Relative Stock Market Performance during the Coronavirus Pandemic: Virus vs. Policy Effects in 80 Countries

Abstract

This paper examines relative stock market performance following the onset of the coronavirus pandemic for a sample of 80 stock markets. Weekly data on coronavirus cases and deaths are employed alongside Oxford indices on each nation's stringency and government support intensity. The results are broken down both by month and by geographical region. The full sample results show that increased coronavirus cases exert the expected overall effect of worsening relative stock market performance, but with little consistent impact of rising deaths. There is some evidence of significantly negative stock market effects arising from lockdowns as reflected in the Oxford stringency index. There are also positive reactions to government support in March and December in the overall sample-combined with some additional pervasive effects seen in mid-2020 in Latin America.

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Journal of
Risk and Financial
Management
Article
Relative Stock Market Performance during the Coronavirus
Pandemic: Virus vs. Policy Effects in 80 Countries
Richard C. K. Burdekin * and Samuel Harrison


Citation: Burdekin, Richard C. K.,
and Samuel Harrison. 2021. Relative
Stock Market Performance during the
Coronavirus Pandemic: Virus vs.
Policy Effects in 80 Countries. Journal
of Risk and Financial Management 14:
177. https://doi.org10.3390/
jrfm14040177
Academic Editor: Waël Louhichi
Received: 20 March 2021
Accepted: 8 April 2021
Published: 12 April 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
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iations.
Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Robert Day School of Economics & Finance, Claremont McKenna College, 500 E. Ninth Street, Claremont,
CA 91711, USA; sharrison22@students.claremontmckenna.edu
*Correspondence: rburdekin@cmc.edu
Abstract:
This paper examines relative stock market performance following the onset of the coron-
avirus pandemic for a sample of 80 stock markets. Weekly data on coronavirus cases and deaths are
employed alongside Oxford indices on each nation’s stringency and government support intensity.
The results are broken down both by month and by geographical region. The full sample results show
that increased coronavirus cases exert the expected overall effect of worsening relative stock market
performance, but with little consistent impact of rising deaths. There is some evidence of significantly
negative stock market effects arising from lockdowns as reflected in the Oxford stringency index.
There are also positive reactions to government support in March and December in the overall
sample—combined with some additional pervasive effects seen in mid-2020 in Latin America.
Keywords: stock markets; coronavirus; government policy
I can’t abandon that [lockdown] tool any more than I would abandon a nuclear
deterrent. But it is like a nuclear deterrent, I certainly don’t want to use it.
(British Prime Minister Boris Johnson, 19 July 2020—as quoted in Malnick 2020)
Speculative manias are in the air
. . .
Along with the other economic trends—a
strong recovery, surging commodity prices and an uptick in inflation—those
asset bubbles have a clear cause: the massive expansion of money and credit.
(Greenwood and Hanke 2021)
1. Introduction
The coronavirus pandemic of 2020 represented the most devastating health crisis since
the Spanish Flu of 1918–1919. Although stock markets reacted negatively to the spike in
death rates at that time (Burdekin 2021), far more violent moves were seen in 2020. The
record-breaking 33.7% drop in the S&P 500 stock market index between 19 February and
23 March 2020 was accompanied by massive declines in most other major stock markets
around the world. This was quickly followed by record GDP declines in the second quarter
of 2020 in the United States and many European countries. Nevertheless, most of these
same countries then enjoyed a major stock market boom from the March lows in the face of
massive central bank liquidity expansion (Burdekin 2020a).1
The initial US stock market plunge following the onset of the 2020 pandemic greatly
outpaced the earlier experience under the Spanish Flu. Even though the Spanish Flu
appears to have been far deadlier (cf. Burdekin 2020b), the US stock market initially
continued to rise following the outbreak in late 1918 and merely fell back to summer 1918
levels after monthly deaths peaked later in the year. There was a sharp rebound afterwards,
however, and the Dow Jones Industrial Average gained around 50% from late February
1919 through the end of the year. Pent-up demand following the end of the pandemic
may well have been a factor in this outcome (Burdekin 2020a)—as could prove to be true
once again in 2021. Nevertheless, a major difference between the 1918–1919 and 2020–2021
J. Risk Financial Manag. 2021,14, 177. https://doi.org/10.3390/jrfm14040177 https://www.mdpi.com/journal/jrfm

J. Risk Financial Manag. 2021,14, 177 2 of 18
experiences remains the far lesser extent of the lockdowns and government intervention
seen during the earlier episode.2
The main goal of this paper is to assess US stock market behavior not in isolation
but in comparison with relative performance in other world stock markets. Additionally,
in contrast to most prior empirical work, the analysis extends beyond the initial crisis
period up until the final trades of December 2020. Following the next section’s discussion
of the existing literature on stock market responses to the pandemic, Section 3outlines
the paper’s methodology. The data used to explain relative stock market performance on
the basis of virus spread and government intervention are also detailed there. The main
findings for our 80-country study are then presented in Section 4. Finally, Section 5contains
the paper’s conclusions.
2. Explaining the 2020 Stock Market Reactions
The enormous market turnaround after March 2020 occurred despite accelerating
coronavirus spread around the world, with cases rising in every continent besides Antarc-
tica. Cox et al. (2020, p. 20) conclude that the stock market rebound was largely driven by
shifts in sentiment rather than fundamentals: “We find that the most likely candidate for
explaining the market’s volatility during the early months of the pandemic is the pricing
of stock market risk, driven by big fluctuations in risk aversion or sentiment unrelated to
economic fundamentals or interest rates.” The rise in market volatility actually appears to
precede the March shutdowns and travel bans, with Just and Echaust (2020), for example,
identifying a shift from a low volatility regime to a high volatility regime in late February
2020.
3
Alber (2020) and Ashraf (2020) seek to quantify stock market responses to rising
virus cases and deaths early in the pandemic across as many as 64 countries. Although
they each identify a more significant role for cases than deaths, Al-Awadhi et al. (2020)
find both of these variables significant in China over a 10 January—16 March 2020 sample
period. Panel data analysis by Khan et al. (2020), using weekly data through the end of
March 2020, confirms significant effects of coronavirus cases across sixteen stock markets.
4
Although the pandemic continued to play a key role, the extreme shifts seen early on
in the pandemic were not typical of the later experience. Indeed, Phan and Narayan (2020)
find support for a possible initial stock market over-reaction to the coronavirus that was
already evident in data going through just early April 2020. This reflects Phan and Narayan
(2020) finding that initial negative reactions to new cases and deaths are often followed by
positive responses later on, with half of the 25 stock markets seemingly evincing positive
reactions after reaching 100,000 cases and 100 virus-related deaths. A case in point is the
remarkable US market rally from the March 2020 lows even as virus cases and deaths
continued to ratchet higher through the end of the year. Meanwhile, whereas contagion
between markets appeared to soar with the onset of the pandemic, Okorie and Lin (2021)
find that this initial shock effect was already fading away by the end of March 2020.
5
This
only points to the importance of considering data extending beyond the initial crisis period.
In addition to the spread of the virus itself, other factors coming into play over time
include government reactions to the public health emergency, such as the massive stimulus
measures undertaken in the United States and Europe to provide economic support to
individuals and businesses. The positive stock market effects themselves appear to have
been far from uniform, with Harjoto et al. (2021) finding the beneficial effects to have been
concentrated on larger firms.
6
These government spending initiatives were accompanied
by widespread monetary easing, which, in the US case, resulted in annual M2 money
supply growth of 28% after February 2020. This was far greater than during the global
financial crisis, and the highest rate of money emission seen since 1943 at the height
of World War II (Greenwood and Hanke 2021).
7
Australia was just one of many other
countries embarking on similarly unprecedented stimulus efforts. Nevertheless, Rahman
et al. (2021) find evidence of quite mixed stock market reactions with only the “JobKeeper”
package (announced on 22 March 2020) eliciting a clearly significant, favorable stock market
reaction.

J. Risk Financial Manag. 2021,14, 177 3 of 18
Shifts in the level of economic support in either direction could be expected to trigger
stock market reactions, with Chan-Lau and Zhao (2020), for example, emphasizing negative
stock market reactions in cases where government stimulus was withdrawn when the
number of daily COVID-19 cases remained relatively high. There is also the impact of
government-ordered lockdowns, however, and Baker et al. (2020) conclude that it was
these government restrictions on commercial activity, combined with social distancing,
that account for the US stock market reacting so much more forcefully to COVID-19 than to
the Spanish Flu (or prior pandemics). Given that stock markets would be expected to react
to both stimulus measures and lockdowns, this paper provides new comparative evidence
on the impact of each assessed over a broad range of world stock markets. The effects of
rising coronavirus cases and deaths are also controlled for in the analysis.
We compare relative market performance to each country’s relative levels of virus
cases, virus deaths, government stringency and economic support. We examine these
relationships not only in the aggregate but also on a continent-by-continent basis for
each month between March and December 2020. This encompasses the span between
the extreme pessimism evident in market reactions early in the pandemic to the greater
optimism emerging later on, spurred also by encouraging vaccine news in November and
December 2020. Following the release of favorable clinical trials data in November 2020,
the Pfizer COVID-19 vaccine was approved for emergency use in the United Kingdom on
2 December and in the United States on December 12.
3. Methodology and Properties of the Data
This paper sets virus effects on stock market performance against the relative impor-
tance of government interventions. Whereas most prior work was limited to data through
just the first quarter of 2020, this combination of factors is assessed over a sample period
extended from March 2020 through the end of the year.
8
Like Khan et al. (2020), we utilize
weekly data and employ pooled OLS (ordinary least squares) analysis. In order to allow
for varying relationships over the year, panel regression analysis is applied not only for the
full sample and also for the individual months between March 2020 and December 2020.
In order to make the analysis as broad-based as possible, we incorporate 80 stock market
indices drawn from all corners of the globe (Appendix ATable A1). Our focus is on the
relative strength of each market during 2020 and we essentially include every available
stock market, with just Venezuela and Zimbabwe excluded owing to the distortions associ-
ated with their respective hyperinflations that predated the pandemic. The stock market
data are drawn from the Bloomberg terminal (with the exception only of the Jamaican
market index, which is directly downloaded from the Jamaica Stock Exchange website at
www.jamstockex.com, accessed on 10 April 2021).
The paper’s panel estimation assesses how each market’s relative strength is affected
by variations in virus conditions and government measures. We utilize publicly available
data on coronavirus virus and deaths, in each case scaled by population in order to
achieve comparability across countries. The government policy responses are quantified
on the basis of the data series available in Oxford University’s “Coronavirus Government
Response Tracker” (Hale et al. 2020). The Oxford University data utilizes an ordinal
scale reflecting the relative level of economic support and the relative level of stringency.
Economic support encompasses income support, debt/contract relief for households, fiscal
measures, and provision of international support. Meanwhile, the stringency index collates
publicly available information on such policies as school and workplace closures, stay at
home restrictions and travel bans to produce an additive score measured on an ordinal
scale that varies from 0 to 100 (see Appendix ATable A2).
We regress each stock market’s relative strength index (market_RSI) on (i) increases in
cases per 100,000 (g_cases100k), (ii) increases in deaths per 100,000 (g_deaths100k), and
(iii) the Oxford series on the levels of government economic support (econsupport) and
stringency measures (stringency). A lagged dependent variable and lagged cases and
deaths are also included to allow for inertia. Increases in cases and deaths are used instead

J. Risk Financial Manag. 2021,14, 177 4 of 18
of cumulative cases and deaths owing to the non-stationarity of the latter series, which
continuously rise over the sample period. Although cases and deaths are themselves both
manifestations of the same virus spread, the timing of the two series is quite different.
Testa et al. (2020), for example, show deaths typically not occurring until between two
weeks and eight weeks after the onset of symptoms. Each of the Oxford series is measured
on a scale of 0–100, with zero being lowest and 100 representing maximum intensity. The
market_RSI puts each stock market on a scale from 1 to 80, with 1 being the top performer
and 80 being the bottom performer. Relative strength is assessed on the basis of the dollar
returns for each market index.9
In addition to the fluctuations in virus infections and policy responses over time,
Table 1reveals substantial geographical variation in 2020. Summary statistics over the
March-December 2020 period for the full sample of 80 countries (Table 1a) are followed by
summary statistics broken down according to the following geographical groupings:
Table 1. Summary statistics for full sample and by region.
a. Full Sample
count mean sd min max
market_RSI 4240 40.5 23.09493 1 80
g_cases100k 4160 41.44165 90.85629 −179.5596 1203.091
g_deaths100k
4160 0.7668794 1.798592 −3.2232 18.49932
stringency 4240 51.30944 27.16211 0 100
econsupport 4240 48.98585 34.41286 0 100
b. Africa
count mean sd min max
market_RSI 583 42.48885 23.49986 1 80
g_cases100k 572 9.616895 22.52485 0 146.5333
g_deaths100k
572 0.2194387 0.6140417 0 5.347495
stringency 583 48.40607 28.56593 0 93.52
econsupport 583 37.92882 32.34679 0 100
c. Australasia
count mean Sd min max
market_RSI 106 37.76415 21.59345 2 78
g_cases100k 104 1.502934 2.912689 0 13.40398
g_deaths100k
104 0.0392611 0.100428 −0.0039216 0.5803949
stringency 106 44.64877 26.9876 0 96.3
econsupport 106 60.14151 32.5555 0 100
d. East Asia
count mean sd min max
market_RSI 583 39.4837 21.63178 1 80
g_cases100k 572 4.373834 11.17784 0 120.0784
g_deaths100k
572 0.0442898 0.0892606 0 0.6588728
stringency 583 50.63443 23.04173 0 100
econsupport 583 46.93396 34.95796 0 100
e. Eastern and Southern Europe
count mean sd min max
market_RSI 795 40.54591 21.73602 1 80
g_cases100k 780 57.9161 117.8906 −0.4040714 748.3035
g_deaths100k
780 1.123939 2.549789 −0.4523048 17.31658
stringency 795 46.34185 26.29474 0 96.3
econsupport 795 53.01887 33.15399 0 100

J. Risk Financial Manag. 2021,14, 177 5 of 18
Table 1. Cont.
f. Latin America
count mean sd min max
market_RSI 530 42.88679 26.56565 1 80
g_cases100k 520 36.31582 51.12816 0 246.3407
g_deaths100k
520 1.13994 1.776847 0 15.79226
stringency 530 58.97732 30.16572 0 100
econsupport 530 41.58019 32.50223 0 100
g. North America
count mean sd min max
market_RSI 106 37.73585 18.72778 7 74
g_cases100k 104 72.89453 102.6356 0 463.8697
g_deaths100k
104 1.402589 1.410535 0 5.45615
stringency 106 54.84009 26.07422 0 75.46
econsupport 106 53.89151 29.04037 0 75
h. South Asia and Middle East
count mean Sd min max
market_RSI 636 40.47956 23.21433 1 80
g_cases100k 624 51.01512 86.15512 0 1203.091
g_deaths100k
624 0.3522827 0.4588852 0 2.98075
stringency 636 57.83392 28.00892 0 100
econsupport 636 44.65409 32.02359 0 100
i. Western Europe
count mean sd min max
market_RSI 901 39.08768 23.12896 1 80
g_cases100k 884 68.73868 123.657 −179.5596 1075.221
g_deaths100k
884 1.357633 2.388899 −3.2232 18.49932
stringency 901 49.26022 25.26129 0 93.52
econsupport 901 59.43396 36.13918 0 100
Africa: Botswana, Egypt, Ghana, Kenya, Malawi, Mauritius, Morocco, Nigeria,
South Africa, Tunisia and Zambia.
Australasia: Australia and New Zealand.
East Asia: China, Hong Kong, Indonesia, Japan, Malaysia, Philippines, Singapore,
South Korea, Taiwan, Thailand and Vietnam.
Eastern and Southern Europe: Bulgaria, Croatia, Czech Republic, Cyprus, Estonia,
Greece, Hungary, Lithuania, Latvia, Poland, Romania, Russia, Slovakia, Slovenia
and Ukraine.
Latin America: Argentina, Barbados, Bermuda, Brazil, Chile, Colombia, Jamaica,
Mexico, Peru and Trinidad.
North America: Canada and United Sates.
South Asia and Middle East: Bahrain, India, Israel, Kazakhstan, Kuwait, Lebanon,
Oman, Pakistan, Qatar, Saudi Arabia, Turkey and United Arab Emirates.
Western Europe: Austria, Belgium, Denmark, Finland, France, Germany, Iceland,
Ireland, Italy, Luxembourg, Netherlands, Norway, Portugal, Spain, Sweden,
Switzerland and United Kingdom.
The geographical groupings are primarily done by continent, but with Asia and Europe
divided into two parts. In the case of Europe, the separation is between the more established
nations of Western Europe and the primarily emerging economies of Eastern and Southern
Europe. Although East Asia covers a range of development levels, all these nations share
relative proximity with China and most are members of ASEAN (Association of Southeast

J. Risk Financial Manag. 2021,14, 177 6 of 18
Asian Nations). The countries in the South Asia and Middle East group are distinct both in
terms of geographical location and by typically having lesser links with China.
The summary data broken down by region in Table 1b–i show some of the enormous
variations in national experiences under the pandemic. For example, North America
(Canada and United States) show an average 72.9 increase in cases per hundred thousand
and 1.40 deaths per hundred thousand. Even with all these numbers scaled by population,
the North America figures are over fifty times greater than the corresponding Australasia
values. Cases and deaths in Africa and East Asia are also dramatically lower than in North
America, while the other regions represent intermediate cases—but generally closer to the
North American levels than Africa, Australasia, and East Asia.
The highest average levels of stringency are seen in Latin America, South Asia and
Middle East, North America, and East Asia (in that order). The other regions are all
below 50 on a scale of 0–100, with Australasia having the lowest average value of all.
However, it should be emphasized that even Australia’s average stringency level of 44.6 is
substantial, leaving it within 25% of the highest level of 59.0 registered in Latin America.
Interestingly, despite having the lowest virus cases, virus deaths and average stringency
levels, Australasia has the highest average level of government economic support (60.1).
Western Europe is just behind with 59.4, followed by North America with 59.3 and Eastern
and Southern Europe with 59.0. The nations of Africa and Latin America on average offered
the least economic support, with index values of 37.9 and 41.6, respectively.
4. Empirical Findings
The panel estimation results have the relative strength of each market index is re-
gressed on its own lagged value, current and lagged levels of additional cases (per hundred
thousand of the population), current and lagged values of additional deaths (per hundred
thousand of the population) and that country’s Oxford index values for stringency and
economic support. As noted earlier, weekly data are employed in the regressions and the
results are broken down both by month and by geographical region.
10
Given that stronger
market performance equates to a lower market_RSI value, the expected signs on virus
cases and virus deaths is positive insofar as worse virus spread exerts a negative market
effect. The expected sign on stringency would also be positive given that negative effects on
economic activity should worsen the relative performance of the stock market. Finally, the
expected sign on econsupport would be negative if the additional funding boosts relative
market performance.
The full sample results in Table 2show increased cases to exert the expected overall
effect of worsening relative stock market performance. This effect is significant at the 95%
confidence level or better overall as well as for April, August and October individually.
Although there is some evidence of an offsetting effect of lagged cases, this may simply
reflect the market impact of a rise in cases subsiding over the following month. New
deaths are insignificant overall, but are significant and positive in May and August (while
significant and negative in April and September). Such variation over the sample is
unsurprising insofar as Phan and Narayan (2020) noted swings from negative to positive
effects even in a study limited to the early months of the pandemic alone. There are
similarly mixed findings for lagged deaths and no clear systematic effect.

J. Risk Financial Manag. 2021,14, 177 7 of 18
Table 2. Stock market regressions for all 80 countries.
VARIABLES March April May June July August
L.market_RSI
−0.502 *** −0.407 *** −0.234 *** −0.253 *** −0.259 *** −0.228 ***
(0.044) (0.055) (0.039) (0.064) (0.043) (0.050)
g_cases100k
−0.334 0.161 ** 0.074 0.220 0.115 0.174 ***
(0.289) (0.064) (0.094) (0.194) (0.127) (0.054)
L.g_cases100k
0.068 −0.220 *** −0.037 −0.296 0.127 0.140
(0.487) (0.051) (0.107) (0.242) (0.116) (0.168)
g_deaths100k
−3.122 −2.731 *** 6.880 *** 3.378 −1.557 2.776 *
(4.282) (0.651) (2.172) (3.498) (2.377) (1.526)
L.g_deaths100k
−7.161 2.931 *** −3.302 * 7.889 ** −3.632 ** 2.213 *
(8.475) (1.034) (1.666) (3.851) (1.495) (1.279)
stringency 0.161 *** −0.275 −0.025 0.117 0.640* −0.371
(0.060) (0.333) (0.161) (0.254) (0.381) (0.274)
econsupport
−0.144 * −0.015 0.484 * −0.058 0.104 0.106
(0.073) (0.142) (0.244) (0.420) (0.200) (0.588)
Constant 58.772 *** 81.153 *** 18.766 43.953 4.566 52.597
(2.870) (27.107) (16.711) (31.508) (26.160) (42.888)
Observations
320 320 400 320 400 320
R-squared 0.262 0.190 0.079 0.082 0.100 0.097
VARIABLES September October November December Overall
L.market_RSI
−0.365 *** −0.321 *** −0.187 *** −0.205 *** −0.095 ***
(0.040) (0.037) (0.038) (0.056) (0.016)
g_cases100k
0.110 0.107 *** 0.002 −0.003 0.030 **
(0.131) (0.039) (0.048) (0.011) (0.012)
L.g_cases100k
0.060 −0.094 −0.019 −0.030 ** −0.035 ***
(0.108) (0.075) (0.036) (0.011) (0.011)
g_deaths100k
−
18.080 ***
−2.859 −0.086 1.273 −0.627
(6.454) (2.196) (2.373) (1.715) (0.460)
L.g_deaths100k
−6.440 1.693 −0.449 −1.008 0.462
(8.296) (3.115) (2.157) (1.499) (0.478)
stringency −1.070 *** 0.158 0.190 −0.241 0.006
(0.236) (0.247) (0.310) (0.342) (0.019)
econsupport
−0.114 0.228 0.053 −0.612 *** −0.018
(0.169) (0.190) (0.200) (0.220) (0.014)
Constant 126.146 *** 29.175 * 36.922 104.391 *** 45.238 ***
(17.053) (16.411) (23.421) (25.052) (1.212)
Observations
320 400 320 400 4080
R-squared 0.193 0.136 0.061 0.059 0.013
Note: Robust standard errors are in parentheses; and *** p< 0.01; ** p< 0.05; * p< 0.1.
Stringency and econsupport are insignificant overall. This may reflect, in part, mea-
surement error in the available Oxford indices, which would, in turn, bias down the
estimated coefficients. Although stringency has the expected positive and significant effect
in March and July, the significant negative effect for September suggests a favorable impact
on relative market strength later in the year. Meanwhile, econsupport is significant with
the expected negative sign in March and December, but significant with the opposite sign
in May. With the major first and second waves of the virus in countries like the United
States occurring in or around those same March and December months, it may well be that
government support was seen as being more critical at those times; this could explain why
there appear to be favorable market reactions in those particular months.
Appendix BTables A4–A9 present results for the individual geographical groupings.
Although Australasia and North America were included in the full sample results reported
in Table 2, the number of countries in these two groups is insufficient for separate regression
analysis. In terms of the overall findings across the different groupings, increased cases are
significant with the expected positive sign for East Asia and Western Europe. Increased

J. Risk Financial Manag. 2021,14, 177 8 of 18
cases are insignificant overall in all other regions, with mixed significance observed in
individual months. New deaths are significant overall only for Western Europe, with a
negative sign. This counterintuitive finding seems to primarily derive from a strong effect
indicated for July, when deaths were generally trending down, and may simply reflect a
failure of markets to recover when the death rate improved. Elsewhere, there are generally
very mixed findings across individual months that leaves little clear pattern. However,
Africa has four months (March, April, September and December) for which deaths exert
significant positive effects, i.e., weakening stock market RSI.
Stringency is significant overall with the expected positive (weakening RSI) effect
only for Africa. Although it is otherwise significant overall just for East Asia, and with
a negative sign, in terms of individual months it is significant there with the expected
positive sign in March and only significant with a negative sign in September. In Eastern
and Southern Europe as well as Latin America, stringency is significant with the expected
positive sign in June and insignificant for other months. South Asia and Middle East also
evinces a significant positive effect of stringency in June, but with offsetting indicated
significant negative effects in May and September. Although the findings remain quite
mixed, there is a tendency for stringency measures to hurt stock market performance earlier
in the pandemic (as seen for March and June 2020).
Finally, economic support is never significant overall and the only clear case of benefi-
cial stock market effects is seen for Latin America. In this region, econsupport is negative
and significant in each of May, June and August (while positive and significant in July). In-
cluded here is the Brazilian support package of as much as USD 10 billion per month, more
than for any other developing nation, that helped Brazil’s GDP exceed its pre-pandemic
January 2020 levels by July—albeit it at the expense of potentially disastrous longer-term
fiscal consequences (see Magalhaes and Pearson 2020). The lack of more consistent overall
findings for economic support may reflect the fact that such support is usually applied
when times are darkest. If the such support is applied in the face of an already weakening
market, it will only appear successful on a statistical basis if it is able to immediately turn
this trend around. This will not always be the case, even if these same interventions do
help lay the groundwork for subsequent recovery.
5. Conclusions
Whereas the existing literature on the stock market effects of the pandemic has pri-
marily focused on just the early crisis period in the first quarter of 2020, this paper assesses
virus effects and government policy effects through December 2020 for a broad sample of
80 world stock markets. The overall results offer some support for growth in coronavirus
cases and stringency hurting stock market performance, together with some evidence
that 2020 government support measures helped the market both in March and late in the
year. However, there is considerable variation across the different geographical regions
considered in this paper. Clear-cut adverse effects of new coronavirus cases are found only
for East Asia and Western Europe, for example. There is more widespread evidence of
adverse effects associated with stringency, in overall terms for Africa and during mid-year
in Eastern and Southern Europe, Latin America and South Asia and Middle East. Economic
support’s favorable effects in March and December in the overall sample are generally not
mirrored over the different geographical groupings. However, there are several significant
months for the Latin American grouping around mid-2020.
The empirical findings are limited both by the total coverage being only ten months
and the impossibility of fully capturing the tumultuous events through the limited array
of variables included in the regressions. Nevertheless, markets are seen to generally
react to growth in cases in the expected fashion and there is some relatively widespread
evidence of adverse stringency effects on relative stock market performance. Although it
is possible that positive reactions to government support in March and December in the
overall sample may be driven by the US experience, there are also some pervasive effects
in mid-2020 evident elsewhere—especially in Latin America. More generally, it is clear

J. Risk Financial Manag. 2021,14, 177 9 of 18
that the substantial variation by region would make it unwise to draw too many general
conclusions from analysis focused more narrowly on either the United States alone or just
more advanced industrial countries. It is hoped that the wide-ranging sample covered in
this study can serve as a starting point in unpacking what has driven market performance
globally under the coronavirus pandemic. A similarly broad-based approach might be
employed in assessing the impact of the vaccine rollout in 2021, which began much more
rapidly in countries like Israel, the United Kingdom and the United States than in most of
the rest of the world.
Author Contributions:
Conceptualization, R.C.K.B.; methodology, R.C.K.B. and S.H.; software,
R.C.K.B. and S.H.; validation, R.C.K.B. and S.H.; formal analysis, S.H.; investigation, R.C.K.B. and
S.H.; resources, R.C.K.B. and S.H.; data curation, S.H.; writing—original draft preparation, R.C.K.B.;
writing—review and editing, R.C.K.B.; visualization, R.C.K.B. and S.H.; supervision, R.C.K.B.; project
administration, R.C.K.B.; funding acquisition, R.C.K.B. All authors have read and agreed to the
published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: N/A.
Informed Consent Statement: N/A.
Data Availability Statement:
The data presented in this study are available on request from the
corresponding author. The data are not publicly available due to the lack of a suitable depository.
Acknowledgments:
The authors thank Thomas Willett and two anonymous referees for helpful
comments and are grateful to the Lowe Institute at Claremont McKenna College for supporting this
project under the Lowe Faculty Student Research Program.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Table A1. National stock market indices.
Rank Country Market Index Index Ticker
1 United States S&P 500 SPX Index
2 China Shanghai Composite SHCOMP Index
3 Japan Nikkei 225 NKY Index
4 Hong Kong Hang Seng Index HIS Index
5 United Kingdom FTSE 100 UKX Index
6 France CAC 40 CAC Index
7 Saudi Arabia Tadawul All Share SASEIDX
8 Germany DAX DAX Index
9 Canada S&P/TSX Composite SPTSX Index
10 India Nifty 50 NSEI
11 Switzerland SMI SMI Index
12 South Korea KOPSI KOSPI Index
13 Taiwan Taiwain Weighted
Index TWSE Index
14 Australia S&P/ASX 200 AS51 Index
15 Sweden OMX Stockholm 30 OMX Index
16 Netherlands AEX AEX Index
17 Brazil Bovespa IBOV Index
18 Russia MOEX Russia IMOEX Index
19 Spain IBEX 35 IBEX Index
20 Italy Italy 40 FTSEMIB Index
21 Denmark OMX Copenhagen 20 OMXC20CP Index

J. Risk Financial Manag. 2021,14, 177 10 of 18
Table A1. Cont.
Rank Country Market Index Index Ticker
22 Thailand SET Index SET Index
23 Indonesia Jakarta SE Composite
Index JCI Index
24 Singapore MSCI Singapore
Index MXSG Index
25 Malaysia FTSE Malaysia KLCI FBMKLCI Index
26 Belgium BEL 20 BFX
27 South Africa South Africa Top 40 TOP40 Index
28 Mexico S&P BMV IPC MEXBOL Index
29 Finland OMX Helsinki 25 OMXH25GI Index
30 Norway OSE Benchmark OSEBX Index
31 Philippine PSEi Composite PCOMP Index
32 UAE DFM General Index DFMGI Index
33 Vietnam VN VNI
34 Turkey BIST 100 XU100
35 Israel TA 35 TA-35 Index
36 Chile S&P CLX IPSA IPSASD Index
37 Qatar DSM Index DSM Index
38 Poland WIG20 WIG20 Index
39 Austria ATX ATX Index
40 New Zealand NZX 50 NZSE50FG Index
41 Ireland ISEQ Overall ISEQ Index
42 Kuwait Kuwait All-Share
Index KWSEAS Index
43 Colombia COLCAP COLCAP Index
44 Peru S&P Lima General SPBLPGPP
45 Portugal PSI 20 PSI20 Index
46 Morocco MASI Free Float
All-Shares Index MOSENEW Index
47 Egypt EGX 30 EGX30 Index
48 Pakistan Karachi 100 KSE100 Index
49 Greece Athens General Stock
Index ASE Index
50 Argentina S&P Merval MERVAL Index
51 Nigeria NSE 30 NGSE30 Index
52 Hungary Budapest SE Index BUX Index
53 Czech Republic PX PX Index
54 Romania BET BET Index
55 Croatia CROBEX CRO Index
56 Kenya Nairobi SE All-Share
Index NSEASI Index
57 Bahrain Bahrain Bourse All
Share Index BHSEASI Index
58 Oman MSM 30 MSI
59 Bulgaria BSE SOFIX SOFIX Index
60 Jamaica JSE Market Index JMSMX Index
61 Trinidad
TT Market Composite
Index TTCOMP
62 Iceland OMX ICEX All Share
PI ICEXI Index
63 Slovenia Blue-Chip SBITOP SBITOP Index
64 Tunisia Tunindex TUSISE Index
65 Kazakhstan KASE KZKAK Index
66 Luxembourg LUXX LUXXX Index
67 Mauritius Semdex SEMDEX Index
68 Lebanon BLOM Index BLOM Index
69 Slovakia SAX SKSM Index
70 Lithuania OMX Vilnius Index VILSE Index
71 Cyprus Cyprus Main Market CYSMMAIN

J. Risk Financial Manag. 2021,14, 177 11 of 18
Table A1. Cont.
Rank Country Market Index Index Ticker
72 Botswana Botswana Gaborone
Index BGSMDC Index
73 Estonia Tallinn TR Index TALSE Index
74 Ghana Ghana SE Composite
Index GGSECI Index
75 Bermuda BSX Index BSX Index
76 Barbados BSE Market Index BARBL Index
77 Malawi Malawi Shares
Domestic Index MWSIDOM Index
78 Ukraine PFTS PFTS Index
79 Zambia Lusaka SE All Share
Index LUSEIDX
80 Latvia OMX Riga Index RIGSE Index
Table A2. Oxford University indicators and basis for stringency index.
Containment and closure
C1 School closing
C2 Workplace closing
C3 Cancel public events
C4 Restrictions on gathering size
C5 Close public transport
C6 Stay at home requirements
C7 Restrictions on internal movement
C8 Restrictions on international travel
Economic response
E1 Income support
E2 Debt/contract relief for households
E3 Fiscal measures
E4 Giving international support
Health systems
H1 Public information campaign
H2 Testing policy
H3 Contact tracing
H4 Emergency investment in healthcare
H5 Investment in COVID-19 vaccines
H6 Facial coverings
H7 Vaccination Policy
Overall indices
J. Risk Financial Manag. 2021, 14, x FOR PEER REVIEW 11 of 18
74 Ghana Ghana SE Composite Index GGSECI Index
75 Bermuda BSX Index BSX Index
76 Barbados BSE Market Index BARBL Index
77 Malawi Malawi Shares Domestic Index MWSIDOM Index
78 Ukraine PFTS PFTS Index
79 Zambia Lusaka SE All Share Index LUSEIDX
80 Latvia OMX Riga Index RIGSE Index
Table A2. Oxford University indicators and basis for stringency index.
Containment and closure
C1 School closing
C2 Workplace closing
C3 Cancel public events
C4 Restrictions on gathering size
C5 Close public transport
C6 Stay at home requirements
C7 Restrictions on internal movement
C8 Restrictions on international travel
Economic response
E1 Income support
E2 Debt/contract relief for households
E3 Fiscal measures
E4 Giving international support
Health systems
H1 Public information campaign
H2 Testing policy
H3 Contact tracing
H4 Emergency investment in healthcare
H5 Investment in COVID-19 vaccines
H6 Facial coverings
H7 Vaccination Policy
Overall indices
Source: Hale et al. (2020, pp. 4, 26).
Source: Hale et al. (2020, pp. 4, 26).

J. Risk Financial Manag. 2021,14, 177 12 of 18
Table A3. Unit root tests.
Harris and Tzavalis (1999)test statistics:
Ho Panels contain unit
roots Number of panels = 80
Ha Panels are stationary Number of periods = 52
AR parameter: Common Asymptotics: N ->
Infinity
Panel means: Included T Fixed
Time trend: Not included
Test Statistic z p-value
g_cases100k 0.9292 −2.1264 0.0167
g_deaths100k 0.9265 −2.5296 0.0057
econsupport 0.9299 −2.2308 0.0128
stringency 0.9161 −4.3313 0.0000
Appendix B
Table A4. Stock market regressions for Africa (11 countries).
VARIABLES March April May June July August
L.market_RSI −0.744 *** −0.447 ** −0.312 ** −0.260 −0.325 ** 0.082
(0.152) (0.172) (0.138) (0.201) (0.131) (0.257)
g_cases100k 13.718 −20.311 ** 1.529 0.130 −0.841 * 1.020
(8.696) (8.069) (4.515) (4.215) (0.401) (1.194)
L.g_cases100k −142.701 ** −8.377 * 1.622 1.039 0.469 −0.607
(55.279) (3.831) (5.176) (6.444) (0.783) (0.420)
g_deaths100k 480.140 *** 235.157 ** 86.765 32.621 35.143 15.499
(120.150) (90.527) (180.701) (56.367) (30.337) (69.784)
L.g_deaths100k −5412.173 ** 300.498 −472.027 ** −68.911 −5.399 −7.206
(1762.566) (226.469) (157.446) (49.951) (11.942) (30.337)
stringency 0.236 −0.745 0.221 0.409 −0.628 2.002
(0.172) (1.432) (0.466) (0.450) (1.750) (3.224)
econsupport 0.619 ** 0.199 1.348 ** 0.932 *** N.A. 1.608 ***
(0.201) (0.250) (0.504) (0.262) (0.356)
Constant 52.978 *** 128.909 −20.634 −31.262 96.370 −169.473
(8.476) (116.183) (49.962) (38.554) (96.676) (201.462)
Observations 44 44 55 44 55 44
R-squared 0.497 0.384 0.247 0.123 0.232 0.192
VARIABLES September October November December Overall
L.market_RSI −0.497 *** −0.353 *** −0.248 *** 0.223 −0.080 *
(0.082) (0.106) (0.074) (0.208) (0.044)
g_cases100k −0.864 *** −0.129 −0.229 −0.538 −0.077
(0.222) (0.253) (0.461) (0.416) (0.190)
L.g_cases100k 2.892 *** −0.441 *** −0.200 0.165 0.079
(0.510) (0.087) (0.403) (0.253) (0.182)
g_deaths100k 75.342 *** 0.205 −0.440 63.061 ** 6.573
(22.311) (10.781) (5.295) (27.461) (5.506)
L.g_deaths100k −113.612 *** −23.332 ** −5.711 −51.258 ** −4.990
(29.899) (7.574) (6.054) (19.772) (5.239)
stringency −0.224 0.723 −0.664 1.773 0.113 **
(0.335) (0.834) (1.975) (1.053) (0.043)
econsupport −0.044 −0.053 −0.321 ** N.A. 0.004
(0.175) (0.322) (0.109) (0.037)
Constant 65.039 ** 34.532 127.520 −58.046 39.884 ***
(24.355) (42.385) (115.204) (52.808) (3.485)
Observations 44 55 44 55 561
R-squared 0.456 0.220 0.204 0.198 0.026

J. Risk Financial Manag. 2021,14, 177 13 of 18
Table A5. Stock market regressions for East Asia (11 countries).
March April May June July August
L.market_RSI −0.530 * −0.536 *** −0.294 ** 0.110 −0.165 −0.299 **
(0.254) (0.097) (0.117) (0.140) (0.122) (0.097)
g_cases100k −4.509 −1.108 −0.701 −3.563 *** 1.280 * 0.995 *
(2.679) (0.941) (0.488) (1.087) (0.665) (0.520)
L.g_cases100k 3.252 2.896 −0.116 3.816 *** −0.122 −1.329 **
(2.168) (2.244) (0.583) (1.172) (1.088) (0.554)
g_deaths100k −116.483 −123.979 −109.811 476.655 110.931 24.827
(300.361) (106.059) (240.260) (415.277) (117.489) (83.461)
L.g_deaths100k −792.754 ** 226.992 253.016 267.141 −4.059 165.591
(303.131) (196.603) (305.944) (247.474) (75.972) (181.660)
stringency 0.572 *** −0.566 0.105 −0.467 0.904 3.805
(0.135) (0.475) (0.462) (0.490) (0.529) (3.612)
econsupport 0.164 0.070 0.247 N.A. 0.675 N.A.
(0.395) (0.305) (0.441) (0.496)
Constant 29.941 ** 84.926 ** 36.648 41.930 −47.830 −171.266
(11.627) (35.623) (32.772) (29.311) (34.670) (211.734)
Observations 44 44 55 44 55 44
R−squared 0.357 0.563 0.166 0.309 0.169 0.217
VARIABLES September October November December Overall
L.market_RSI −0.395 *** −0.409 *** −0.404 *** −0.130 −0.102 **
(0.069) (0.105) (0.093) (0.089) (0.045)
g_cases100k −1.271 3.423 ** 2.395 −0.037 0.393 **
(4.036) (1.138) (2.120) (2.640) (0.128)
L.g_cases100k −1.583 −7.571 *** −5.564 ** 7.108 ** −0.193
(2.304) (0.837) (2.488) (3.070) (0.109)
g_deaths100k 92.402 209.814 *** −267.268 * −34.593 18.023
(72.386) (26.199) (136.796) (121.723) (12.643)
L.g_deaths100k 20.187 379.406 *** −519.049 ** −283.198 ** −30.654 *
(57.935) (47.946) (230.209) (111.966) (14.542)
stringency −1.394 ** −0.192 −0.544 0.634 −0.120*
(0.470) (0.580) (0.319) (0.995) (0.064)
econsupport −0.838 * 0.075 −1.044 *** N.A. −0.013
(0.445) (0.272) (0.279) (0.031)
Constant 185.791 *** 30.843 207.107 *** −18.009 50.157 ***
(42.899) (17.288) (39.709) (60.508) (3.483)
Observations 44 55 44 55 561
R−squared 0.378 0.310 0.544 0.237 0.028
Table A6. Stock market regressions for Eastern/Southern Europe (15 countries).
VARIABLES March April May June July August
L.market_RSI −0.428 *** −0.468 *** −0.248 ** −0.218 −0.428 *** −0.335 ***
(0.099) (0.118) (0.112) (0.183) (0.140) (0.112)
g_cases100k −0.181 1.574 −0.834 3.445 ** −0.570 −1.938 *
(1.416) (0.942) (0.886) (1.261) (1.718) (0.960)
L.g_cases100k 0.620 −0.639 0.022 −1.218 −1.718 3.280 **
(1.678) (0.908) (0.573) (1.402) (2.570) (1.483)
g_deaths100k −15.519 −28.022 22.677 −19.849 29.426 −6.398
(24.565) (21.217) (26.760) (61.024) (77.903) (35.036)
L.g_deaths100k −656.247 ** 11.441 4.998 −38.381 22.175 −6.396
(254.080) (15.646) (24.329) (70.173) (50.650) (29.155)
stringency 0.202 0.716 0.205 1.188 ** 0.486 −2.348
(0.199) (0.928) (0.345) (0.412) (0.682) (1.681)
econsupport 0.109 −0.094 −0.213 ** −0.622 −0.326 −1.406 ***
(0.193) (0.346) (0.091) (0.411) (0.551) (0.399)
Constant 46.634 *** 4.741 46.776 ** 30.250 68.592 241.655 ***

J. Risk Financial Manag. 2021,14, 177 14 of 18
Table A6. Cont.
VARIABLES March April May June July August
(5.823) (72.788) (20.599) (42.737) (66.061) (43.420)
Observations 60 60 75 60 75 60
R-squared 0.299 0.299 0.105 0.244 0.227 0.290
VARIABLES September October November December Overall
L.market_RSI −0.363 ** −0.341 ** −0.068 −0.226 −0.087 **
(0.123) (0.121) (0.100) (0.137) (0.032)
g_cases100k 0.675 0.290 *** 0.004 0.042 0.054
(0.935) (0.080) (0.057) (0.042) (0.039)
L.g_cases100k −0.071 −0.635 ** 0.055 −0.093 −0.048
(1.039) (0.250) (0.067) (0.078) (0.040)
g_deaths100k 42.726 6.618 −8.207 0.702 −0.810
(36.303) (11.298) (4.938) (3.306) (2.483)
L.g_deaths100k −53.162 −2.505 5.420 −0.064 0.210
(31.399) (9.568) (5.000) (1.879) (2.215)
stringency 0.104 0.420 −0.178 0.195 −0.047
(0.857) (0.572) (0.769) (0.575) (0.039)
econsupport 0.338 0.729 0.441 ** 0.179 0.030
(0.349) (0.418) (0.180) (0.599) (0.026)
Constant 22.225 4.157 26.000 31.965 45.128 ***
(63.869) (37.693) (36.055) (59.332) (2.753)
Observations 60 75 60 75 765
R-squared 0.283 0.258 0.141 0.098 0.018
Table A7. Stock market regressions for Latin America (10 countries).
VARIABLES March April May June July August
L.market_RSI −0.670 *** −0.203 −0.266 *** −0.410 * −0.204 −0.162
(0.078) (0.226) (0.078) (0.206) (0.113) (0.184)
g_cases100k 10.573 −1.421 0.151 0.473 0.279 0.729 *
(11.344) (1.577) (0.527) (0.564) (0.857) (0.368)
L.g_cases100k −18.246 1.166 0.047 0.364 0.093 0.285
(36.549) (3.572) (0.605) (0.726) (0.470) (0.620)
g_deaths100k 97.397 23.488 12.702 0.780 −2.405 1.690
(191.826) (14.754) (47.466) (10.623) (2.878) (2.042)
L.g_deaths100k 1305.017 *** 15.234 −18.446 22.164 * −4.318 * 1.667
(299.437) (14.131) (46.832) (11.818) (1.917) (1.707)
stringency −0.167 0.117 −0.411 1.919 ** 1.623 −1.604
(0.266) (0.189) (0.644) (0.675) (1.082) (1.380)
econsupport 0.072 1.024 −1.503 *** −1.496 * 0.163 *** −0.738 *
(0.202) (0.840) (0.191) (0.763) (0.014) (0.361)
Constant 79.437 *** −7.653 159.103 ** −76.639 −82.996 137.262
(9.885) (38.750) (60.377) (65.775) (81.419) (75.742)
Observations 40 40 50 40 50 40
R-squared 0.404 0.186 0.108 0.419 0.153 0.153
VARIABLES September October November December Overall
L.market_RSI −0.276 ** −0.341 ** 0.027 −0.211 −0.065
(0.121) (0.107) (0.080) (0.164) (0.051)
g_cases100k −0.685 −0.900 ** 0.658 0.689 * −0.187
(0.553) (0.350) (0.378) (0.375) (0.133)
L.g_cases100k −0.424 0.560 −1.352 * 0.225 0.173
(0.378) (0.398) (0.666) (0.289) (0.133)

J. Risk Financial Manag. 2021,14, 177 15 of 18
Table A7. Cont.
VARIABLES September October November December Overall
g_deaths100k −16.887 ** −0.590 −1.091 −13.964 −0.380
(6.769) (3.509) (17.140) (11.587) (0.481)
L.g_deaths100k 10.618 −1.719 41.564 ** 26.028 −1.011
(13.972) (1.916) (17.820) (16.944) (0.554)
stringency 0.451 1.211 −0.641 −1.847 −0.018
(3.357) (1.438) (4.114) (1.238) (0.052)
econsupport N.A. 0.546 1.940 0.010 −0.020
(0.384) (1.178) (0.199) (0.048)
Constant 109.093 −36.296 −51.570 102.468 50.040 ***
(235.789) (90.164) (211.930) (90.198) (3.678)
Observations 40 50 40 50 510
R-squared 0.368 0.345 0.380 0.273 0.021
Table A8. Stock market regressions for South Asia and Middle East (12 countries).
VARIABLES March April May June July August
L.market_RSI −0.568 *** −0.346 ** −0.304 ** −0.393 ** −0.451 *** −0.373 **
(0.119) (0.125) (0.136) (0.146) (0.064) (0.146)
g_cases100k −1.833 *** 0.307 −0.116 0.206 0.036 −0.095
(0.311) (0.228) (0.089) (0.276) (0.170) (0.304)
L.g_cases100k −6.225 *** −0.500 0.079 −0.414 0.078 −0.003
(1.282) (0.530) (0.097) (0.337) (0.110) (0.274)
g_deaths100k 301.004 *** −43.091 51.445 *** 29.386 −18.239 * 41.388 *
(93.478) (85.319) (10.123) (29.490) (8.716) (21.533)
L.g_deaths100k −304.246 16.517 −23.748 −15.818 −1.660 21.193
(370.710) (47.538) (16.573) (22.477) (7.496) (32.716)
stringency 0.239 1.535 −2.735 *** 5.126 *** −0.288 −0.470
(0.187) (1.493) (0.549) (1.428) (0.623) (0.448)
econsupport −0.153 0.017 1.214 *** N.A. 0.205 2.043 ***
(0.489) (0.398) (0.312) (0.181) (0.310)
Constant 60.284 *** −74.888 211.773 *** −322.243 ** 68.880 −77.688 **
(9.090) (139.403) (52.258) (110.870) (50.708) (28.648)
Observations 48 48 60 48 60 48
R-squared 0.391 0.139 0.338 0.243 0.239 0.232
VARIABLES September October November December Overall
L.market_RSI −0.481 *** −0.376 *** −0.147 −0.177 −0.091 **
(0.136) (0.092) (0.152) (0.228) (0.038)
g_cases100k 0.578 *** 0.085 0.316 −0.008 0.003
(0.171) (0.134) (0.210) (0.011) (0.012)
L.g_cases100k −0.256 ** −0.216 ** −0.426 −0.028 *** −0.029 **
(0.105) (0.089) (0.340) (0.009) (0.010)
g_deaths100k −55.666 4.572 8.720 −24.143 7.230
(46.227) (19.869) (9.381) (16.088) (5.575)
L.g_deaths100k 11.466 −12.941 −2.006 −3.493 −7.233 *
(48.205) (26.220) (8.550) (25.960) (3.677)
stringency −1.780 *** 0.828 0.119 −0.874 0.020
(0.349) (0.553) (0.221) (0.546) (0.036)
econsupport N.A. −0.267 −0.322 ** −0.584* 0.038
(0.472) (0.126) (0.288) (0.041)
Constant 159.008 *** 36.473 ** 70.664 *** 153.390 *** 42.441 ***
(17.888) (15.050) (20.417) (24.035) (2.432)
Observations 48 60 48 60 612
R-squared 0.484 0.230 0.208 0.168 0.018

J. Risk Financial Manag. 2021,14, 177 16 of 18
Table A9. Stock market regressions for Western Europe (17 countries).
VARIABLES March April May June July August
L.market_RSI −0.498 *** −0.483 *** −0.117 −0.319 *** −0.107 −0.379 ***
(0.096) (0.140) (0.074) (0.109) (0.076) (0.039)
g_cases100k −0.114 0.147 1.694 5.975 ** 1.064 *** 0.136 **
(0.265) (0.091) (1.237) (2.200) (0.346) (0.059)
L.g_cases100k 0.025 −0.135 * −0.880 −4.796 *** 0.033 −0.253
(0.420) (0.073) (0.835) (1.393) (0.167) (0.325)
g_deaths100k −0.891 −3.102 *** 7.139 *** −6.858 −114.578 *** 10.443
(3.503) (0.698) (2.252) (6.785) (30.085) (7.862)
L.g_deaths100k −12.493 * 3.020 ** −3.941 2.587 28.716 35.886
(7.105) (1.257) (5.521) (4.790) (34.079) (33.406)
stringency −0.207 −1.897 −0.459 −0.571 0.229 0.408
(0.154) (1.118) (0.452) (0.341) (0.541) (0.344)
econsupport −0.158 −0.554 *** 1.402 ** N.A. −0.272 N.A.
(0.102) (0.099) (0.561) (0.302)
Constant 85.220 *** 250.937 ** −45.938 74.225 *** 46.982 26.006
(6.609) (87.328) (35.082) (22.815) (29.883) (16.776)
Observations 68 68 85 68 85 68
R-squared 0.457 0.449 0.109 0.222 0.233 0.306
VARIABLES September October November December Overall
L.market_RSI −0.410 *** −0.214 *** −0.163 * −0.396 *** −0.160 ***
(0.085) (0.061) (0.085) (0.108) (0.029)
g_cases100k 0.004 0.080 −0.052 −0.043 0.051 ***
(0.317) (0.048) (0.055) (0.045) (0.009)
L.g_cases100k 0.176 ** −0.034 −0.026 0.021 −0.059 ***
(0.061) (0.108) (0.053) (0.066) (0.015)
g_deaths100k −8.524 −3.357 4.042 * 3.119 −1.251 **
(17.846) (4.885) (2.225) (4.030) (0.544)
L.g_deaths100k 6.475 19.665 *** −3.714 * −4.854 * 1.861 ***
(17.787) (6.074) (1.775) (2.395) (0.378)
stringency 0.173 0.121 0.829 −0.863 * −0.025
(0.948) (0.483) (1.213) (0.476) (0.056)
econsupport −0.214 −0.267 −0.075 −0.886 *** −0.060
(0.407) (0.390) (0.479) (0.103) (0.041)
Constant 62.757 44.764 5.512 189.460 *** 49.459 ***
(55.013) (35.564) (105.188) (28.876) (2.153)
Observations 68 85 68 85 867
R-squared 0.183 0.311 0.125 0.196 0.045
Note to Appendix BTables A4–A9: Robust standard errors are in parentheses; and *** p< 0.01; ** p< 0.05; * p< 0.1.
Notes
1
This is itself consistent with the past experiences considered by Friedman (2005), who links the US stock market
recovery from the post-1999 crash to rapid Federal Reserve monetary expansion and contrasts this with the effects of
stagnant money supply in post-1989 Japan and monetary contraction in the post-1929 US case. Meanwhile, as with
the late 1990s Nasdaq bubble, the effects of US monetary expansion in 2020–2021 may well have been channeled
primarily into the stock market (whereas goods prices remained subdued).
2
An especially telling fact is Velde (2020) observation that, by the January 1919 issue of the Federal Reserve Bulletin,
the Spanish Flu pandemic was no longer meriting even a mention in the Federal Reserve’s main publication. The
contrast with the aftermath of the coronavirus pandemic in 2021 could not be more stark.
3
The onset of such high volatility regimes can itself produce significant sectoral effects, as seen in Burdekin and Tao’s
(2021) comparative Markov-switching analysis of gold’s hedging value in 2020 vs. 2008–2009.
4
A further factor considered by Ashraf (2021) is the potential for stronger virus-related stock market effects in countries
with higher uncertainty avoidance (as proxied by survey data from employees and middle-managers of sampled
firms).

J. Risk Financial Manag. 2021,14, 177 17 of 18
5
Similarly, whereas contagion often emerged during past European crises, Bo¸toc and Anton (2020) find that this
typically proved to to be a short-lived phenomenon and was not necessarily indicative of greater longer-run
cointegration.
6
Further evidence on the differential stock market reactions around this time is provided by Mazur et al. (2021), who,
not surprisingly, find widespread sectoral variations.
7
Not only does experience from past pandemics suggest considerable risks of post-pandemic inflation owing to
pent-up consumer demand (Burdekin 2020a), but also such dangers have likely been greatly understated by Modern
Monetary Theory proponents (Bird et al. 2021).
8Few countries outside of China exhibited meaningful virus case numbers and deaths prior to March.
9
Dollar returns, rather than returns in local currency, allows for more of an apples-to-apples comparison as the gains
in value of a dollar invested in the US market are set against the gains realized from that same dollar invested abroad.
10
Stationarity of the variables entered in the regressions is confirmed by application of the Harris and Tzavalis (1999)
unit root test. This test is appliable to cases where the number of panels is large relative to the number of time
periods. The results reject the presence of a unit root at better than the 98% confidence level or better in each case
(Appendix ATable A3).
References
Al-Awadhi, Abdullah Khaled Alsaifi, Ahmad Al-Awadhi, and Salah Alhammadi. 2020. Death and contagious infectious diseases:
Impact of the COVID-19 virus on stock market returns. Journal of Behavioral and Experimental Finance 27: 100326. [CrossRef]
[PubMed]
Alber, Nader. 2020. The Effect of Coronavirus Spread on Stock Markets: The Case of the Worst 6 Countries. Available online:
https://ssrn.com/abstract=3578080 (accessed on 10 April 2021).
Ashraf, Badar Nadeem. 2020. Stock markets’ reaction to COVID-19: Cases or fatalities? Research in International Business and Finance 54:
101249. [CrossRef]
Ashraf, Badar Nadeem. 2021. Stock markets’ reaction to COVID-19: Moderating role of national culture. Finance Research Letters.
forthcoming.
Baker, Scott R., Nicholas Bloom, Steven J. Davis, Kyle J. Kost, Marco C. Sammon, and Tasaneeya Viratyosin. 2020. The Unprecedented
Stock Market Impact of COVID-19. Working Paper 26945. Cambridge, MA: National Bureau of Economic Research.
Bird, Graham, Eric Pentecost, and Thomas Willett. 2021. Modern Monetary Theory and the policy response to COVID-19: Old wine in
new bottles. World Economy. forthcoming.
Bo¸toc, Claudio, and Sorin Gabriel Anton. 2020. New empirical evidence on CEE’s stock markets integration. World Economy 43:
2785–802. [CrossRef]
Burdekin, Richard C. K. 2020a. The money explosion of 2020, monetarism and inflation: Plagued by history? Modern Economy 11:
1887–900. [CrossRef]
Burdekin, Richard C. K. 2020b. Economic and financial effects of the 1918–1919 Spanish Flu pandemic. Journal of Infectious Diseases &
Therapy 8: 1000439.
Burdekin, Richard C. K. 2021. Death and the stock market: International evidence from the Spanish Flu. Applied Economics Letters.
forthcoming.
Burdekin, Richard C. K., and Ran Tao. 2021. The golden hedge: From global financial crisis to global pandemic. Economic Modelling 95:
170–80. [CrossRef]
Chan-Lau, Jorge A., and Yunhui Zhao. 2020. Hang in There: Stock Market Reactions to Withdrawals of COVID-19 Stimulus Measures.
Working Paper 20/285. Washington, DC: International Monetary Fund.
Cox, Josue, Daniel L. Greenwald, and Sydney C. Ludvigson. 2020. What Explains the COVID-19 Stock Market? Working Paper 27784.
Cambridge, MA: National Bureau of Economic Research.
Friedman, Milton. 2005. A natural experiment in monetary policy covering three episodes of growth and decline in the economy and
the stock market. Journal of Economic Perspectives 19: 145–50. [CrossRef]
Greenwood, John, and Steve H. Hanke. 2021. The money boom is already here. Available online: https://www.wsj.com/articles/the-
money-boom-is-already-here-11613944730 (accessed on 10 April 2021).
Hale, Thomas, Noam Angrist, Thomas Boby, Emily Cameron-Blake, Laura Hallas, Beatriz Kira, Saptarshi Majumdar, Anna Petherick,
Toby Phillips, Helen Tatlow, and et al. 2020. Variation in Government Responses to COVID-19. Working Paper 2020/032. Oxford,
UK: Blavatnik School of Government, Oxford University.
Harjoto, Maretno Agus, Fabrizio Rossi, and John K. Paglia. 2021. COVID-19: Stock market reactions to the shock and the stimulus.
Applied Economics Letters. forthcoming.
Harris, Richard D. F., and Elias Tzavalis. 1999. Inference for unit roots in dynamic panels where the time dimension is fixed. Journal of
Econometrics 91: 201–26. [CrossRef]
Just, Malgorzata, and Krzystof Echaust. 2020. Stock market returns, volatility, correlation and liquidity during the COVID-19 crisis:
Evidence from the Markov switching approach. Finance Research Letters 37: 101775. [CrossRef] [PubMed]

J. Risk Financial Manag. 2021,14, 177 18 of 18
Khan, Karamat, Huawei Zhao, Han Zhang, Huilin Yang, Muhammad Haroon Shah, and Atif Jahanger. 2020. The impact of COVID-19
pandemic on stock markets: An empirical analysis of world major stock indices. Journal of Asian Finance, Economics, and Business 7:
463–74. [CrossRef]
Magalhaes, Luciana, and Samantha Pearson. 2020. Brazil’s president promised economic change: Now he is writing Covid
−
19 relief
checks. Available online: https://www.wsj.com/articles/brazils-president-promised-economic-change-now-he-is- writing-
covid-19-relief-checks-11605186008 (accessed on 10 April 2021).
Malnick, Edward. 2020. Boris Johnson exclusive interview: We will not need another national lockdown. Available online: https:
//www.telegraph.co.uk/politics/2020/07/18/boris-johnson- exclusive-interview- will-not- need-another-national/ (accessed on
10 April 2021).
Mazur, Mieszko, Man Dang, and Miguel Vega. 2021. COVID-19 and the March 2020 stock market crash. Evidence from S&P1500.
Finance Research Letters 38: 101690. [PubMed]
Okorie, David Iheke, and Boqiang Lin. 2021. Stock markets and the COVID-19 fractal contagion effects. Finance Research Letters 38:
101640. [CrossRef] [PubMed]
Phan, Dinh Hoang Bach, and Paresh Kumar Narayan. 2020. Country responses and the reaction of the stock market to COVID-19—A
preliminary exposition. Emerging Markets Finance and Trade 56: 2138–50. [CrossRef]
Rahman, Md Lutfur, Abu Amin, and Mohammed Abdullah Al Mamun. 2021. The COVID-19 outbreak and stock market reactions:
Evidence from Australia. Finance Research Letters 38: 101832. [CrossRef]
Testa, Christian C., Nancy Krieger, Jarvis T. Chen, and William P. Hanage. 2020. Visualizing the Lagged Connection between COVID-19
Cases and Deaths in the United States: An Animation Using Per Capita State-Level Data (January 22, 2020–July 8, 2020). Working Paper
19(4). Cambridge, MA: Harvard Center for Population and Development Studies.
Velde, François R. 2020. What Happened to the US Economy during the 1918 Influenza Pandemic? A View through High-Frequency
Data. WP 2020-11, Federal Reserve Bank of Chicago. Available online: https://www.chicagofed.org/publications/working-
papers/2020/2020-11 (accessed on 10 April 2021).