The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/1940-5979.htm

Impact of Stock market liquidity, the great COVID-19 on lockdown and the COVID-19 stock market global pandemic nexus liquidity in MENA countries 51

Anas Alaoui Mdaghri and Abdessamad Raghibi Received 21 June 2020 Revised 23 September 2020 National School of Business and Management (ENCG), Ibn Zohr University, 25 October 2020 Agadir, Morocco Accepted 27 October 2020 Cuong Nguyen Thanh Faculty of Accounting and Finance, Nha Trang University, Nha Trang, Vietnam, and Lahsen Oubdi National School of Business and Management (ENCG), Ibn Zohr University, Agadir, Morocco

Abstract Purpose – The purpose of this paper is to investigate the impact of the global coronavirus (COVID-19) pandemic on stock market liquidity, while taking into account the depth and tightness dimensions. Design/methodology/approach – The author used a panel data regression on stock market dataset, representing 314 listed firms operating in six Middle East and North African (MENA) countries from February to May 2020. Findings – The regression results on the overall sample indicate that the liquidity related to the depth measure was positively correlated with the growth in the confirmed number of cases and deaths and stringency index. Moreover, the market depth was positively related to the confirmed cases of COVID-19. The results also indicate that the liquidity of small cap and big cap firms was significantly impacted by the confirmed number of cases, while the stringency index is only significant for the liquidity depth measure. Moreover, the results regarding sectors and country level analysis confirmed that COVID-19 had a significant and negative impact of stock market liquidity. Research limitations/implications – This paper confirms that the global coronavirus pandemic has decreased the stock market liquidity in terms of both the depth and the tightness dimensions. Originality/value – While most empirical papers focused on the impact of the COVID-19 global pandemic on stock market returns, this paper investigated liquidity chock at firm level in the MENA region using both tightness and depth dimensions. Keywords Coronavirus, Stock market, Liquidity, MENA Paper type Research paper

Introduction The first months of 2020 brought a new challenge to our economies, with a shift in focus away from the traditional “usual” business risk panoply. The outbreak of the COVID pandemic put the markets under great stress and led to unprecedented uncertainty. All the affected countries and the attendant international investors were bound to be influenced by the speculative news about how deadly the virus was, when a vaccine would be available and how the governments were intending to respond to the crisis (Wagner, 2020). Reports surfaced indicated that the CBOE volatility index had exceeded the previous all-time peak that occurred during the 2008 financial crisis. Markets throughout the world experienced an intense state of high volatility and immense uncertainty. However, the panic has subsequently dissipated, with buy and sell movements beginning Review of Behavioral Finance to emerge again across the globe. Indeed, the current crisis has affected various economic Vol. 13 No. 1, 2021 pp. 51-68 © Emerald Publishing Limited 1940-5979 JEL Classification — G01, G40, G41 DOI 10.1108/RBF-06-2020-0132 RBF sectors in terms of creating opportunities driven by the wealth transfer mechanism. Here, 13,1 Xinhua (2020) reported that while China experienced the first wave of the pandemic, its financial market remained largely stable. The earlier stock market volatility was arguably related to investors’ overreaction, which was likely driven by various factors, including the intensive media coverage. The stock markets’ global integration can also partly explain the first wave of panic in the markets, which was rapidly controlled, and the markets returned to a state of stability. In fact, many central banks across the world intervened in the financial 52 markets through monetary policy easing and liquidity injection measures. While many continue to believe that the stock market is a place governed by greed, excessive risk taking and unethical behaviour, in reality, it is a vital indicator for the well- being of an economy due to its pricing feature, which enables policy makers and investors to develop a view of the future state of the economy. Here, stock market liquidity plays a major role since it allows different stakeholder to safely hold and exchange stock market instruments. Nonetheless, the stock markets have endured significant shocks due to the panic and the uncertainty surrounding the global economies. Researchers have established a direct link between bid–ask spread widening and uncertainty. Thus, the liquidity in the stock markets is expected to decrease, but in unequal terms throughout the various economic sectors since some have been more negatively impacted than others. On March 11, 2020, the COVID-19 outbreak was declared by the WHO as a global pandemic [1]. This led to most countries restricting movements, limiting public gatherings and reducing commercial air travel to a minimum. The reliance on such strategies was largely influenced by the measures adopted by China as the outbreak first surfaced. John Hopkins University (2020) reported that by March 2020, the containment measures adopted by China had helped to halt the spread of the virus and had significantly reduced the number of confirmed new cases. The reason for the stock markets’ relative immunity to pandemic-based shocks relates to the huge government support provided to the financial markets during such events. JPMorgan Insights (2020) summarises the major international government intervention plans that brought some stability to the financial markets. Here, the Federal Reserve unveiled a package of measures that included the purchase of assets ($500bn in treasuries and $200bn in mortgage-backed securities). Meanwhile, the ECB announced a 750bn euros pandemic emergency purchase program (PEPP) on top of the ongoing asset purchase program (APP), which followed the 2008 financial crisis and the Greek sovereign debt crisis. Elsewhere, the Bank of Japan announced that it would double stock purchases and would help companies obtain loans in response to the coronavirus pandemic. Moreover, it pledged to buy exchange- traded funds (ETFs) at an annual pace of around 12 trillion yen ($112.55bn) alongside further reductions in interest rates. Traditional theories attempt to explain stock markets features such as return and volatility based on investors’ rational behaviour. However, empirical studies have demonstrated that the behaviour of real-life investors differs from that traced in the traditional models. Indeed, uninformed investors often tend to operate outside of the systematic and informed behaviour of institutional investors, generally to their own detriment. This factor and the high media exposure that occurs in times of crisis means that many investors tend to take decisions outside of the framework of rationality, which results in a sharp decline and liquidity pressures in certain sectors. Since the media coverage of the ongoing global pandemic can be described as both huge and unprecedented, investors’ behaviour is undoubtedly being driven by these variables, which could lead to stock market dysfunction. While the volatility and liquidity pressures have been eased through intensive government intervention in the developed countries, the situation in developing countries is more complicated and divergent. In fact, Morocco tentatively cut the interest rates by 0.25%; Tunisia adopted a 100bps cut, while Egypt drastically decreased its interest rate by Impact of 300 points. Meanwhile, the stock markets have reduced the maximum variation thresholds to COVID-19 on reduce stock market volatility. The volatility was also amplified by the uncertainty surrounding the economic strength of the developing countries and the low economic growth stock market expectations. This may lead to foreign investors abandoning these markets, which will liquidity subsequently magnify the liquidity pressure among the local financial markets. Stock markets in the Middle East and North African (MENA) region are known to be heterogonous due to the economic and social disparities. While the stock markets of Casablanca 53 and Tunisia are relatively small and are facing various liquidity challenges, those in the Gulf region are more developed, with the Saudi Tadawul by far the largest market in the region. The OECD (2012) outlined six common features of the stock markets in the MENA region as follows: widespread state ownership, low regional integration, moderate competition for listing, young markets, a high level of retail investment and a diversification of financial products. The novelty of the current pandemic combined with the new emerging international patterns, such as intensive globalisation, the rapidly growing technologies and media banalisation, are the main motivations behind this paper. The current situation offers us an opportunity to study how market actors have reacted to the pandemic and how stock market liquidity has been affected. In addition, we examine the sector-related impact of this crisis since the confinement policies and the travel bans applied by most countries have significantly affected a number of sectors. Specifically, this paper investigates the impact of the great lockdown on stock market liquidity in specific MENA countries (Morocco, Tunisia, Egypt, KSA, the UAE and Qatar), covering the period from February 2020 to May 2020. This paper contributes to the existing literature in a number of ways. First, our results add to the current research on the impact of the COVID-19 pandemic on the stock markets and the impacts of pandemics in general on financial markets. Second, our study emphasises the firm- level and industry-specific aspects, taking the heterogeneity of the pandemic’s effect on each firm and each sector into consideration. Third, our work extends previous empirical studies on the liquidity related to the US stock markets and MENA countries (Baig et al., 2020). The remainder of the paper is organised as follows. Section 2 presents a brief literature review with a focus on stock market liquidity, pandemics and financial markets, before the research design – including the data and methodology – is outlined in Section 3. Section 4 details the results and provides a discussion along with a robustness test for the main model, while Section 5 presents the conclusion and the relevant implications.

Literature review Stock markets are naturally affected by major and exceptional events. Empirical studies have confirmed the correlation between stock markets and sporting events, political events, social media content, natural disasters and religious events (Kaplanski and Levy, 2010; Kollias et al., 2011; Liu and Zhang, 2015), with many documenting the impact of previous pandemics of the last 20 years (Marinc, 2016; Ichev and Marinc, 2018). However, no study has revealed the significant impact pandemics have had on the stock markets around the world. In fact, the global financial markets have experienced multiple pandemics during the past 30 years. Here, while the stock markets experienced sharp drops in the first days of the pandemic, the persistence of the volatility was quickly dissipated. Mohren et al. (2005) established a link between seasonable flu and individual work activity, which may have disrupted the operations of the financial markets. Similarly, McTier et al. (2013) affirmed that the trading activity involving proxies such as volatility and bid–ask spreads is influenced by flu epidemics in the US financial market. However, these effects relate to human activity and not to the mechanisms of the financial markets. Hence, they tend to disappear quickly, and the markets tend to return to normal operations. RBF Previous studies have investigated the impact of the coronavirus pandemic on stock 13,1 market returns, volatility and liquidity. The large-scale COVID-19 pandemic had an impact on stock market returns and liquidity since the pandemic resulted in significant economic slowdown (McKibbin and Fernando, 2020). Baker et al. (2020) investigated the impact of the pandemic on global equity markets and concluded that the stock markets’ response was unprecedented, demonstrating that the US equity market reached volatility levels that surpassed those seen in October 1987 and December 2008 and, before that, in late 1929 and 54 the early 1930s. Elsewhere, Sansa (2020) conducted a study on both the Chinese and the US equity markets during the month of March, with the results confirming that there was a strong correlation between the newly confirmed cases of coronavirus and the stock markets in both countries. Meanwhile, (Liu et al., 2020) adopted an event study related to global equity markets using stock indices from 21 leading countries affected by the pandemic. The authors found that the stock markets fell quickly following the advent of the pandemic and confirmed the negative abnormal returns with high persistence in Asia compared to other regions. In addition, (Baig et al., 2020) incorporated the liquidity component in their study and reported a positive relationship between the confirmed cases and the lockdown and market illiquidity. Strong evidence of the negative impact of the coronavirus cases on the stock market returns in the Chinese stock market was also found (Al-Awadhi et al., 2020). Meanwhile, (Yan et al., 2020) conducted a study based on the previous empirical findings related to how global pandemics tend to cause short-term shocks followed by quick recoveries. Here, the authors suggest that investing in airlines and travel companies, which only experienced a short-term effect, alongside buying gold ETFs could be the best investment strategy in the current crisis. Market liquidity is at the centre of investors’ concerns. It affects the return of their portfolios since an illiquid asset involves higher buy and sell costs and poses the risk of higher losses. Numerous studies attempted to price illiquidity and investigated its association with expected stock returns (Amihud, 2002; Amihud and Mendelson, 1986; Hendershott and Seasholes, 2014; Pastor and Stambaugh, 2003). Nevertheless, the concept of liquidity has always been considered as elusive (Amihud, 2002), with it easy to sense but difficult to define and even more difficult to estimate (Lesmond, 2005; O’Hara, 2004). Due to its polymorphic character, there is no common definition that can capture the multiple dimensions of liquidity. (Kyle, 1985) defines market liquidity as the ability of a particular asset to be traded in the market in a considerably short-time with a minimum loss of value, while (Chordia et al., 2005) state that market liquidity can be defined as “the ability to buy or sell large quantities of an asset quickly and at low cost”. Elsewhere, several researchers (Sarr and Lybek, 2002; Wyss, 2004) have noted that liquid markets tend to have the following four main characteristics: (1) depth, which refers to the ability to buy and sell a certain amount of assets without causing a major impact on the quoted value; (2) tightness, which is related to low transaction costs and indicates the ability to buy and sell an asset at around the same price at the same time; (3) immediacy, which is associated with the efficiency of the trading and represents the required time within which orders can be executed and (4) resiliency, which refers to the rapid flow of new orders to correct any market imbalances. However, it should be noted that there is no single liquidity measure that can include all the above aspects. The growing body of the empirical literature has proposed several liquidity measures to capture all the aforementioned dimensions. However, due to the lack of high-frequency data regarding certain assets and specific markets, the computation of liquidity remains difficult. The use of one liquidity measure at the expense of another is necessarily dependent on the availability of the data. In fact, most liquidity measures require data related to the market microstructure such as bid–ask spread, volumes and daily quotes (Dıaz and Escribano, 2020). Market depth has been widely studied by a number of researchers (Amihud, 2002; Chordia et al., 2005; Dick-Nielsen et al.,2012; Fong et al., 2017; Kyle, 1985; Nashikkar et al., 2011; Ranaldo, 2001; Wyss, 2004), while research related to bid–ask spread, which is used as a measure for market Impact of tightness to capture the cost of transactions, has also been extensively carried out (Chung and COVID-19 on Zhang, 2014; Corwin and Schultz, 2012; Fong et al.,2017; Goyenko et al.,2009; Lesmond, 2005; Roll, 1984; Wyss, 2004). Nonetheless, only a limited number of studies have analysed the market stock market resiliency and immediacy, largely due to the difficulty of estimating this specific liquidity liquidity characteristic (Alan et al.,2015; Kim and Kim, 2015; Liu, 2006). 55 Methodology Data In this research study, we used a balanced panel dataset of 24,492 daily-firm observations for the period of February 3rd, 2020–May 20th, 2020, representing 314 listed firms in Morocco, Tunisia, Egypt, KSA, Qatar and the UAE. This covers the period before most of these countries reported their first COVID-19 case to the WHO [2]. The daily market firm, daily price index and exchange rate data were gathered from Datastream, while the data related to the COVID-19 cases, deaths and stringency index were obtained from the Ourworldindata website [3] and were matched with the John Hopkin’s database.

Dependent variables The aim of this paper was to investigate the impact of the COVID-19 pandemic on market liquidity. Given that the latter is multidimensional, we chose to measure two different proxies to capture both market depth and tightness [4]. We did not include other dimensions such as resiliency and immediacy due to the non-availability of the daily data on certain variables. Market depth was computed using Amihud’s (2002) measure of illiquidity as the first dependent variable with reference to Kyle’s(Kyle, 1985) price impact measure, a widely used robust proxy. Amihud’s measure represents the price shock provoked by a one-dollar volume. This ratio is calculated by dividing the absolute daily return of the stock by its daily dollar trading volume. Thus, the higher the ratio, the higher the illiquidity. Ri;t Amihudi;t ¼ (1) lnðvolumei;tÞ where Ri;t is the daily stock return calculated as the natural logarithm of the closing price at t divided by the closing price at t–1, and volumei;t represents the dollar volume of stock i at day t. We denote this measure as AMIHUD in our study. Regarding market tightness, which is usually computed in terms of the bid–ask spread, we employed the effective spread as the second dependent variable. To do so, we used the Closing Percent Quoted Spread denoted as CPQS, which was introduced by (Chung and Zhang, 2014). The CPQS measure is a low-estimator of bid–ask spread which is computed using daily closing bid and ask prices. It is considered a good proxy to capture the effective bid–ask spread (Fong et al., 2017; Gao et al., 2020). Ask pricei;t Bid pricei;t

CPQS ; ¼ (2) i t þ = Ask pricei;t Bid pricei;t 2 where Ask pricei;t and Bid pricei;t are, respectively, the ask and the bid closing prices of stock i at day t.

Independent variables COVID-19 variables. We included three independent variables representing the COVID-19 daily cases and deaths and the stringency index in the six MENA countries. The first variable RBF is the daily growth of the total number of confirmed cases and was denoted as CASES_G, 13,1 while the second was calculated as the daily growth of the number of confirmed deaths and was denoted as DEATHS_G. The last variable was computed as the natural logarithm of the daily stringency index and was denoted as STRINGENCY. It should be noted that the latter is an index rescaled from a value of 0–100 and indicates the governments’ responses to the COVID-19 pandemic. It is a measure that integrates nine response indicators, including school and workplace closings and travel bans. 56 Firm, stock market and macroeconomic control variables. We also incorporated several control variables that may have had an effect on the firms’ stock market liquidity. With respect to firm level, we employed the firms’ daily market capitalisation calculated as the natural logarithm of daily closing price multiplied by their outstanding shares. This was denoted as MKT_CAPI. The daily volatility was also included using (Garman and Klass, 1980) volatility estimator, which is denoted as GK_VOL and was computed as follows: vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u     u 2 t1 HPi;t CPi;t GKVOL ¼ log ð2:logð2Þ1Þ:log (3) 2 LPi;t OPi;t

where HPi;t,LPi;t,CPi;t and OPi;t are, respectively, the highest, lowest, closing and opening prices of firm i at day t. Regarding the stock market level, we included the daily return of the countries’ stock market indices, namely, MASI, TUNINDEX, EGX100, TASI, QE and ADX General. This was denoted as R_INDEX. In terms of the macroeconomic level, we chose to integrate the daily exchange rates of each country against the U.S. dollar, as we believe that the FX market will also have been affected by the COVID-19 outbreak. This was denoted as EX_R. Stock market liquidity of the MENA countries. Figures 1 and 2 illustrate the Amihud illiquidity and the CPQS measures for all the stock in our study, respectively. It is clear that the stocks’ illiquidity gradually rose during the month of March (the date when the majority of MENA countries announced their first COVID-19 case), reaching its peak in the middle of the month. This means that, on the one hand, the impact of trading orders on market prices was more pronounced during this period, while, on the other, the transaction costs reached their highest level, which indicates that the stocks had become more expensive to both buy and sell. Furthermore, we noted that immediately after this point, the market stocks’ liquidity of these financial markets (for both measures) was adjusted to regain lower levels during the month of April. However, it should be noted that an increase in the illiquidity of the stocks took place during the month of May.

Empirical findings Econometric model specifications Hsiao (2014) stated that panel data analysis has the advantage over cross-section and time series datasets since it improves the efficiency of the econometric estimates by providing higher degrees of freedom, reducing the multicollinearity problem among the independent variables and allowing for heterogeneity across individual factors. To estimate the effect of the COVID-19 pandemic on market liquidity, we developed the following panel regression model: ¼ β þ β þ β þ ε MLi;t 0 1Covid Vari;t Xi;t i;t (4)

where MLi;t indicates market liquidity (tightness and depth measures) of stock i at day t, CovidVari;t represents the vector of the COVID-19 independent variables, Xi;t designates the β ε vector of the control variables and 0 and i;t are the intercept and the error term, respectively. Impact of COVID-19 on stock market liquidity

57

Figure 1. Amihud illiquidity measure

Figure 2. Closing Percent Quoted Spread measure

Summary of descriptive statistics and multicollinearity test Table 1 shows the descriptive statistics of all the variables used in our empirical model. As is clear, the mean value of the market depth of the selected firms was 0.17%, with a minimum value of 0% and a maximum of 2.73%. Meanwhile, the average value of market tightness was set at 2.05% with a minimum of 38.07% and a maximum of 83.99%. In terms of the COVID-19 variables, the mean values of confirmed cases and death growth rates were 21.5% and 8.65%, respectively, while the stringency index returned an average value of 2.85. Table 2 presents the correlation matrix and the VIF indicator of the dependent and independent variables. As is clear, the correlation between the independent variables was weak since there was no correlation above 0.5. The VIF values of all the independent variable were also below 5, which indicates that there was no multicollinearity in our model.

Regression results To decide which estimator (fixed effects or random effects) is more suitable for our model (4), we ran the Hausman test. The results demonstrated that the p-value was 0.000 < 0.05, which RBF indicates that the fixed effects estimator was more appropriate for our research. Table 3 13,1 highlights the regression results. The estimators were robust and clustered at firm level. As is clear from Table 3, the Amihud measure (AMIHUD) was significantly positively associated with the daily growth in confirmed cases and deaths as well as the stringency index. This suggests that an increase in COVID-19 cases and deaths hampered the stock market depth. Moreover, the numerous measures taken by the governments of the six MENA countries to prevent and stop the pandemic appeared to have negatively affected the stock 58

Variable Obs Mean Std. dev Min Max

AMIHUD 24,492 0.0017 0.0020 0.0000 0.0273 CPQS 24,492 0.0205 0.0363 0.3807 0.8399 CASES_G 24,492 0.2153 1.0000 0.0000 15.3333 DEATHS_G 24,492 0.0865 0.2196 0.0000 1.6667 STRINGENCY 24,492 2.8529 2.0316 0.0000 4.5452 MKT_CAPI 24,492 19.4336 2.1587 13.0212 28.2271 GK_VOL 24,492 0.0217 0.0179 0.0000 0.1449 R_INDEX 24,492 0.0022 0.0219 0.1000 0.0808 Table 1. EX_R 24,492 0.0001 0.0026 0.0197 0.0185 Descriptive statistics Source(s): Authors’ calculations

Variables (1) (2) (3) (4) (5) (6) (7) (8) (9)

(1) AMIHUD 1.00 (2) CPQS 0.10 1.00 (3) CASES_G 0.22 0.05 1.00 (4) DEATHS_G 0.06 0.03 0.00 1.00 (5) STRINGENCY 0.07 0.01 0.01 0.26 1.00 (6) MKT_CAPI 0.21 0.24 0.02 0.03 0.03 1.00 (7) GK_VOL 0.37 0.09 0.11 0.03 0.09 0.25 1.00 (8) R_INDEX 0.32 0.03 0.32 0.03 0.11 0.01 0.09 1.00 (9) EX_R 0.03 0.05 0.09 0.09 0.02 0.00 0.01 0.02 1.00 –– Table 2. VIF (MEAN 1.09) 1.13 1.08 1.1 1.07 1.1 1.14 1.02 Correlation matrix Source(s): Authors’ calculations

Market depth Market tightness AMIHUD CPQS

CASES_G 0.0002*** (0.0000) 0.0009*** (0.0003) DEATHS_G 0.0002*** (0.0001) 0.0004 (0.0013) STRINGENCY 0.0000*** (0.0000) 0.0003 (0.0002) MKT_CAPI 0.0011*** (0.0002) 0.0064** (0.0032) GK_VOL 0.0288*** (0.0019) 0.1180*** (0.0193) R_INDEX 0.0249*** (0.0011) 0.0054 (0.0116) EX_R 0.0146*** (0.0042) 0.4827*** (0.1462) _CONS 0.0222*** (0.0041) 0.1421** (0.0628) Observations 24,492 24,492 Table 3. R-squared 0.2028 0.0110 Regression results Note(s): Robust standard errors are in parentheses; ***p < 0.01, **p < 0.05, *p < 0.1 market depth. The results also suggest that the Closing Percent Quoted Spread measure Impact of (CPQS) was positively and significatively related to only the daily growth of confirmed cases. COVID-19 on This means that the transaction costs in the MENA stock markets rose after each increase in COVID-19-related cases. However, the daily growth in confirmed deaths and stringency stock market index appeared to have had no significant effect on market tightness. liquidity

Additional analysis 59 Market capitalisation analysis We conducted further analysis to investigate the effect of COVID-19 on the stock market liquidity of both big and small capitalisation firms, as we expected that the impact would have been largely different depending on the size of the listed companies. To this end, we divided our stock sample into two sub-samples based on market capitalisation. Firms with an average market capitalisation that covered the 70th percentile of the whole sample were considered as big capitalization stocks, while the remainder were classified as small capitalisation firms. The results are listed in Table 4. Here, it would appear that the liquidity of both the big and the small firms was negatively impacted by the COVID-19 pandemic. With regard to the big capitalisation firms, their market depth was negatively and significantly correlated with both the daily growth of confirmed cases and the stringency index, while it appears that the confirmed cases and deaths growth negatively affected these firms’ market tightness. In terms of the small capitalisation firms, we observed that their market depth was negatively impacted by the daily growth in COVID-19 cases and deaths as well as the stringency index. Meanwhile, we found that only the daily growth in cases impeded the small firms’ market tightness.

Sector analysis To gain more insight into the impact of COVID-19 on stock market liquidity, we examined a number of sectors, some of which we expected to have been more affected by the effects of the pandemic than others. We ran additional panel regressions after classifying our stock sample into the following eight sectors: consumer goods, energy and utilities, financial, healthcare, industrial, materials, real estate and technology and communications. The results are displayed in Table 5. In terms of market depth, we observed that all the sectors, with the exception of healthcare, were negatively affected by the daily growth in confirmed cases, while we found that only industrial and real estate were significantly and negatively influenced by the daily growth in confirmed deaths. Meanwhile, consumer goods, financials,

Big cap Small cap AMIHUD CPQS AMIHUD CPQS

CASES_G 0.0003*** (0.0000) 0.0014*** (0.0005) 0.0002*** (0.0000) 0.0009*** (0.0003) DEATHS_G 0.0001 (0.0001) 0.0050** (0.0023) 0.0004*** (0.0001) 0.0012 (0.0016) STRINGENCY 0.0001*** (0.0000) 0.0003 (0.0002) 0.0000*** (0.0000) 0.0003 (0.0002) MKT_CAPI 0.0001*** (0.0000) 0.0037 (0.0046) 0.0012*** (0.0003) 0.0060*** (0.0009) GK_VOL 0.0403*** (0.0019) 0.1585*** (0.0274) 0.0256*** (0.0022) 0.1047*** (0.0237) R_INDEX 0.0151*** (0.0012) 0.0236* (0.0138) 0.0297*** (0.0013) 0.0011 (0.0167) EX_R 0.0108** (0.0048) 0.8072*** (0.2731) 0.0152*** (0.0055) 0.3752** (0.1685) _CONS 0.0016*** (0.0004) 0.0884 (0.1015) 0.0235*** (0.0047) 0.1300*** (0.0161) Table 4. Obs 7,410 7,410 17,082 17,082 Regression results for R-squared 0.5194 0.0277 0.4652 0.1422 big and small Note(s): Robust standard errors are in parentheses; ***p < 0.01, **p < 0.05, *p < 0.1 capitalisations sector to according tightness and depth market of results Regression 5. Table 60 13,1 RBF

Sectors Consumer goods Energy and utilities Financials Healthcare Industrials Materials Real estate Tech and com

Market depth (AMIHUD) CASES_G 0.0002** (0.0001) 0.0003** (0.0001) 0.0002*** (0.0000) 0.0002 (0.0001) 0.0002*** (0.0001) 0.0002*** (0.0000) 0.0001*** (0.0000) 0.0002* (0.0001) DEATHS_G 0.0002 (0.0002) 0.0002 (0.0002) 0.0001 (0.0001) 0.0001 (0.0003) 0.0004* (0.0002) 0.0002 (0.0001) 0.0005** (0.0002) 0.0002 (0.0001) STRINGENCY 0.0001*** (0.0000) 0.0000 (0.0000) 0.0000** (0.0000) 0.0001** (0.0000) 0.0000 (0.0000) 0.0000 (0.0000) 0.0001** (0.0000) 0.0000 (0.0000) MKT_CAPI 0.0006 (0.0004) 0.0018** (0.0008) 0.0013*** (0.0003) 0.0007 (0.0015) 0.0022*** (0.0006) 0.0018*** (0.0004) 0.0001*** (0.0000) 0.0014*** (0.0004) GK_VOL 0.0245*** (0.0065) 0.0281*** (0.0072) 0.0314*** (0.0025) 0.0098 (0.0089) 0.0302*** (0.0052) 0.0245*** (0.0039) 0.0353*** (0.0035) 0.0402*** (0.0053) R_INDEX 0.0280*** (0.0030) 0.0153*** (0.0036) 0.0236*** (0.0016) 0.0202*** (0.0033) 0.0260*** (0.0039) 0.0314*** (0.0029) 0.0208*** (0.0026) 0.0201*** (0.0037) EX_R 0.0216 (0.0135) 0.0199** (0.0084) 0.0169*** (0.0064) 0.0099 (0.0149) 0.0040 (0.0179) 0.0111 (0.0092) 0.0116 (0.0168) 0.0166 (0.0099) _CONS 0.0115 (0.0074) 0.0394** (0.0164) 0.0267*** (0.0050) 0.0125 (0.0292) 0.0424*** (0.0122) 0.0354*** (0.0077) 0.0035*** (0.0009) 0.0286*** (0.0081) Observations 4,602 936 6,708 1,092 2,262 4,134 3,276 1,482 R-squared 0.1823 0.2059 0.2435 0.1224 0.2108 0.2180 0.2730 0.2730 Market tightness (CPQS) CASES_G 0.0017** (0.0008) 0.0028* (0.0014) 0.0002 (0.0005) 0.0003 (0.0012) 0.0016** (0.0008) 0.0005 (0.0008) 0.0010 (0.0006) 0.0012 (0.0011) DEATHS_G 0.0016 (0.0022) 0.0107 (0.0122) 0.0015 (0.0026) 0.0048 (0.0055) 0.0053** (0.0024) 0.0009 (0.0037) 0.0012 (0.0033) 0.0054 (0.0040) STRINGENCY 0.0001 (0.0002) 0.0008 (0.0010) 0.0000 (0.0003) 0.0004 (0.0003) 0.0003 (0.0006) 0.0014** (0.0005) 0.0002 (0.0004) 0.0008* (0.0004) MKT_CAPI 0.0034*** (0.0011) 0.0404* (0.0206) 0.0114 (0.0072) 0.0024 (0.0018) 0.0051*** (0.0014) 0.0001 (0.0068) 0.0062 (0.0068) 0.0001 (0.0068) GK_VOL 0.1962*** (0.0657) 0.1378 (0.1082) 0.1000*** (0.0298) 0.1434** (0.0686) 0.0344 (0.0552) 0.0997*** (0.0355) 0.0831* (0.0484) 0.2376*** (0.0735) R_INDEX 0.0162 (0.0233) 0.0031 (0.0665) 0.0168 (0.0245) 0.0137 (0.0428) 0.0330 (0.0626) 0.0286 (0.0281) 0.0461* (0.0269) 0.0557 (0.0337) EX_R 0.6907 (0.4214) 1.0457 (0.7037) 0.5053* (0.2750) 0.0481 (0.1097) 0.0533 (0.2380) 0.7429* (0.4131) 0.2406 (0.1901) 0.2552 (0.1889) _CONS 0.0773*** (0.0207) 0.8962* (0.4437) 0.2411* (0.1436) 0.0611* (0.0366) 0.1207*** (0.0282) 0.0201 (0.1309) 0.1373 (0.1311) 0.0064 (0.1392) Observations 4,602 936 6,708 1,092 2,262 4,134 3,276 1,482 R-squared 0.0168 0.0567 0.0115 0.0133 0.0072 0.0163 0.0075 0.0534 Note(s): Robust standard errors are in parentheses; ***p < 0.01, **p < 0.05, *p < 0.1 healthcare and real estate were found to have been impacted by the stringency measures. Impact of With regard to market tightness, the results suggest that consumer goods, energy and COVID-19 on utilities and industrials were negatively affected by the daily growth in confirmed COVID-19 cases. However, it appears that industrial sector was positively impacted by the daily growth stock market in confirmed deaths. Finally, the results show that the stringency index has negatively and liquidity significantly influenced the materials and technology and communication sectors, while we found that there was no significant association with the other sectors. 61

Countries analysis In order to develop a better understanding on the impact of COVID-19 pandemic on each financial market, we conducted further panel regression analysis on the stocks of the six sampled countries’ financial markets namely: Egypt, KSA, Morocco, Qatar, Tunisia and UAE. We believed that the financial market of each country has reacted differently to the pandemic effects. Table 6 presents the regression results of market depth and tightness of each country. According to market depth results, we noticed that all countries were negatively impacted by the daily growth in confirmed COVID-19 cases. While it appears that only Egypt was significantly and negatively affected by the daily growth in confirmed COVID-19 deaths, surprisingly, KSA and UAE were found to have been positively impacted by the daily growth in confirmed deaths. Furthermore, the stringency measures seem to have a negative and significant influence on Egypt, KSA and UAE. With respect to market tightness, we observed a significant positive coefficient on daily confirmed cases for Egypt, Morocco and Tunisia, suggesting that transaction costs in those financial markets rose after the increasing of the COVID-19 cases. Meanwhile, we found a significantly negative coefficient on daily confirmed deaths only for Tunisia and a significant positive coefficient for KSA. In terms of the stringency index, the coefficients are significantly positive for Morocco and UAE and significantly negative for Egypt and KSA.

Robustness tests In order to check the robustness of our results, we considered an alternative measure of both dependent and independent variables as well as a new estimation technique.

Using GMM estimation First, we mobilized the Generalized Method of Moments (GMM) developed by (Blundell and Bond, 1998) to count for endogeneity and omitted variables problems in our main model. In addition to that, the lagged impact of COVID-19 variables can generate a dynamic component in the model. To test for the validity of our estimation, we have conducted Hansen and AR tests which yielded statistically insignificant results for all our samples. Note that Hansen tests for robustness of model and AR tests for second-order autocorrelation and it has confirmed the absence of second-order autocorrelation in the model. The results are presented in Table A1. The regression results for the market depth using Amihud’s measure yielded significant results at 1% for confirmed cases, deaths and stringency index. Parallelly, using market tightness measure, results confirm that cases are positively related to market tightness and statistically significant at 10%. In addition to that, the significant relationship between the stringency index and market tightness measure remain strong and positive. Accordingly, our robustness check results confirm our main model’s results regarding both the market depth and tightness. countries to according tightness and depth market of results Regression 6. Table 62 13,1 RBF

Countries Egypt KSA Morocco Qatar Tunisia UAE

Market depth (AMIHUD) CASES_G 0.0001*** (0.0000) 0.0009*** (0.0000) 0.0016*** (0.0002) 0.0002*** (0.0000) 0.0009*** (0.0003) 0.0018*** (0.0005) DEATHS_G 0.0016*** (0.0002) 0.0003*** (0.0000) 0.0002 (0.0002) 0.0001 (0.0002) 0.0002 (0.0003) 0.0008** (0.0004) STRINGENCY 0.0001*** (0.0000) 0.0000*** (0.0000) 0.0001** (0.0000) 0.0001 (0.0000) 0.0000 (0.0000) 0.0003*** (0.0000) MKT_CAPI 0.0014*** (0.0003) 0.0002* (0.0001) 0.0022*** (0.0006) 0.0000 (0.0010) 0.0021** (0.0008) 0.0001* (0.0001) GK_VOL 0.0139*** (0.0032) 0.0351*** (0.0015) 0.0439*** (0.0044) 0.0320*** (0.0022) 0.0127 (0.0083) 0.0329*** (0.0060) R_INDEX 0.0388*** (0.0025) 0.0144*** (0.0009) 0.0026 (0.0031) 0.0216*** (0.0018) 0.0477*** (0.0079) 0.0080*** (0.0028) EX_R 0.0669*** (0.0138) 0.0574*** (0.0179) 0.0121** (0.0057) 0.0030 (0.0074) 0.0005 (0.0087) 0.3682 (0.8042) _CONS 0.0261*** (0.0061) 0.0046* (0.0025) 0.0432*** (0.0128) 0.0008 (0.0200) 0.0394** (0.0138) 0.0029* (0.0016) Observations 6,006 11,076 2,652 2,496 1,248 1,014 R-squared 0.2122 0.3349 0.2532 0.1995 0.1350 0.1715 Market tightness (CPQS) CASES_G 0.0006* (0.0004) 0.0005 (0.0008) 0.0165*** (0.0058) 0.0001 (0.0002) 0.0039** (0.0015) 0.0021 (0.0028) DEATHS_G 0.0014 (0.0032) 0.0012** (0.0005) 0.0057 (0.0080) 0.0017 (0.0014) 0.0094** (0.0036) 0.0027 (0.0026) STRINGENCY 0.0007*** (0.0002) 0.0005*** (0.0002) 0.0049*** (0.0007) 0.0000 (0.0002) 0.0004 (0.0003) 0.0023*** (0.0007) MKT_CAPI 0.0018*** (0.0006) 0.0157*** (0.0059) 0.0036** (0.0017) 0.0078*** (0.0026) 0.0073 (0.0043) 0.0184*** (0.0043) GK_VOL 0.1126*** (0.0311) 0.0785** (0.0388) 0.4044*** (0.1054) 0.1063*** (0.0181) 0.2659*** (0.0493) 0.1440** (0.0481) R_INDEX 0.1318*** (0.0368) 0.0205** (0.0102) 0.0108 (0.0711) 0.0277 (0.0168) 0.0784** (0.0298) 0.0192 (0.0282) EX_R 1.0893*** (0.3020) 0.1744 (0.3589) 0.9313*** (0.3003) 0.0656 (0.0458) 0.0596 (0.0561) 8.7890 (5.4552) _CONS 0.0709*** (0.0120) 0.3186*** (0.1188) 0.1045*** (0.0329) 0.1671*** (0.0542) 0.1255 (0.0784) 0.3868*** (0.0929) Observations 6,006 11,076 2,652 2,496 1,248 1,014 R-squared 0.0111 0.0145 0.0887 0.0468 0.0734 0.0707 Note(s): Robust standard errors are in parentheses; ***p < 0.01, **p < 0.05, *p < 0.1 Using alternative dependent variable Impact of We also investigated the robustness of our results by replacing the AMIHUD and CPQS COVID-19 on measures with an alternative market liquidity measure developed by (Florackis et al., 2011) [5]. It is defined as the ratio of absolute stock return divided by its turnover ratio and captures stock market the price impact, we denoted it as RtoTR. Results displayed in Table A2 confirm our main liquidity findings that liquidity in its depth dimension is positively and significantly related to the COVID-19 pandemic confirmed cases. 63 Using additional independent variable Likewise, we have also chosen to include an additional independent variable namely the market to book ratio. Interestingly, the regression results highlighted in Table A3 remained the same for both market depth and tightness measures. Ultimately, robustness tests have all confirmed the impact of COVID-19 global pandemic on stock market liquidity in our selected MENA countries.

Conclusions The coronavirus pandemic resulted in an unprecedented global lockdown, which greatly halted global growth. At the same time, the financial markets endured a stressful period of high volatility, which was eventually returned to normality after intensive interventions from the central banks. Various studies have investigated the return response and volatility following the outbreak of the coronavirus around the world and the findings confirmed the negative impact of the global pandemic on the stock market returns along with a spike in market volatility. The current paper addressed the issue of stock market liquidity through employing depth and tightness measures to capture the impact of the coronavirus on the markets. Here, we used a sample of 314 listed firms operating in six MENA countries from February 1st to May 20th, 2020 using panel data analysis. Our results confirmed that liquidity was positively correlated with the growth in confirmed cases and deaths in terms of the depth measure and with the confirmed cases for the tightness measure. In addition, the stringency index was positively related to depth liquidity measure, indicating that the governments’ responses to the outbreak induced a liquidity shock on the stock markets. Our study also investigated the impact of the outbreak on small and big cap firms, with the results indicating that the liquidity of small cap firms was significantly impacted by the confirmed number of cases and deaths. The results were also significant for the big cap firms. Moreover, the results regarding sectors and country level analysis confirmed that COVID-19 had a significant and negative impact of stock market liquidity. As the unprecedented global pandemic persists, our results have shown that stock market liquidity was instantly hit by the effect of the COVID-19. Indeed, all our selected MENA countries have experienced a liquidity shock across different industries and countries. However, we have recorded that deaths have not significantly impacted our sample. The reason might be related to a lagged effect as the number of deaths have started to rise by the end of our studied period. This implies that governments and regulators should act actively to stimulate stock market liquidity as well to support affected industries. Furthermore, our study has the limit to focus on the first three months of the pandemic. Future research might go further to broaden their time period, especially after different countries announcing recovery plans and earnings release.

Notes 1. https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media- briefing-on-covid-19-11-march-2020 RBF 2. Except for the UAE: its first case was reported on January 27th, 2020. 13,1 3. The data are available and updated on a daily basis. 4. Given the multifaceted nature of market liquidity, the literature proposes many other low-frequency proxies. The reader could refer to the survey paper of Le and Gregoriou (2020) for more details. 5. This measure is considered as free of any size bias allowing comparability across firm sizes. 64 References Al-Awadhi, A.M., Alsaifi, K., Al-Awadhi, A. and Alhammadi, S. (2020), “Death and contagious infectious diseases: impact of the COVID-19 virus on stock market returns”, Journal of Behavioral and Experimental Finance, Vol. 27, doi: 10.1016/j.jbef.2020.100326. Alan, N.S., Hua, J., Peng, L. and Schwartz, R.A. (2015), Stock Resiliency and Expected Returns, [WWW Document], Baruch College Zicklin School of Business, New York. Amihud, Y. (2002), “Illiquidity and stock returns: cross-section and time series effects”, Journal of Financial Markets, Vol. 5, pp. 31-56. Amihud, Y. and Mendelson, H. (1986), “Asset pricing and the bid-ask spread”, Journal of Financial Economics, Vol. 17, pp. 223-249, doi: 10.1016/0304-405X(86)90065-6. Baig, A., Butt, H.A., Haroon, O. and Rizvi, S.A.R. (2020), “Deaths, panic, lockdowns and US equity markets: the case of COVID-19 pandemic”, SSRN Journal, 101701, doi: 10.2139/ssrn.3584947. Baker, S.R., Bloom, N., Davis, S.J., Kost, K., Sammon, M. and Viratyosin, T. (2020), “The unprecedented stock market reaction to COVID-19”, The Review of Asset Pricing Studies. doi: 10.1093/rapstu/ raaa008. Blundell, R. and Bond, S. (1998), “Initial conditions and moment restrictions in dynamic panel data models”, Journal of Econometrics, Vol. 29, pp. 115-143. Chordia, T., Sarkar, A. and Subrahmanyam, A. (2005), “An empirical analysis of stock and bond market liquidity”, Review of Financial Studies, Vol. 18, pp. 85-129, doi: 10.1093/rfs/hhi010. Chung, K.H. and Zhang, H. (2014), “A simple approximation of intraday spreads using daily data”, Journal of Financial Markets, Vol. 17, pp. 94-120, doi: 10.1016/j.finmar.2013.02.004. Corwin, S. and Schultz, P. (2012), “A simple way to estimate bid-ask spreads from daily high and low prices”, Journal of Finance, Vol. LXVII, pp. 719-760. Dıaz, A. and Escribano, A. (2020), “Measuring the multi-faceted dimension of liquidity in financial markets: a literature review”, Research in International Business and Finance, Vol. 51, p. 101079, doi: 10.1016/j.ribaf.2019.101079. Dick-Nielsen, J., Feldhutter,€ P. and Lando, D. (2012), “Corporate bond liquidity before and after the onset of the subprime crisis”, Journal of Financial Economics, Vol. 103, pp. 471-492, doi: 10.1016/ j.jfineco.2011.10.009. Florackis, C., Gregoriou, A. and Kostakis, A. (2011), “Trading frequency and asset pricing on the London Stock Exchange: evidence from a new price impact ratio”, Journal of Banking and Finance, Vol. 35, pp. 3335-3350. Fong, K.Y.L., Holden, C.W. and Trzcinka, C.A. (2017), “What are the best liquidity proxies for global research?”, Review of Finance, Vol. 21, pp. 1355-1401, doi: 10.1093/rof/rfx003. Gao, Y., Zhao, W. and Wang, M. (2020), “The comparison study of liquidity measurements on the Chinese stock markets”, Emerging Markets Finance and Trade, Vol. 0, pp. 1-29, doi: 10.1080/ 1540496X.2019.1709819. Garman, M.B. and Klass, M.J. (1980), “On the estimation of security price volatilities from historical data”, The Journal of Business, Vol. 53, p. 67, doi: 10.1086/296072. Goyenko, R.Y., Holden, C.W. and Trzcinka, C.A. (2009), “Do liquidity measures measure liquidity?”, Journal of Financial Economics, Vol. 92, pp. 153-181, doi: 10.1016/j.jfineco.2008.06.002. Hendershott, T. and Seasholes, M.S. (2014), “Liquidity provision and stock return predictability”, Impact of Journal of Banking and Finance, Vol. 45, pp. 140-151, doi: 10.1016/j.jbankfin.2013.12.021. COVID-19 on Hsiao, C. (2014), Analysis of Panel Data, Cambridge University Press. stock market Ichev, R. and Marinc, M. (2018), “Stock prices and geographic proximity of information: evidence from liquidity the Ebola outbreak”, International Review of Financial Analysis, Vol. 56, pp. 153-166. John Hopkins University (2020), Coronavirus COVID-19 Global Cases, Johns Hopkins CSSE. JPMorgan Insights (2020), “Assessing the fallout from the coronavirus pandemic”, available at: https:// 65 www.jpmorgan.com/insights/research/coronavirus-impact (accessed 15 April 2020). Kaplanski, G. and Levy, H. (2010), “Sentiment and stock prices: the case of aviation disasters”, Journal of Financial Economics, Vol. 95, pp. 174-201, doi: 10.1016/j.jfineco.2009.10.002. Kim, J. and Kim, Y. (2015), “Transitory prices, resiliency, and the cross-section of stock returns”, International Review of Financial Analysis, Vol. 63, pp. 243-256, doi: 10.1016/j.irfa.2018.11.009. Kollias, C., Manou, E., Papadamou, S. and Stagiannis, A. (2011), “Stock markets and terrorist attacks: comparative evidence from a large and a small capitalization market”, European Journal of Political Economy, Vol. 27, Supplement 1, pp. S64-S77, doi: 10.1016/j.ejpoleco.2011.05.002. Kyle, A.S. (1985), “Continuous auctions and insider trading”, Econometrica, Vol. 53, pp. 1315-1335. Le, H. and Gregoriou, A. (2020), “How do you capture liquidity? A review of the literature on low? frequency stock liquidity”, Journal of Economic Surveys, pp. 1-17, doi: 10.1111/joes.12385. Lesmond, D.A. (2005), “Liquidity of emerging markets”, Journal of Financial Economics, Vol. 77, pp. 411-452, doi: 10.1016/j.jfineco.2004.01.005. Liu, L. and Zhang, T. (2015), “Economic policy uncertainty and stock market volatility”, Finance Research Letters, Vol. 15, pp. 99-105, doi: 10.1016/j.frl.2015.08.009. Liu, W. (2006), “A liquidity-augmented capital asset pricing model”, Journal of Financial Economics, Vol. 82, pp. 631-671, doi: 10.1016/j.jfineco.2005.10.001. Liu, H., Manzoor, A., Wang, C., Zhang, L. and Manzoor, Z. (2020), “The COVID-19 outbreak and affected countries stock markets response”, IJERPH, Vol. 17, p. 2800, doi: 10.3390/ijerph17082800. Marinc, R.I.M. (2016), “Geographic proximity of information to financial markets and impact on stock prices: evidence from the Ebola outbreak”, Proceedings of the 2016 UBT International Conference. Presented at the UBT International Conference, Durres. McKibbin, W.J. and Fernando, R. (2020), The Global Macroeconomic Impacts of COVID-19: Seven Scenarios (Working Paper No. 19/2020), CAMA Working Paper, Centre for Applied Macroeconomic Analysis, Canberra. McTier, B., Tse, Y. and Wald, J. (2013), “Do stock markets catch the flu?”, Journal of Financial and Quantitative Analysis, Vol. 48 No. 3, pp. 979-1000, doi: 10.1017/S0022109013000239. Mohren, D.C.L., Swaen, G.M.H., Kant, I., van Schayck, C.P. and Galama, J.M. (2005), “Fatigue and job stress as predictors for sickness absence”, International Journal of Behavioural Medicine, Vol. 12 No. 1, pp. 11-20. Nashikkar, A., Subrahmanyam, M.G. and Mahanti, S. (2011), “Liquidity and arbitrage in the market for credit risk”, Journal of Financial and Quantitative Analysis, Vol. 46, pp. 627-656, doi: 10.1017/ S002210901100007X. OECD (2012), “The role of MENA stock exchanges in corporate governance”, available at: https:// www.oecd.org/daf/ca/RoleofMENAstockexchanges.pdf. O’Hara, M. (2004), Liquidity and Financial Market Stability, The National Bank of Belgium, Brussels, doi: 10.2139/ssrn.1691574. Pastor, L. and Stambaugh, R.F. (2003), “Liquidity risk and expected stock returns”, Journal of Political Economy, Vol. 111, pp. 642-685. Ranaldo, A. (2001), “Intraday market liquidity on the Swiss stock exchange”, Financial Markets and Portfolio Management, Vol. 15, pp. 309-327, doi: 10.1007/s11408-001-0303-z. RBF Roll, R. (1984), “A simple measure of the implicit bid-ask spread in an efficient market”, The Journal of 13,1 Finance, Vol. 39, p. 1127, doi: 10.2307/2327617. Sansa, A.N. (2020), “The impact of the COVID-19 on the financial markets: evidence from China and USA”, Electronic Research Journal of Social Science and Humanities, Vol. 2, pp. 29-39. Sarr, A. and Lybek, T. (2002), “Measuring liquidity in financial markets”, IMF Working Paper, International Monetary Fund, Washington, DC. 66 Wagner, A.F. (2020), “What the stock market tells us about the post-COVID-19 world”, Nature Human Behaviour, Vol. 4, p. 440, doi: 10.1038/s41562-020-0869-y. Wyss, R.V. (2004), Measuring and Predicting Liquidity in the Stock Market, Universitat St. Gallen, Zurich. Xinhua (2020), “Timeline of China’s fight against the novel coronavirus”, available at: http://english. www.gov.cn/news/topnews/202003/19/content_WS5e736ce7c6d0c201c2cbef8f.html. 2020b (accessed 2 June 2020). Yan, H., Tu, A., Stuart, L. and Zhang, Q. (2020), “Analysis of the Effect of COVID-19 on the stock market and potential investing strategies”, SSRN Electronic Journal, Vol. 17. Appendix Impact of COVID-19 on Market depth Market tightness stock market AMIHUD CPQS liquidity

L.AMIHUD 0.0536*** (0.0165) – L.CPQS – 0.2318*** (0.0712) CASES_G 0.0004*** (0) 0.0024* (0.0013) 67 DEATHS_G 0.0008*** (0.0001) 0.004 (0.0025) STRINGENCY 0.00004*** (0) 0.004*** (0.0007) MKT_CAPI 0.0001*** (0) 0.003*** (0.0005) GK_VOL 0.0311*** (0.0021) 0.0502* (0.0255) R_INDEX 0.0181*** (0.0011) 0.0239 (0.0178) EX_R 0.0172*** (0.005) 0.2659* (0.144) _CONS 0.003*** (0.0003) 0.0611*** (0.0108) Observations 19,468 19,468 AR (1) test – p-value 0.000 0.000 – AR (2) test p-value 0.105 0.145 Table A1. – Hansen test p-value 0.143 0.407 Regression results Note(s): Robust standard errors are in parentheses; ***p < 0.01, **p < 0.05, *p < 0.1 using GMM estimation

RtoTR

CASES_G 13.4758** (6.3030) DEATHS_G 37.1304 (48.1102) STRINGENCY 4.1914 (3.6844) MKT_CAPI 4.0709 (3.9803) GK_VOL 1185.6553*** (422.1183) R_INDEX 177.7387 (987.5294) EX_R 1071.4432 (2,713.8191) _CONS 42.1640 (76.3734) Table A2. Observations 24,492 Regression results R-squared 0.0001 using another Note(s): Standard errors are in parentheses; ***p < 0.01, **p < 0.05, *p < 0.1 dependent variable RBF Market depth Market tightness 13,1 AMIHUD CPQS

CASES_G 0.0002*** (0.0000) 0.0009*** (0.0003) DEATHS_G 0.0002*** (0.0001) 0.0004 (0.0013) STRINGENCY 0.0000*** (0.0000) 0.0003 (0.0002) MKT_CAPI 0.0013*** (0.0002) 0.0083** (0.0039) 68 MTOB 0.0001** (0.0001) 0.0009 (0.0007) GK_VOL 0.0286*** (0.0019) 0.1170*** (0.0191) R_INDEX 0.0249*** (0.0011) 0.0055 (0.0116) EX_R 0.0147*** (0.0042) 0.4834*** (0.1462) Table A3. _CONS 0.0266*** (0.0046) 0.1761** (0.0744) Regression results Observations 24,492 24,492 using additional R-squared 0.2036 0.0112 independent variable Note(s): Robust standard errors are in parentheses; ***p < 0.01, **p < 0.05, *p < 0.1

Corresponding author Abdessamad Raghibi can be contacted at: [email protected]

For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: [email protected]