. In Figure 2, some indices for instance, TPE TAIEX, HSI and SZCS, saw a sharp decline in
AR right after the event day. On day 4, ARs of Asian main indices experience a dramatic fall with the
biggest drop in SZCS for Shenzhen, SSEC for Shanghai and KOSPI for Korea, rendering the following
fluctuation, which becomes more violent after day 24 (outbreak in Italy). Figure 3shows that the CARs
of included indices keep going down overall from day 0 to 4, after which SZCS and SSEC keep going
up from day 5 to 20. The following violent fluctuation of ARs has different cumulative effects on
indices as CARs of SZCS, SSEC, HSI and AAXJ increase in general while others decrease or stay stuck.
Table 4. Abnormal return on event day and one day after.
Index Abnormal Return
Event Day 1 Day after Event Day
ADX 0.0068259 −0.0056369
CAC40 −0.003114 0.0003379
GDAXI 0.0019976 0.0062665
DJIA −0.0000451
FTSE100 −0.002304 −0.0000359
KLSE −0.0040547 0.0001122
JKSE −0.006961 0.0007234
KOSPI 0.0046693 −0.0084402
IMOEX.ME 0.0061993 −0.0024564
N225 0.0007641 −0.0080299
AXJO 0.0018052 −0.0009313
GSPTSE 0.002124 0.0006704
STI −0.0001791 −0.0085767
TPE TAIEX 0.0013035 −0.0577211
AAXJ −0.0218231
SET50 −0.0078305 −0.0059545
HSI −0.008803 −0.0244252
SSEC 0.0068395 −0.0119402
SZCS 0.0125077 −0.0118903
FTMIB −0.0053448 −0.0010451
NSEI −0.0113759 −0.0042944
Int. J. Environ. Res. Public Health 2020, 17, x 9 of 19
Figure 2. Abnormal Return (AR) change of main indices in Asia from day 0 to 34.
Figure 3. Cumulative abnormal return (CAR) change of main indices in Asia from day 0 to 34.
Figure 4 and Figure 5 exhibit ARs and CARs of main indices out of Asia from day 0 to 34. As is
shown in Figure 4, the ARs witness violent fluctuations relatively except those of GSPTSE for Canada
and DJIA for America, with a drastic “up and down” between day 3 and day 5. After day 24, a violent
fluctuation occurs across all indices showing an obvious negative influence on ARs. Figure 5 indicates
that before day 24 there is no significant effect on CARs except a gentle decline and increase for
IMOEX.ME and FTMIB respectively. After day 24, CARs of most indices decrease in general.
Figure 2. Abnormal Return (AR) change of main indices in Asia from day 0 to 34.

Int. J. Environ. Res. Public Health 2020,17, 2800 9 of 19
Int. J. Environ. Res. Public Health 2020, 17, x 9 of 19
Figure 2. Abnormal Return (AR) change of main indices in Asia from day 0 to 34.
Figure 3. Cumulative abnormal return (CAR) change of main indices in Asia from day 0 to 34.
Figure 4 and Figure 5 exhibit ARs and CARs of main indices out of Asia from day 0 to 34. As is
shown in Figure 4, the ARs witness violent fluctuations relatively except those of GSPTSE for Canada
and DJIA for America, with a drastic “up and down” between day 3 and day 5. After day 24, a violent
fluctuation occurs across all indices showing an obvious negative influence on ARs. Figure 5 indicates
that before day 24 there is no significant effect on CARs except a gentle decline and increase for
IMOEX.ME and FTMIB respectively. After day 24, CARs of most indices decrease in general.
Figure 3. Cumulative abnormal return (CAR) change of main indices in Asia from day 0 to 34.
Figures 4and 5exhibit ARs and CARs of main indices out of Asia from day 0 to 34. As is shown in
Figure 4, the ARs witness violent fluctuations relatively except those of GSPTSE for Canada and DJIA
for America, with a drastic “up and down” between day 3 and day 5. After day 24, a violent fluctuation
occurs across all indices showing an obvious negative influence on ARs. Figure 5indicates that before
day 24 there is no significant effect on CARs except a gentle decline and increase for IMOEX.ME and
FTMIB respectively. After day 24, CARs of most indices decrease in general.
Int. J. Environ. Res. Public Health 2020, 17, x 10 of 19
Figure 4. AR change of main indices out of Asia from day 0 to 34.
Figure 5. CAR change of main indices out of Asia from day 0 to 34.
Tables 5–9 compare the significant CARs of affected countries in different event windows. Table
5 illustrates that in the event window (0,6), indices for Hong Kong, Malaysia, Japan, Thailand and
Asia ex-Japan show significant negative CARs while Canada shows a significantly positive CAR. It
appears that Asian countries experience an obvious downturn after the breakout of COVID-19
immediately. According to the data shown in Table 6, the CAR of Abu Dhabi representative index in
the event window (7,13) is –0.021254 (5% level), while those of Shanghai and Shenzhen representative
index turn significantly positive at 0.0311078 (10% level) and 0.0506241 (10% level), respectively,
Figure 4. AR change of main indices out of Asia from day 0 to 34.

Int. J. Environ. Res. Public Health 2020,17, 2800 10 of 19
Int. J. Environ. Res. Public Health 2020, 17, x 10 of 19
Figure 4. AR change of main indices out of Asia from day 0 to 34.
Figure 5. CAR change of main indices out of Asia from day 0 to 34.
Tables 5–9 compare the significant CARs of affected countries in different event windows. Table
5 illustrates that in the event window (0,6), indices for Hong Kong, Malaysia, Japan, Thailand and
Asia ex-Japan show significant negative CARs while Canada shows a significantly positive CAR. It
appears that Asian countries experience an obvious downturn after the breakout of COVID-19
immediately. According to the data shown in Table 6, the CAR of Abu Dhabi representative index in
the event window (7,13) is –0.021254 (5% level), while those of Shanghai and Shenzhen representative
index turn significantly positive at 0.0311078 (10% level) and 0.0506241 (10% level), respectively,
Figure 5. CAR change of main indices out of Asia from day 0 to 34.
Tables 5–9compare the significant CARs of affected countries in different event windows. Table 5
illustrates that in the event window (0, 6), indices for Hong Kong, Malaysia, Japan, Thailand and
Asia ex-Japan show significant negative CARs while Canada shows a significantly positive CAR.
It appears that Asian countries experience an obvious downturn after the breakout of COVID-19
immediately. According to the data shown in Table 6, the CAR of Abu Dhabi representative index in
the event window (7, 13) is
−
0.021254 (5% level), while those of Shanghai and Shenzhen representative
index turn significantly positive at 0.0311078 (10% level) and 0.0506241 (10% level), respectively,
which demonstrates the recovery of the Chinese stock market as in China the spread of COVID-19 is
being controlled.
Table 5. Cumulative abnormal return in the event window (0, 6).
Index CARi(0, 6) t-Test p-Value
HSI −0.0792506 ** −1.975444 0.0482178
KLSE −0.0258508 * −1.874593 0.0608488
AAXJ −0.0443531 * −1.786196 0.0740676
GSPTSE 0.0065514 * 1.93884 0.0525208
N225 −0.0317926 * −1.664644 0.0959838
SET50 −0.0446717 * −1.836844 0.066233
GDAXI 0.0135345 0.5680507 0.5700005
AXJO −0.00571 −0.2690441 0.7878957
KOSPI −0.0482862 −1.178526 0.2385869
FTSE100 0.001566 0.079654 0.9365125
IMOEX.ME −0.0243261 −1.093912 0.2739936
FTMIB 0.0230502 0.8349835 0.4037271
SZCS −0.0796273 −0.7984056 0.4246351
JKSE −0.0192188 −1.312946 0.1892012
NSEI −0.0255788 −1.424322 0.1543534
ADX −0.001587 −0.0890671 0.9290286
SSEC −0.0910787 −1.095587 0.2732597
TPE TAIEX −0.0373395 −0.56546 0.5717609
CAC40 −0.0003477 −0.0180654 0.9855867
DJIA 0.0035989 0.7588902 0.4479183
STI −0.0234682 −1.011555 0.3117507
Notes: * Significant at the 10% level. ** Significant at the 5% level.

Int. J. Environ. Res. Public Health 2020,17, 2800 11 of 19
Table 6. Cumulative abnormal return in the event window (7, 13).
Index CARi(7, 13) t-Test p-Value
ADX −0.021254 ** −2.327517 0.0199378
SSEC 0.0311078 * 1.890039 0.0587528
SZCS 0.0506241 * 1.741973 0.0815132
AAXJ 0.0022715 0.0942906 0.9248784
HSI 0.0167401 0.6628569 0.5074222
FTSE100 −0.0084192 −0.8061661 0.4201471
IMOEX.ME −0.0191139 −1.506789 0.1318648
NSEI 0.020922 0.9712772 0.3314103
JKSE −0.0221123 −1.068213 0.2854244
SET50 0.0136898 0.6219259 0.5339906
AXJO −0.006852 −0.3604153 0.7185366
CAC40 0.003732 0.2948792 0.7680862
FTMIB 0.0037601 0.1884156 0.8505509
DJIA 0.0088673 0.5705396 0.5683118
STI 0.0117588 0.5398692 0.5892872
GDAXI 0.0012289 0.1095778 0.9127442
KOSPI 0.0105887 0.3394943 0.7342374
GSPTSE 0.0062839 0.9443354 0.3449982
KLSE 0.0028516 0.1731493 0.862534
N225 0.0140107 0.3945872 0.6931475
TPE TAIEX −0.0073383 −0.4206511 0.6740099
Notes: * Significant at the 10% level. ** Significant at the 5% level.
Table 7shows that in the event window (14, 20), N225 for Japan and ADX for Abu Dhabi show
significant negative CARs at
−
0.0337579 (5% level) and
−
0.0356547 (10% level), respectively. SZCS for
Shenzhen and SSEC for Shanghai are still significantly positive in CARs. The effects of COVID-19 on
stock markets during this period are not as significant as a whole. Table 8indicates the indices for
the UK and France show positive CARs at 0.029436 (1% level) and 0.0241526 (10% level), respectively.
During this time window, Europe has not become the center of the pandemic outbreak. Table 9shows
that in the window (28, 34), CARs of indices representing Taiwan, India and Australia are
−
0.1419123
(1% level),
−
0.0873618 (10% level) and
−
0.1167886 (10% level). Most indices of countries out of Asia for
instance, France, the UK, Russia and Italy, have negative CARs.
Table 7. Cumulative abnormal return in the event window (14, 20).
Index CARi(14, 20) t-Test p-Value
SZCS 0.1079927 *** 3.284576 0.0010214
N225 −0.0337579 ** −2.275619 0.0228688
SSEC 0.0641881 ** 2.426681 0.0152377
FTMIB 0.0207867 * 1.679509 0.0930529
ADX −0.0356547 * −1.882657 0.0597468
FTSE100 −0.0112175 −0.8909222 0.3729709
JKSE −0.0189848 −1.443648 0.148838
IMOEX.ME −0.0053375 −0.3103923 0.7562627
AAXJ 0.0020406 0.1118767 0.9109212
KLSE −0.0104853 −0.7950887 0.4265619
NSEI −0.0165062 −1.240968 0.2146177
KOSPI −0.0055114 −0.3353011 0.737398
GSPTSE 0.0048234 0.870159 0.3842134
HSI 0.0089929 0.5463076 0.5848545
AXJO 0.0081514 1.198896 0.2305684
DJIA −0.0027707 −0.3628692 0.7167026
CAC40 0.0040015 0.364354 0.7155937
GDAXI 0.0095633 1.116724 0.2641122
SET50 −0.0091309 −0.4958354 0.6200106
STI −0.0067322 −0.3105193 0.7561661
TPE TAIEX −0.0200302 −1.145352 0.2520634
Notes: * Significant at the 10% level. ** Significant at the 5% level. *** Significant at the 1% level.

Int. J. Environ. Res. Public Health 2020,17, 2800 12 of 19
Table 8. Cumulative abnormal return in the event window (21, 27).
Index CARi(21, 27) t-Test p-Value
FTSE100 0.029436 *** 2.710452 0.0067191
CAC40 0.0241526 * 1.881764 0.059868
KLSE 0.0005445 0.0180252 0.9856187
GSPTSE −0.0121544 −1.029907 0.3030536
NSEI −0.0138207 −0.6640217 0.5066764
SSEC 0.0371332 0.7138488 0.4753207
JKSE −0.0040976 −0.1965423 0.8441858
DJIA −0.0105779 −1.122875 0.2614908
TPE TAIEX −0.0250806 −0.6890613 0.4907847
ADX −0.0222626 −0.5759524 0.5646474
N225 −0.0556228 −1.572033 0.115943
GDAXI 0.0195479 1.425256 0.154083
IMOEX.ME −0.036838 −1.047606 0.2948203
KOSPI −0.0407597 −0.9009348 0.367623
AXJO −0.0373936 −1.461306 0.1439314
AAXJ 0.057032 1.603071 0.108919
STI −0.0062721 −0.3232006 0.7465433
SET50 −0.0208295 −0.2719478 0.7856622
HSI 0.0473891 1.203362 0.2288363
FTMIB 0.0213244 0.5655288 0.5717142
SZCS 0.0027614 0.0391268 0.9687893
Notes: * Significant at the 10% level. *** Significant at the 1% level.
Table 9. Cumulative abnormal return in the event window (28, 34).
Index CARi(28, 34) t-Test p-Value
TPE TAIEX −0.1419123 *** −3.664365 0.000248
NSEI −0.0873618 * −1.798962 0.0720247
AXJO −0.1167886 * −1.893935 0.0582336
ADX −0.1858343 −1.353782 0.1758059
JKSE −0.0139353 −0.250492 0.8022069
CAC40 −0.0373533 −0.7463557 0.4554526
AAXJ 0.0505535 1.008108 0.3134028
SZCS −0.0345424 −0.839241 0.4013341
FTSE100 −0.0278968 −0.7244245 0.4688052
SET50 −0.0710598 −0.8344743 0.4040138
IMOEX.ME −0.1615433 −1.63697 0.1016367
STI −0.0350006 −1.032785 0.3017044
N225 −0.0467718 −1.409184 0.1587808
SSEC 0.0174407 0.3435947 0.7311511
KOSPI 0.0083866 0.2428905 0.8080902
FTMIB −0.0572362 −0.9230368 0.3559881
GDAXI −0.0366837 −0.6212927 0.534407
DJIA 0.0278418 0.7289439 0.466036
KLSE −0.0362832 −1.060241 0.289035
HSI 0.0095766 0.1674873 0.8669866
GSPTSE −0.0940138 −1.412604 0.1577721
Notes: * Significant at the 10% level. ** Significant at the 5% level. *** Significant at the 1% level.
The results show that the stock markets in Asia, especially in Hong Kong, Malaysia, Japan, and
Thailand, responded rapidly to the news of the coronavirus outbreak. For the mainland Chinese
market, the negative influence does not last for long as SZCS and SSEC show significantly positive
CARs in the event window (7, 13) and (14, 20). This demonstrates the quick recovery of the mainland
Chinese market from the pandemic after the confirmed cases decrease. For stock markets in countries

Int. J. Environ. Res. Public Health 2020,17, 2800 13 of 19
out of Asia, there is no noticeable decline of cumulative abnormal return until day 24 in this group
causing negative CARs for most countries, especially significant for Australia.
The results of the daily cumulative average abnormal return (CAAR) across all indices, which is
using an average of 21 indices we chose, are shown in Table 10, indicating that most of the CAARs
are significant and decrease over time, from
−
0.0002348 on day 0 to
−
0.0706297 (5% level) on day 34.
Figure 6illustrates the change of AAR (average abnormal returns of all indices) and CAAR from day 0
to 34, which is a downward sloping trend as a whole with stagnation in between day 10 and day 27.
It seems that there are two plunges in stock markets on day 1 and day 24, which roughly match the
outbreaks in and out of Asia. Similar results using (
−
1, –120), (
−
1, –150) and (
−
1, –180) as the estimated
windows also support the findings which shows the robustness of event negative effect on AR and
CAR using (−1,–90) as estimation window (available under inquiry).
Table 10. Daily cumulative average abnormal return across all indices.
Event Window Coef. Se t-Test p-Value
0−0.0002348 0.00129068 −0.18192307 0.8556431
1−0.00809842 * 0.00315944 −2.5632496 0.01036975
2−0.00715138 * 0.00294243 −2.430432 0.01508084
3−0.0130106 ** 0.00357785 −3.6364307 0.00027644
4−0.01848152 * 0.00748873 −2.4679127 0.01359035
5−0.02472998 ** 0.00691317 −3.5772281 0.00034726
6−0.02543742 ** 0.00684348 −3.7170292 0.00020158
7−0.02182337 ** 0.00630466 −3.4614666 0.00053724
8−0.02577939 *** 0.00598241 −4.3091966 0.00001638
9−0.02698855 *** 0.00547445 −4.9299134 0.0000008227
10 −0.02791188 *** 0.00580662 −4.8069029 0.000001533
11 −0.02256519 *** 0.0048827 −4.6214559 0.000003811
12 −0.02026957 *** 0.00519989 −3.8980742 0.00009696
13 −0.02003991 ** 0.00548889 −3.650998 0.00026122
14 −0.01792733 ** 0.00549579 −3.2620101 0.00110625
15 −0.01904359 ** 0.00555927 −3.4255525 0.00061355
16 −0.0182333 ** 0.00542373 −3.3617668 0.00077445
17 −0.01529506 * 0.00579366 −2.639966 0.00829143
18 −0.01578378 * 0.00646971 −2.4396415 0.01470184
19 −0.01696656 * 0.00732636 −2.3158232 0.02056792
20 −0.01744843 0.00840386 −2.0762396 0.0378718
21 −0.01903315 * 0.00811395 −2.3457313 0.01898979
22 −0.01782969 0.00924501 −1.9285741 0.05378375
23 −0.02108545 * 0.00778318 −2.709105 0.0067465
24 −0.02158735 * 0.00857419 −2.5177137 0.01181193
25 −0.02095435 * 0.00828558 −2.5290149 0.01143832
26 −0.02219828 * 0.00991002 −2.2399834 0.025092
27 −0.01965741 0.01209225 −1.6256206 0.10403039
28 −0.03025933 0.01530937 −1.9765242 0.04809544
29 −0.03593303 * 0.01453605 −2.4719938 0.01343618
30 −0.04155065 ** 0.01311263 −3.1687507 0.00153096
31 −0.03815619 * 0.01552319 −2.4580131 0.01397081
32 −0.04317276 * 0.01727333 −2.4993887 0.01244078
33 −0.0527609 * 0.02012455 −2.6217179 0.00874878
34 −0.0706297 ** 0.02192245 −3.2217983 0.00127389
Notes: * Significant at the 5% level. ** Significant at the 1% level. *** Significant at the 0.1% level.

Int. J. Environ. Res. Public Health 2020,17, 2800 14 of 19
Int. J. Environ. Res. Public Health 2020, 17, x 14 of 19
12
–0.02026957 ***
0.00519989
–3.8980742
0.00009696
13
–0.02003991 **
0.00548889
–3.650998
0.00026122
14
–0.01792733 **
0.00549579
–3.2620101
0.00110625
15
–0.01904359 **
0.00555927
–3.4255525
0.00061355
16
–0.0182333 **
0.00542373
–3.3617668
0.00077445
17
–0.01529506 *
0.00579366
–2.639966
0.00829143
18
–0.01578378 *
0.00646971
–2.4396415
0.01470184
19
–0.01696656 *
0.00732636
–2.3158232
0.02056792
20
–0.01744843
0.00840386
–2.0762396
0.0378718
21
–0.01903315 *
0.00811395
–2.3457313
0.01898979
22
–0.01782969
0.00924501
–1.9285741
0.05378375
23
–0.02108545 *
0.00778318
–2.709105
0.0067465
24
–0.02158735 *
0.00857419
–2.5177137
0.01181193
25
–0.02095435 *
0.00828558
–2.5290149
0.01143832
26
–0.02219828 *
0.00991002
–2.2399834
0.025092
27
–0.01965741
0.01209225
–1.6256206
0.10403039
28
–0.03025933
0.01530937
–1.9765242
0.04809544
29
–0.03593303 *
0.01453605
–2.4719938
0.01343618
30
–0.04155065 **
0.01311263
–3.1687507
0.00153096
31
–0.03815619 *
0.01552319
–2.4580131
0.01397081
32
–0.04317276 *
0.01727333
–2.4993887
0.01244078
33
–0.0527609 *
0.02012455
–2.6217179
0.00874878
34
–0.0706297 **
0.02192245
–3.2217983
0.00127389
Notes: * Significant at the 5% level. ** Significant at the 1% level. *** Significant at the 0.1% level.
Figure 6. Average abnormal return (AAR) and cumulative average abnormal return(CAAR) change from
day 0 to 34.
To test for possible COVID-19 outbreak effects and transmission channel on major stock market
indices we used panel data for 21 market indices in a 35-day window after the outbreak. We
conducted ordinary least square (OLS) regressions to analyze the outbreak effect. The variables we
chose to include in the empirical model are discussed in the earlier sections as daily abnormal returns
(AR) as a dependent variable (the calculation of AR is using Equation (3) in the earlier section: event
study set-up). The independent variable is the logged global COVID-19 confirmed cases (Log_case)
which we extracted from the WIND database. Furthermore, we controlled global market systematic
risks using Dow Jones Global Index daily returns (ReturnM) and country specific systematic risks
Figure 6.
Average abnormal return (AAR) and cumulative average abnormal return (CAAR) change
from day 0 to 34.
To test for possible COVID-19 outbreak effects and transmission channel on major stock market
indices we used panel data for 21 market indices in a 35-day window after the outbreak. We conducted
ordinary least square (OLS) regressions to analyze the outbreak effect. The variables we chose to
include in the empirical model are discussed in the earlier sections as daily abnormal returns (AR) as
a dependent variable (the calculation of AR is using Equation (3) in the earlier section: event study
set-up). The independent variable is the logged global COVID-19 confirmed cases (Log_case) which
we extracted from the WIND database. Furthermore, we controlled global market systematic risks
using Dow Jones Global Index daily returns (ReturnM) and country specific systematic risks using
daily returns of each index (Return). In further regressions to test the mediating effect, we used S&P
500 volatility index (VIX) provided by the Chicago Board Options Exchange which was widely used as
a proxy to gauge investors’ fear. The summary statistics are shown in Table 11.
It can be seen that the mean of AR,ReturnM, and Return are all negative after the virus outbreak.
Table 11. Summary Statistics.
Variable. Obs Mean Std. Dev. Min Max
AR 735 −0.0018 0.0137 −0.0938 0.0402
ReturnM 735 −0.0046 0.0180 −0.0949 0.0488
Return 735 −0.0045 0.0169 −0.1027 0.0509
Log_case 735 7.3615 1.0955 4.3944 9.9323
VIX 688 23.0242 12.5632 12.85 82.69
5. OLS Regression Results of COVID-19 Confirmed Cases and Stock Market Indices ARs
A panel data analysis was conducted to capture the major stock market indices performance after
the outbreak. The panel dataset consisted of a cross-section dimension (21 indices, i =1,
. . .
N) and a
time dimension (35 periods: t =(0,
. . .
34)), there were over 735 observations, which was considered
adequate to produce robust estimations within the scope of the analysis. To begin, we used OLS to
analyze the country-level stock market indices in response to the virus outbreak while controlling for
global and country-specific characteristics embedded in stock indices, including country and year
fixed effects. In our main tests, we analyze how the confirmed cases of COVID-19 affects the abnormal

Int. J. Environ. Res. Public Health 2020,17, 2800 15 of 19
return of major markets stock indices. Then we set the dummy variable Asia to group the market
indices to see the regional effect of the virus outbreak as it could be spreading faster or getting more
attention in Asian areas at the beginning of the outbreak in our study period. If the index belongs to
the country in Asia, Asia equals to 1, otherwise, Asia equals to 0. The following is the baseline model of
our regression:
ARit =α+β1Log_caseit +β2Returnit +β3ReturnMit +γi+δt+εit (7)
First, we run regression on Log_case alone and we gradually add Return,ReturnM, before at last
we add dummy variable Asia to test the regional effect. The mean-centered variance inflation factors
(VIFs) for the independent variables specified are 3.01 which means there is no collinearity issue in the
regression model. We report robust standard errors to deal with the heterogeneity problems that is
always a concern with economic data. The results in Table 12 show a significant negative relationship
between confirmed COVID cases and stock market indices daily abnormal returns after the outbreak.
Asia gets a negative sign which indicates indices in Asian areas as a whole suffered more in their
performances compared with those which are out of the region. These results also echo our earlier
analysis using the event method. As a robustness check of our ordinary least squares (OLS) regression
we used feasible generalized least squares (FGLS) estimation with heteroscedastic error terms including
time dummies. The results also show the negative significance of COVID-19 confirmed cases on AR
(available under inquiry).
Table 12. OLS regression results of COVID-19 confirmed cases and stock market indices AR.
(1) (2) (3) (4)
VARIABLES AR AR AR AR
Log_case −0.00322 ** −0.00179 ** −0.000967 ** −0.000967 **
(0.00154) (0.000721) (0.000406) (0.000406)
Return 0.653 *** 0.863 *** 0.863 ***
(0.0422) (0.0239) (0.0239)
ReturnM −0.456 *** −0.456 ***
(0.0287) (0.0287)
Asia −0.00311 *
(0.00168)
Constant 0.0117 0.00571 * 0.00325 0.00636 ***
(0.00717) (0.00339) (0.00218) (0.00233)
Observations 735 735 735 735
R-squared 0.174 0.608 0.824 0.824
Year Control YES YES YES YES
Country Control YES YES YES YES
Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
6. Transmission Channel of COVID-19 Outbreak on Stock Market Indices
The public health emergency could transmit the effect to the economy as the stock market serves as
the barometer of investors’ expectations and faith in economic prospects [
24
,
25
]. COVID-19 pandemic
compounds uncertainties worldwide, increases stock investors’ fear and creates pessimistic sentiments
on future returns. To study the channel by which COVID-19 transmits the fear to stock markets globally,
we conducted further regressions to test the mediating effect through the channel of VIX. We set paths
to test models as follows (Path A: equation (8), Path B: equation (9), Path C: equation (10)):
ARit =α+β1Log_caseit +β2Controlsit +γi+δt+εit (8)
VIXit =α+β1Log_caseit +γi+δt+εit (9)
ARit =α+β1Log_caseit +β2VIXit +β3Controlsit +γi+δt+εit (10)

Int. J. Environ. Res. Public Health 2020,17, 2800 16 of 19
According to Baron and Kenny [
39
], if
β1
in front of Log_case in path A and
β1
in front of VIX in
path B both show significance, while
β1
is insignificant and
β2
is significant in path C, then we could
claim that VIX is an effective mediator between confirmed cases and stock indices AR. The results in
Table 13 indicate the mediator variable VIX is positively correlated to Log_case with significance in
columns 2 and 3 (Table 13). In column 3, Log_case is not significant after adding VIX into the regression.
VIX is indeed a complete mediator and the fears caused by the COVID-19 pandemic transmitted to the
stock markets by the channel of cumulated panic and uncertainties.
Table 13. Mediating effect of volatility index (VIX).
Path A Path B Path C
VARIABLES AR VIX AR
Log_case −0.000967 ** 2.321147 *** −0.000326
(0.000406) (0.5677434) (0.000359)
Return 0.863 *** 0.867 ***
(0.0239) (0.0246)
ReturnM −0.456 *** −0.498 ***
(0.0287) (0.0316)
VIX −0.000222 ***
(0.0000798)
Constant 0.00325 −0.5885524 0.00160
(0.00218) (2.97612) (0.00234)
Observations 735 688 688
R-squared 0.824 0.873 0.827
Year Control YES YES YES
Country Control YES YES YES
Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
7. Conclusions
This research has aimed to analyze the immediate effect of COVID-19 on the stock markets of the
major affected countries. This research adds to the literature as it explores the unexpected outbreak
effects on financial markets of a feared disease. From the viewpoint of an investor, the findings of this
analysis illustrate the importance of not only the company’s business factors but also the investment
risks brought on by such a sudden event. Our results suggest: (1) COVID-19 outbreak has a significant
negative effect on stock market returns across all affected countries and areas. Two plunges in stock
markets AAR and CAAR on day 1 and day 24 match the outbreaks in and out of Asia. (2) Stock
markets of Asian countries react more quickly to the outbreak with some of them recovering slightly in
the later stage of the pandemic. (3) Confirmed cases of COVID-19 have significant adverse effects on
major stock indices performances with those in Asia suffering a greater decrease in terms of abnormal
returns. (4) Investor’s fear sentiment is proved to be a complete mediator and transmission channel for
the COVID-19 outbreak’s effect on stock markets.
As the COVID-19 epidemic now becomes a pandemic, we need to think of not only ways to
avoid future public health problems but also financial issues as well. The virus spreads exponentially,
doubling new infections every two to three days, or even quicker. Fears of pandemic and policy
measures to control disease transmission have contributed to a global supply shock, especially in the
labor-intensive and manufacturing sector. To safeguard the staff, factories and offices are shutting or
reducing activities which decreases labor force, productivity, and ultimately affects the profitability of
companies. It would leave several businesses illiquid and, if not handled correctly by officials, would
cause companies to resort to staffcutbacks or to shut down entirely. This is the main explanation of
why financial markets have been in panic mode worldwide. Stock prices represent the potential of
future earnings, and investors see the pandemic as a dampening economic activity and are concerned

Int. J. Environ. Res. Public Health 2020,17, 2800 17 of 19
about future revenue. Before the severity of the deterioration is evident, the normal investors’ response
would be to sell the stocks.
Our findings have significant implications for policymakers. A coalition of government officials,
investment banks regulators, and the central bank would be required to tackle this challenge. Through
rolling over current loans, bank authorities would allow banks to be lenient towards businesses in
badly impaired economic sectors such as manufacturing, travel and tourism. Managing the COVID-19
crisis needs a rational approach such that officials should immediately inform citizens of what they
and the health care system will do without triggering uncertainty.
This paper presents an initial analysis of the pandemic issue; there is significant room for further
research into investor confidence inside and between foreign markets. In future studies, the research
could be taken on investor sentiment and uncertainty as a framework. Considering the practicality
of our conclusions, we conclude that our results would be valuable for institutional and individual
investors, fund managers, financial, industrial analysts, and public health officials to effectively
communicate the risk of an infectious disease. Health officials must consider the psychological and
sentimental impact of their announcements as well.
As with all studies our work has several limitations, one of them is that we only studied the
immediate and short-term effects of COVID-19 on majors affected countries’ stock markets due to the
short event window period and the evolving nature of the virus spread. Another limitation is that we
didn’t study the demographic variables such as age, gender, education level, experience in the stock
market and type of investor, etc. due to lack of data.
Author Contributions:
Conceptualization, H.L. and A.M.; methodology, H.L.; software, L.Z.; validation, A.M.,
C.W. and Z.M.; formal analysis, A.M.; investigation, C.W.; resources, L.Z.; data curation, C.W.; writing—original
draft preparation, A.M. and C.W.; writing—review and editing, A.M. and Z.M.; visualization, C.W.; supervision,
H.L.; project administration, H.L. and L.Z.; funding acquisition, H.L. All authors have read and agreed to the
published version of the manuscript.
Funding: This research was funded by the National Social Science Fund (19BJY100).
Conflicts of Interest: The authors declare no conflict of interest.
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