COVID-19, government policy responses, and stock market liquidity around the world: A note

Res Int Bus Finance.

2021 Apr; 56: 101359.





PMID: 33343055

COVID-19, government policy responses, and stock market liquidity around the world: A note

,a,b,1,* ,c ,e,f ,d and b

Adam Zaremba

aMontpellier Business School, 2300 Avenue des Moulins, 34185, Montpellier, France

bDepartment of Investment and Financial Markets, Institute of Finance, Poznan University of Economics and Business, al. Niepodległości 10, 61-875, Poznań, Poland

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David Y. Aharon

cFaculty of Business Administration, Ono Academic College, Tzahal St 104, Kiryat Ono, Israel

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Ender Demir

eUniversity of Social Sciences, Lodz, Poland

fIstanbul Medeniyet University, Dumlupınar D100 Karayolu No:98, 34720, Kadıköy, İstanbul, Turkey

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Renatas Kizys

dDepartment of Banking and Finance, Southampton Business School, University of Southampton, Room 1013, Building 4, Highfield Campus, Southampton, SO17 1BJ, United Kingdom

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Dariusz Zawadka

bDepartment of Investment and Financial Markets, Institute of Finance, Poznan University of Economics and Business, al. Niepodległości 10, 61-875, Poznań, Poland

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Author information Article notes Copyright and License information Disclaimer

aMontpellier Business School, 2300 Avenue des Moulins, 34185, Montpellier, France

bDepartment of Investment and Financial Markets, Institute of Finance, Poznan University of Economics and Business, al. Niepodległości 10, 61-875, Poznań, Poland

cFaculty of Business Administration, Ono Academic College, Tzahal St 104, Kiryat Ono, Israel

dDepartment of Banking and Finance, Southampton Business School, University of Southampton, Room 1013, Building 4, Highfield Campus, Southampton, SO17 1BJ, United Kingdom

eUniversity of Social Sciences, Lodz, Poland

fIstanbul Medeniyet University, Dumlupınar D100 Karayolu No:98, 34720, Kadıköy, İstanbul, Turkey

⁎Corresponding author at: Montpellier Business School, 2300 Avenue des Moulins, 34185, Montpellier, France.

1Adam Zaremba acknowledges the support of the National Science Centre of Poland (Grant No. 2016/23/B/HS4/00731).

Copyright © 2020 Elsevier B.V. All rights reserved.

Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company’s public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre – including this research content – immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.


Unprecedented non-pharmaceutical interventions targeted to curb the spread of COVID-19 exerted a dramatic impact on the global economy and financial markets. This study is the first attempt to investigate the influence of these government policy responses on global stock market liquidity. To this end, we examine daily data from 49 countries for the period January-April 2020. We demonstrate that the impact of the interventions is limited in scale and scope. Workplace and school closures deteriorate liquidity in emerging markets, while information campaigns on the novel coronavirus facilitate trading activity.


Novel coronavirus, COVID-19, Stock market liquidity, Turnover ratio, Non-pharmaceutical interventions, Government policy responses, International financial markets

3. Data and methods

We base our study on 49 country equity markets classified as developed or emerging by MSCI (see
for details).
To avoid arbitrariness, our study period begins on the first day when the World Health Organization (WHO) was informed about the unknown cluster of pneumonia in Wuhan, China (WHO, 2020), so our sample runs from 1 January 2020 to 3 April 2020.
We source all the market data from Datastream and calculate the market-related variables (total returns, trading volumes, market capitalizations, and valuation ratios) based on the Datastream Global Equity Indices. These indices represent value-weighted portfolios that cover the majority of tradeable stocks in a market, and are the prime choice in the country-level asset pricing literature (Umutlu, 2015, 2019; Zaremba, 2019).

Table 1

TURNPR1PR2PR3PR4PR5PR6PR7Developed marketsAustralia0.41005045045Austria0.1816161716301520Belgium0.201515190421223Canada0.371130018052Denmark0.271615211917024Finland0.3226160231013France0.29241440291351Germany0.00153240272326Greece0.2116019027814Hong Kong0.274933490653965Ireland0.27112086000Israel0.2516152214381443Italy0.5521202311461752Japan0.482428300412760the Netherlands0.391515170171413New Zealand0.1461015725744Norway0.2616017010015Portugal0.301615012501218Singapore0.22000066066South Korea0.5030031053945Spain0.261501515452019Sweden0.350016020012Switzerland0.39161426024916United Kingdom0.2910141404290United States0.97000014363Emerging marketsArgentina0.0412412547815Brazil0.57000025016Chile0.0915150014013China0.6250120053029Colombia0.04142813028810Czechia0.091701810211520Egypt0.10110121291212Hungary0.21175181424820India0.3011713929947Indonesia0.09151450541556Malaysia0.121515170561347Mexico0.11119509824Pakistan0.08817136121Peru0.02015170221721Philippines0.050000000Poland0.21160190271320Qatar0.0518132015511149Russia0.24105140151145Saudi Arabia0.15200151148815South Africa0.341215156251552Taiwan0.3318019045038Thailand0.421200021620Turkey0.9815101512411551UAE0.05205150101052Open in a separate window

We proxy stock market liquidity with a turnover ratio (Datar et al., 1998), which is available at a daily frequency. Contrary to other popular measures, such as Amihud’s (2002) ratio or the number of zero-return days (Lesmond et al., 1999), it does not require backward-looking trailing data, so we can monitor day-to-day changes in liquidity. Furthermore, it could be easily aggregated at the country level even in emerging markets, where timely and reliable data on bid-ask spreads (Chung and Zhang, 2014) may be scarce. We calculate the turnover ratio for country i on day t (TURNi,t) as the daily trading volume (Vi,t) expressed in currency terms on all the stocks included in the index over the total market capitalization (MCi,t) of the index portfolio:



To account for different government policy responses to COVID-19, we rely on data from the Oxford COVID-19 Government Response Tracker.
In particular, we use seven different measures of government policies that sought to curb the outbreak of the pandemic: school closing (PR1), workplace closing (PR2), canceled public events (PR3), closed public transport (PR4), public information campaigns (PR5), restrictions on internal movement (PR6), and international travel controls (PR7). The measures take on a positive value if the government launches a particular measure and take on zero otherwise (Hale et al., 2020, downloaded on 12 April 2020).
In the baseline approach, we consider measures that are applied to an entire country, and not only to targeted regions. Also, the measures used in our study are either formal regulatory (“hard measures”) or government recommendations (“soft measures”). provides an overview of all the countries included in the sample, along with the number of days when different policies were in place, and the average turnover ratio.

To examine the role of policy responses on stock market liquidity, we run the following panel regression:



where PRj,i,t denotes the variables representing different policy responses PR1–PR7 for country i on day t, Kc,i,t indicates a set of additional control variables, αi and γt are country and time-fixed effects, εi,t is the error term, and βj and βc are the regression coefficients. To examine the coefficient significance, we estimate two-way fixed effects cluster-robust standard errors, which are robust to general heteroscedasticity and correlation across markets or across times (Cameron et al., 2011; Thompson, 2011).

Following the general approach established in earlier studies of stock market liquidity (Scharnowski, 2020; Qadan and Aharon, 2019; Chordia et al., 2001), we add several control variables. Specifically, we control for contemporaneous and lagged market returns (Rt, Rt-1), contemporaneous volatility measured as the absolute return on day t (AbsRt), past volatility estimated as the average absolute return through trading days t-1 to t-5 (VOLt-1), market capitalization (MVt-1), market-wide price-to-earnings ratio (PEt-1), and weekday dummies for the day of the week effect. Finally, integrate out the influence of the pandemic from government policies, we control for the total number of coronavirus-related cases and deaths (INFt, DTHt) obtained from the European Centre for Disease Prevention and Control.
Notably, due to the international nature of our dataset, we are not able to incorporate country-specific variables available only for a few developed markets, such as implied-volatility indices or credit spreads.
presents the basic statistical properties of the variables in this research.

Table 2

MeanStandard deviationSkewnessKurtosisMinimumFirst quartileMedianThird quartileMaximumTURN0.2740.2561.8744.5710.0010.0990.2020.3651.902INF2485.57612762.3447.72775.0340.0000.0001.000117.000216721.000DTH103.304695.65310.895148.3770.0000.0000.0001.00013157.000R−0.0040.027−1.1205.667−0.174−0.011−0.0010.0070.122AbsR0.0170.0212.5938.4310.0000.0040.0090.0200.174VOL0.0150.0151.6943.1840.0000.0050.0090.0220.107MV320.178269.5353.32914.95266.223162.384258.882387.3431861.960PE14.7624.5930.216−0.2213.83911.50814.63717.82528.803PR10.2170.4121.376−0.1060.0000.0000.0000.0001.000PR20.1330.3402.1582.6590.0000.0000.0000.0001.000PR30.2100.4081.4210.0200.0000.0000.0000.0001.000PR40.0610.2393.68411.5820.0000.0000.0000.0001.000PR50.4740.4990.105−1.9900.0000.0000.0001.0001.000PR60.1440.3512.0262.1070.0000.0000.0000.0001.000PR70.4640.4990.145−1.9800.0000.0000.0001.0001.000Open in a separate window

Specifically, as we consider all the variables simultaneously, we can isolate the role of non-pharmaceutical interventions from the impact of the coronavirus pandemic or the market crash. Also, many policy measures were implemented simultaneously or within short periods. By considering them jointly, we extract the influence of each type of government action.

4. Results

reports the results of the panel regressions applied to the full set of countries in the sample. The estimated effects of the majority of the policy responses are insignificant. The only exception is public information campaigns, which exert a positive and significant effect on liquidity. When we control for all the policy responses simultaneously (specification [8]), the ensuing improvement in the daily turnover ratio (expressed in percentage terms) is estimated at 0.027 percentage points (t-stat = 2.36), which is equivalent to approximately 6.7 percentage points on an annualized basis. The impact of the information campaigns could be understood relatively intuitively. Spreading information about the COVID-19 development may facilitate the pricing of a negative news about future states of the economy in the stock market. This, in turn, may induce a widespread portfolio repositioning. Also, investors may be prone to rebalance their portfolios towards safer assets. All these may result in additional trading, which enhances stock market liquidity.

Table 3

(1)(2)(3)(4)(5)(6)(7)(8)PR1t−0.002−0.016(-1.08)(-0.73)PR2 t−0.026−0.020(-1.07)(-0.75)PR3 t0.0010.015(0.05)(0.71)PR4 t−0.034*−0.026(-1.83)(-1.11)PR5 t0.023**0.027**(2.18)(2.36)PR6 t−0.017−0.004(-0.82)(-0.17)PR7 t0.003−0.006(0.22)(-0.46)INFt0.0000.0000.0000.0000.0000.0000.0000.000(0.83)(0.68)(0.76)(0.63)(0.63)(0.71)(0.81)(0.50)DTH t0.0000.0000.0000.0000.0000.0000.0000.000(-1.46)(-1.25)(-1.40)(-0.90)(-1.47)(-1.12)(-1.68)(-0.60)R t−0.789***−0.798***−0.791***−0.790***−0.747***−0.779***−0.788***−0.730***(-4.03)(-4.10)(-4.03)(-4.06)(-3.80)(-3.95)(-4.01)(-3.78)R t-1−0.826***−0.833***−0.870***−0.831***−0.862***−0.837***−0.870***−0.781***(-3.11)(-3.27)(-3.36)(-3.27)(-3.40)(-3.30)(-3.39)(-3.09)AbsR t1.947***1.951***1.982***1.945***1.989***1.980***1.983***1.929***(6.22)(6.41)(6.39)(6.41)(6.49)(6.49)(6.43)(6.27)VOL t-13.329***3.285***3.351***3.294***3.296***3.337***3.346***3.233***(3.42)(3.39)(3.40)(3.32)(3.33)(3.37)(3.38)(3.35)MV t-10.1470.1950.2440.1890.2970.2010.2500.190(0.55)(0.74)(0.90)(0.68)(1.10)(0.73)(0.91)(0.73)PE t-1−0.020**−0.021**−0.019**−0.020**−0.018**−0.020**−0.019**−0.019**(-2.37)(-2.45)(-2.32)(-2.46)(-2.29)(-2.42)(-2.36)(-2.31)Adj. R20.5210.5210.5190.5210.5210.5200.5190.525F-stat0.0000.0000.0000.0000.0000.0000.0000.000Open in a separate window

So far, we have performed our tests on the whole sample. In addition, we ask if our research findings hold for developed and emerging markets separately. The premise for this split is the difference in technological advances in financial markets and trading between the two groups. Whereas in the developed markets trading is largely automated and conducted electronically, in some emerging markets such technological infrastructure may be more limited, and the role of proprietary trading may be larger. In consequence, factors such as closing workplaces may affect trading activity more severely.

summarizes the impact of government restrictions separately in the developed and emerging markets (classified according to MSCI standards). The dissimilarities are clearly noticeable. In the developed countries, no influence of the policy responses is recorded—none of the coefficients on PR1 to PR7 depart markedly from zero. On the other hand, the picture for the emerging markets is very different. The effect of information campaigns is even more pronounced, resulting in a 0.04 percentage points (t-stat = 2.89) increase in the turnover ratio per day. Moreover, we also record a negative effect of workplace and school closings. When considered jointly with other measures, these two factors lead to a decline in the market turnover ratio by -0.041 (t-stat = -3.05) and -0.050 percentage points (t-stat = -2.66), respectively. This corresponds with a 10–13 % drop in the turnover ratio on an annualized basis.

Table 4

(1)(2)(3)(4)(5)(6)(7)(8)Panel A: Developed marketsPR1t−0.0010.012(-0.04)(0.36)PR2 t−0.0010.008(-0.03)(0.16)PR3 t−0.0010.004(-0.02)(0.13)PR4 t−0.029−0.027(-1.03)(-0.82)PR5 t0.0130.021(0.72)(1.14)PR6 t−0.021−0.023(-0.62)(-0.65)PR7 t−0.014−0.020(-0.61)(-0.89)Adj. R20.6210.6210.6210.6220.6210.6220.6210.623F-stat0.0000.0000.0000.0000.0000.0000.0000.000Panel B: Emerging marketsPR1t−0.053***−0.050***(-2.66)(-2.66)PR2 t−0.043**−0.041***(-2.42)(-3.05)PR3 t−0.0060.009(-0.29)(0.68)PR4 t−0.038*−0.029(-1.69)(-1.42)PR5 t0.033***0.040***(2.72)(2.89)PR6 t−0.0100.029*(-0.52)(1.87)PR7 t0.012−0.002(0.81)(-0.18)Adj. R20.4240.4180.4100.4140.4180.4110.4110.623F-stat0.0000.0000.0000.0000.0000.0000.0000.00Open in a separate window

The observations on the detrimental effect of the workplace and school closures are in line with our expectations, and they contrast with the positive effect of information campaigns. The workplace closures could lead to challenges in the investment decision-making process and undermine the proprietary trading possibilities, thus damaging market liquidity. The adverse effect of school closures could lead to similar consequences indirectly, via increased work absenteeism (Chen et al., 2011; Viner et al., 2020) or by signaling the forthcoming introduction of stricter measures (Lindzon, 2020). It is also important to note that when such interventions were imposed at the beginning of the COVID-19 outbreak, adjustment to the practice and ability of working from home was slow and costly. As indicated above, in emerging economies such an adaptation is constrained by the limited technological infrastructure, and may be slow. Furthermore, school closures may also signal disruptions of future household income, which reduces the incentive to buy risky assets (Epstein et al., 2009).

Other phenomena that may explain the negative impact of workplace and school closures, may be due to irrational behavioral motives that are likely to be more pronounced in emerging markets. These motives include the tendency of ignorance of bad news, demonstrated by the “ostrich effect” (Galai and Sade, 2006), the “information overload” effect (Agnew and Szykman, 2005), the negative effect of bad experience (Thaler and Johnson, 1990), and the disposition effect (Shefrin and Statman, 1985), which refers to the reluctance of investors to realize losses and to holding loser stocks for too long. All these potential behavioral drivers may lead to lower levels of market liquidity.

5. Further robustness checks

To assure the reliability of our findings, we perform a battery of robustness tests. First, we experiment with earlier starting points for the study period. Second, we use alternative definitions of the policy response measures. In our baseline approach, we use the recommended and required actions jointly. For example, considering school closures, our binary variable takes the value of 1 when a government either requires or recommends the closure. Alternatively, in these robustness checks, we take into account only the required actions. Third, we modify the selection and construction of the control variables. For instance, we a) discard the weekday dummies or the numbers of COVID-19 infections or deaths, and b) use raw market capitalization instead of its natural logarithm. Fourth, we employ alternative estimation methods including the random-effects model. We also examine the natural logarithm (in lieu of the level) of turnover. Importantly, these checks do not qualitatively change our findings: the information campaigns facilitate market liquidity, while other policies do not exert a reliable impact. For the sake of brevity, these outcomes are not reported in detail, but they are available from the authors upon request.

6. Concluding remarks

The study examines the influence of government policy responses to the COVID-19 pandemic. Having considered implementations of policy actions in 49 countries, we demonstrate that the effect of policy responses is rather small and limited in scope. Workplace and school closures may limit stock market liquidity, while public information campaigns facilitate additional trading. All these effects, however, are driven solely by emerging markets and play no role in developed countries.

Our study has explicit policy implications. It highlights that governments need to be aware that in addition to a vast detrimental economic impact, the COVID-19-related restrictions may adversely influence the trading environment in financial markets. Specifically, our results should encourage governments to engage in public information campaigns, which are instrumental in greater trading activity and, consequently, a lower cost of equity capital.

The prime limitation of this research is the limited dataset. Longer study periods and the use of richer (e.g., intraday) datasets would enable the employment of alternative liquidity proxies. Future changes and progress in policy responses will allow for the evaluation and verification of our findings. Furthermore, there were possibly some other important phenomena or variables that influenced trading activity, but we were unable to fully capture them. Increased margin requirements or short-selling restrictions in several developed markets (Unsted, 2020) may serve as an example.

Future studies on the issues discussed in this paper could be extended into at least two directions. First, it would be valuable to explore the developments in other asset classes, such as corporate or Treasury bonds. Second, it would be interesting to examine the impact of policy changes during the second wave of coronavirus and its influence on stock market liquidity.

CRediT authorship contribution statement

Adam Zaremba: Conceptualization, Investigation, Methodology, Project administration, Resources, Software, Writing – original draft, Writing – review & editing. David Y. Aharon: Conceptualization, Investigation, Data curation, Methodology, Resources, Formal analysis, Writing – original draft, Writing – original draft, Writing – review & editing. Ender Demir: Conceptualization, Investigation, Writing – review & editing. Renatas Kizys: Conceptualization, Investigation, Methodology, Writing – review & editing. Dariusz Zawadka: Conceptualization, Investigation, Writing – original draft, Writing – review & editing.


2Data retrieved from: on 31 July 2020.

3On the other hand, Correira et al. (2020) argue that rapid policy responses do not necessarily exacerbate the economic impact of the pandemic itself.

4Some governments implemented policies – such as supporting enterprises, employment, and personal incomes – in order to stimulate the economy and protect workers in the workplace (see, e.g., International Labor Organization (2020) for further information). However, due to difficulties in reaching standardized data, which could be reliably quantified, we decided not to include them in the study.

5Https:// (accessed 10 April 2020).

6More precisely, the WHO received the first notification on 31 December 2019, but this day was a non-trading day around the world.

7Https:// (accessed 10 April 2020).

8In case of missing policy variables, we rely on the most recent available observations. Otherwise, we use zero if no previous observation is available.

9Https:// (accessed 10 April 2020).

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