【CFRI佳作分享】公司债券市场中的交易价格聚类-上海交通大学安泰经济与管理学院.pdf

The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2044-1398.htm Stock market reactions to COVID-19 shocks: do financial market interventions walk the talk? Mutaju Isaack Marobhe Department of Finance and Accounting, Tanzania Institute of Accountancy, Dar es Salaam, United Republic of Tanzania and SSM-ESAMI Research Center, Swiss School of Management, Bellinzona, Switzerland, and Stock market reactions to COVID-19 shocks 623 Received 27 January 2022 Revised 24 March 2022 Accepted 27 April 2022 Jonathan Mukiza Peter Kansheba Department of Business Studies, Ardhi University, Dar es Salaam, United Republic of Tanzania and Department of Management, Universitetet i Agder, Kristiansand, Norway Abstract Purpose – Following the COVID-19 outbreak, various economies imposed different financial interventions as part of initiatives to cushion their stock markets from deteriorating performance. Our article examines the effectiveness of these interventions in protecting stock markets during the pandemic. Design/methodology/approach – The authors employ Panel Vector Autoregression to model the magnitude and timing of shocks from COVID-19 to stock markets. The fixed effects regression is then utilized to assess the role of financial interventions in protecting stock markets during COVID-19. The study uses daily stock index returns as well COVID-19 containment measures stringency index data from 39 countries ranging from 2nd January 2020 to 30th September 2021. Findings – Our findings firstly reveal a significant positive stock market reaction to country-level containment measures stringency but only during the first wave of COVID-19. We secondly show that stock market functioning interventions that include short selling bans and circuit breakers amplify the positive effects of COVID-19 containment measures stringency on stock market performance. Research limitations/implications – The authors stress the need for policymakers and regulators to timely intervene in protecting economies and stock markets during crises such as COVID-19 in order to reduce panic among investors. Moreover, investors should adjust their portfolios by investing in stocks from countries that have proper financial market interventions in place. Originality/value – Despite growing body of literature on COVID-19 and stock market performance, there is limited evidence on the role of financial sector interventions to cushion stock markets during tumultuous conditions caused by the pandemic. Keywords Stock market performance, COVID-19 pandemic, Financial market interventions Paper type Research paper 1. Introduction Stock markets across the globe have experienced increasing volatility and significant negative returns since the outbreak of COVID-19 (Zhao et al., 2022; Uddin et al., 2021; Ashraf, 2020; Zhang et al., 2020; Baek et al., 2020; Marobhe, 2022). Some major stock indices such as Dow Jones Industrial Average, Nikkei, FTSE 100 and Shanghai Composite index have exhibited a downward trend with average drops ranging between 24 and 33% from late December 2019 to late March 2020 (Hui and Chan, 2022). This may be attributed to the unprecedented levels of uncertainties due to lockdowns and other social distancing measures (Baig et al., 2021). These conditions make forecasting of asset prices during COVID-19 difficult (Ashraf, 2021). China Finance Review International Vol. 12 No. 4, 2022 pp. 623-645 © Emerald Publishing Limited 2044-1398 DOI 10.1108/CFRI-01-2022-0011 CFRI 12,4 624 Despite rising volatility in global stock markets during COVID-19, the effect has been unparallel among regions. The major Asian stock markets have shown more resilience than any other markets while those in Latin American being hit the hardest (Szczygielski et al., 2021). These disparities can be explicated by variations in financial market interventions by respective countries’ governments to restore confidence and stability in the financial system during crisis (The World Bank, 2021). These interventions were also used in the global financial crisis (2008) (Yacine et al., 2009). They include market functioning interventions in the form of bans on short selling of securities, extension of the deadlines for disclosure of financial statements of investment companies and cancellation of listing tariffs on corporate bonds (The World Bank, 2021). Moreover, the interventions also include public debt management (PDM) which can take the form of issuing special anti-pandemic government bonds, relaxing conditions for issuance of Treasury bills and injecting funds to support wholesale funding markets used by small lenders (The World Bank, 2021). We draw the motivation for this study to inform policymakers, investors and regulators on the effectiveness of financial market interventions in cushioning stock markets against adversity caused by COVID-19. Stock markets’ volatility during major crises rises as a result of panic selling among investors as well as continuance of short-selling (Taleb, 2007; Ho, 2021). So it is vital to understand whether interventions such as bans on short selling by regulators and injection of liquidity may reduce deteriorating stock market performance during crises by restoring investors’ confidence. Our paper intends to contribute to the existing knowledge on three folds. Firstly, we use Panel Vector Autoregression (PVAR) to examine the timing and magnitude of shocks from COVID-19 to stock market returns. This provides further evidence to supplement results from previous studies (Marobhe, 2021; Ashraf, 2020; Marobhe and Dickson, 2022). Secondly, we use individual countries’ COVID-19 containment measures stringency index to examine COVID-19’s impact on stock markets (Ashraf, 2021). This index is instrumental in measuring daily stringency of COVID-19 containment measures such as extent of lockdowns and other social distancing measures (Hale et al., 2021). Thirdly, we contribute to the current literature by showing the extent at which financial market interventions have helped to reduce rising stock markets volatilities during COVID-19 (The World Bank, 2021). The article proceeds as follows. Section 2 presents the discussion on stock market performance in times of crisis. It also discusses the moderation effects of different financial market interventions. The section also provides for hypotheses development. Section 3 provides for the employed methods while section 4 presents the findings and discussion. Section 5 presents the implications and avenues for future research and section 6 covers conclusions. 2. Literature review and hypotheses development 2.1 Stock market reactions in the past pandemics The mechanics of stock market performance during crisis can be sourced from the famous Black Swan Theory (Taleb, 2007). The theory posits that the occurrence of unexpected events such as financial crises pandemics, accidents, natural disasters, terrorism may positively or negatively impact stock market (Spelta et al., 2019; Valizadeh et al., 2017; Memdani and Shenoy, 2019; Scholtens and Boersen, 2011). The impact that these events have on stock markets is usually severe as explicated by their unpredictability (Del Giudice and Paltrinieri, 2017). With reference to past health crises, Chen et al. (2018) portray that shocks caused by SARS-CoV took a chunk of stock values in China and spilled over to other South East Asian stock markets which supports findings of earlier studies such as (Bhuyan et al., 2010; Nippani and Washer, 2004; De Lisle. 2003). Similarly, the fear created by the Ebola virus in West Africa negatively impacted investors’ sentiments resulting into plummeting stock prices for US companies and mutual equity funds operating in the region (Ichev and Marinc, 2018; Del Giudice and Paltrinieri, 2017). This impact resembles that brought by influenza outbreak in United States resulting into dwindling trading volume and higher bid-ask spreads (Mc Tier et al., 2013). Further evidence seems to suggest that stock markets in major Latin America economies were adversely affected by the outbreak of Zika virus with Brazil suffering a relatively larger impact (Macciocchi et al., 2016). However, unlike SARS-CoV, Ebola and MERS-CoV which had regional effects, the current COVID-19 pandemic has impacted all regions around the globe which makes its impact more severe (The World Bank, 2020a). 2.2 Stock markets during the COVID-19 pandemic 2.2.1 Stock market performance disparities between regions. Literature on stock markets reactions during the current COVID-19 has put forward evidence to indicate increasing volatility during the pandemic (Ashraf, 2021; Zhang et al., 2020; Baek et al., 2020). However, evidence points toward disparities in stock market performance among countries during the prevailing pandemic. Zhao et al. (2022) depict that stock markets in developed countries have suffered immensely as opposed to those from developing countries due to supply reduction, demand reduction and economic instability. Moreover, investors in developed and emerging economies reacted differently to COVID-19 in both the pre-April 2020 period (rising infections) and post April 2020 period (stabilizing) (Harjoto et al., 2021). Liu et al. (2020a) narrate that stock indices in South East Asian economies were hit the hardest during the first outbreak of COVID-19. However, later evidence showed that Asian stock markets recovered and remained resilient to further COVID-19 shocks (Szczygielski et al., 2021). This is supported by recent studies such as (Hui and Chan, 2022) that show how stock markets in Europe have been more volatile than those in South East Asia during the pandemic. Given disparities among studies pertaining to stock market performance dynamics in different regions we hypothesize that H1. There are significant differences in stock market performance between regions during COVID-19 2.2.2 Stock market performance and COVID-19 containment measures stringency. The literature on how different government containment measures such as lockdowns, closure of schools affect stock markets has been gradually growing. This has created two opposing schools of thought on the subject with each group advocating for either improving or deteriorating stock market performance (Deng et al., 2021). The advocates for improved stock market as a result of COVID-19 containment measures imposition argue that growing number of cases and deaths causes panic among investors resulting into panic selling (Aggarwal et al., 2021; Haroon and Rizvi, 2020 Deng et al., 2021). Therefore imposition of measures such as lockdowns and cancellation of public events is instrumental in slowing down the spread of COVID-19 and eventually reduce fatalities (Haroon and Rizvi, 2020). This helps to reduce panic among investors which inhibits their propensity to engage in panic selling of stocks thus improving stock market performance (Aggarwal et al., 2021). On the other hand, imposition of containment measures has the potential to cause economic slowdown which may eventually drive the economy into recessions (Bauer and Weber, 2021; Baig et al., 2021). Deteriorating economic conditions can thus create fear and panic among investors with the potential to increase stock market volatility. Due to the economic recessions experienced during COVID-19 and disruptions in activities of key economic sectors such as manufacturing and transportation we develop the following hypotheses; H2. There are significant differences in COVID-19 containment measures stringency between regions. Stock market reactions to COVID-19 shocks 625 CFRI 12,4 626 H3. COVID-19 measures stringency has negative effect on stock market performance. 2.3 The moderation role of financial market interventions during COVID-19 The financial sector is the key organ to assist countries repel the adverse effects of crisis on the economy hence accelerate recovery (The World Bank, 2020a). Since COVID-19 was declared as a pandemic, governments worldwide have intervened by creating financial policies intended to mitigate the adverse effects of the pandemic on the economy (International Monetary Fund, 2020). These particular policies have been imposed during crises to restore economic stability through crucial economic drivers such as the financial sector (Cho, 2010). Since COVID-19 has run roughshod over economies, financial policy interventions have been enacted to provide liquidity to financial institutions and help maintain financial markets stability hence restoring investors’ confidence (The World Bank, 2020a). One particular type of these interventions is the financial market interventions. These are put in implemented by governments to restore confidence in the financial markets by stabilizing them during crisis (IMF, 2020). There are two main groups of financial market interventions that have been done by governments during COVID-19 namely, marketing functioning interventions and PDM (The World Bank, 2021). 2.3.1 Market functioning interventions. These interventions intend to change various financial market regulations amid crisis in order to reduce panic thus restore confidence in the market. Some of the commonly used one during COVID-19 includes bans on short selling of securities. Evidence suggests that short selling trading strategy can increase securities price volatility even during normal market conditions making the ban amid COVID-19 necessary (Ho, 2021). The other intervention is extension of the deadlines for disclosure of audited financial statements of investment companies. This is due to the uncertainties surrounding COVID-19 which may inhibit these companies from timely preparing materially correct financial statements for disclosure to investors (IOSCO, 2020). Thus deadline extension provides time for investment companies to assess the conditions amid COVID-19 provide investors with informed disclosures on business continuity. Furthermore, other governments cancelled tariffs on issuance of corporate bonds to encourage companies to raise finance and stay afloat during the pandemic. We therefore hypothesize that; H4. Market functioning interventional strategy has a positive moderating effect on the relationship between COVID-19 measures stringency and stock market performance. 2.3.2 Public debt management interventions. These firstly include injection of funds to support wholesale funding markets used by smaller lenders, including non-bank lenders. The second form of these interventions involves strengthening liquidity in currencies by extending groups of institutions with access to auctions and to the liquidity window of the Central Bank. This is by extending access to public debt instruments by including pension and severance fund organizations. The other intervention is establishing temporary financing facilities for commercial banks that will be guaranteed by credits to corporations that issue bonds. These finances are intended to be channeled to micro, small- and medium-size enterprises (SMEs) to assist them during the pandemic (The World Bank, 2021). We therefore hypothesize that; H5. PDM interventional strategy has a positive moderating effect on the relationship between COVID-19 measures stringency and stock market performance. 3. Methods 3.1 Data Our study employed a global dataset from 2nd January 2020 to 30th September 2021 of 39 economies distributed across six regions namely: Africa, Asia, Europe, South America, North America and Oceania. Our sample covered 39 countries alone because these had complete stock market and financial market interventions data for the selected timeframe. All countries with gaps in their data were dropped from the dataset to ensure robust analyses. The selection of the timeframe from 2nd January 2020 to 30th September 2021 was done to capture the effects of the first and second wave of COVID-19. Our timeframe ended at 30th September 2021 as this was the last date that the World Bank reported on financial market interventions by individual countries (The World Bank, 2021) thus making the extension to 31st December 2021 impractical. We explore two main sub-periods; the first is the period from 2nd January 2020 to 31st November 2020 which marks the first wave of COVID-19 caused by the Alpha variant. This includes the period of rapid surge in cases from January to June 2020 and the period from July 2020 to November during which infections were falling. The second period ranges from 1st December 2020 to 30th September 2021 which marks the second wave of COVID-19. In December another more contagious variant of COVID-19 namely Delta was discovered in India and managed to spread across the globe causing a sudden surge in infections and deaths especially in March 2021. The descriptive statistics for the variables incorporated in our dataset are presented in Table 1. The results show that the stock market performance during the studied timeframe averaged at about 0.04%. The average COVID-19 containment measures stringency has been reported to be 58.4% indicating that several countries implemented some degree of containment measures such as lockdown and social distancing. The results also indicate significant disparities among countries in terms of COVID-19 deaths and cases as revealed by the high standard deviation for the two variables. This may be caused by containment measures stringency differences between different countries. Regarding the financial market interventional strategies, the results show that market functioning strategy was employed on about 1% of the studied observations (periods) while PDM being employed on about 1.5% of the studied observations. The average GDP growth was negative indicating decreasing output in most economies since the outbreak of the virus as elucidated by disruption in main economic activities. The mean interest rate was below 5 despite some countries reporting double digit inflation rates during COVID-19 as shown by the maximum value for the variable. Variable Obs Mean Std. dev. Min Max Stock returns COVID-19 measures stringency Market functioning Public debt management N. culture I. freedom Lag. returns COVID-19 cases COVID-19 deaths Inflation Interest GDP growth Number of countries Source(s): Own compilation (2022) 16,310 16,310 16,310 16,310 16,310 16,310 16,310 16,310 16,310 16,310 16,310 16,310 39 0.044 58.427 0.007 0.001 57.277 68.909 0.044 1,680,154 37,062 3.029 2.946
0.209 1.7938 19.417 0.083 0.035 24.353 17.308 1.794 4,819,559 90,633 7.493 6.630 5.740
98.997 0 0 0 8 20
98.997 0 0
2.570
0.810
11.250 13.909 100 1 1 100 90 13.909 43,500,000 699,634 48 38 18.300 Stock market reactions to COVID-19 shocks 627 Table 1. Descriptive statistics CFRI 12,4 628 3.2 Variables 3.2.1 Dependent variable. Stock market Performance: This is measured by daily stock returns of respective stock market indices. This variable and its respective measurement have also been used by recent studies such as (Baek et al., 2020; Al Awadh et al., 2020). The returns are computed as follows: Return on Dayone ¼ ðReturn on Dayone
Return on Day0 Þ=Return on Day0 3.2.2 Independent variables. COVID-19 measures stringency: this variable is compiled by the Oxford Corona Virus Government Response Tracker (OxCGRT). It is a composite measure that incorporates nine of the response metrics. These are school closures, workplace closures, cancellation of public events, restrictions on public gatherings, closures of public transport, stay-at-home requirements, public information campaigns, restrictions on internal movements and international travel controls. The index is computed on daily basis as an average score of the nine metrics with each ranked between 0 and 100. The highest score of 100 indicates a strictest policy on a particular day. 3.2.3 Moderating variables. We employ two moderating variables representing financial market specific interventions: (1) Market functioning and (2) PDM. The country’s financial market interventions to stabilize the financial system can steer/deter the effects of COVID-19 containment measures on stock market performance through restoring investors’ confidence (The World Bank, 2021). 3.2.4 Control variables. To ensure results robustness we controlled for COVID-19 outbreak effects using the number of COVID-19 cases and deaths reported for respective countries. Recent studies such as (Ashraf, 2020: Liu et al., 2020a) have studied the impact of the mentioned variables on investors’ behavior. We further control the effects of the following variables on stock market performance: National Culture (Ashraf, 2021), Lag of stock returns (Irshad, 2017), Real GDP growth (Ramraika, 2015), Interest (Martinez et al., 2020), Inflation rate (Otieno et al., 2019) and Investment Freedom (Ashraf, 2021). The descriptions of each variable are presented in Table 2. 3.3 The empirical models 3.3.1 Panel vector autoregression (PVAR) model. We firstly employed the Panel Granger noncausality test to evaluate the potential of COVID-19 variables namely number of cases, number of deaths and policy stringency to cause stock returns. This test is part of VAR models that is instrumental in examining the power of one variable to forecast the other. The Granger causality (Dumitrescu and Hurlin, 2012) approach that is designed to deal with heterogeneous panel data was utilized in this particular case. Secondly, we utilized the PVAR model to examine the impact of shocks from countries’ number of cases, number of deaths and policy stringency on stock returns using impulse response functions (IRFs). IRFs are paramount to panel VAR as they visualize the magnitude and timing of shocks from one variable to the other. The IRFs are generated using Cholesky decomposition of the variance– covariance with orthogonalized shocks (Holtz–Eakin et al., 1988). Panel VAR is well suited to handle high-frequency data similar to the ones used in this study as well as treating variables endogenously by accounting for unobserved individual heterogeneity (Love and Zicchino, 2006). The magnitudes of cases, deaths and containment measures stringency since the first outbreak of COVID-19 have been changing over time. PVAR through IRFs is better suited to analyze and visualize the differences in shocks transmission from each COVID-19 variable to stock returns in different time periods. This is unlike other methods such as regressions which only analyze the significance and direction of relationships between variables without considering the changes in the magnitude and direction of the relationship in different time periods. Therefore PVAR captures both static and dynamic interdependencies between Variables Description Sources Stock market performance (returns) COVID-19 measures stringency (CoMS) Market functioning (MFN) Daily closing stock returns from 2nd January 2020 to 30th September 2021 https://www.investing.com 0 if the country has the leniest policy and 100 if the country has the strictest policy 1 if the country employed market functioning strategy, 0 otherwise Public debt management (PDM) 1 if the country employed market PDM strategy, 0 otherwise National culture (N. culture) Lag. returns COVID-19 cases (cases) COVID-19 deaths (deaths) Inflation Measured by country’s uncertainty avoidance index ranging from 0 to 100. The higher the score, the more the panic and discomfort people have with uncertainties An index that ranges between 0 and 100. It measures stock market liberalization including the extent of foreign investors’ participation in local stock market The lag of the stock returns New daily COVID-19 cases per million people New daily COVID-19 deaths per million people Quarterly inflation rate https://ourworldindata.org/covidstringency-index https://datacatalog.worldbank.org/ dataset/COVID-19-finance-sectorrelated-policy-responses https://datacatalog.worldbank.org/ dataset/COVID-19-finance-sectorrelated-policy-responses https://www.hofstede-insights.com/ product/compare-countries/ Interest rate Quarterly interest rate GDP growth Quarterly GDP growth rate Investment freedom (I. freedom) Stock market reactions to COVID-19 shocks 629 https://www.heritage.org/index/ download https://www.investing.com https://ourworldindata.org/coviddeaths https://ourworldindata.org/covid-cases https://tradingeconomics.com/countrylist/inflation-rate https://tradingeconomics.com/countrylist/interest-rate https://tradingeconomics.com//countrylist/gdp Source(s): Own compilation (2022) variables across time (Canova and Ciccarelli, 2013) thus provide robust analyses given the changing nature of COVID-19 pandemic. We specify the following Panel VAR model as postulated by Love and Zicchino (2006); Srit ¼ Γ0 þ Γ1 sri;t−1 þ vi þ εit iεf1; 2; . . . ; Ng; t εf1; 2; . . . ; Tg where (1) Srit5Vector of the variables COVID-19 cases, deaths, containment policy stringency and stock market returns; (2) Γ0 and Γ1 5 the matrix of our parameters; (3) the vectors of country-specific panel fixed effects; (4) Ɛit 5 the error term which is assumed to be independently and identically distributed with constant variance and zero mean. 3.3.2 Fixed effects regression model. We specify the following fixed effects regression model for empirical analyses; Table 2. Variable descriptions Yc;d ¼ αc þ β1 ðCOVID
19MSc;d 3 Market Functioningc Þ þ β2 ðCOVID
19MSc;d 3 Public Debt Managementc Þ CFRI 12,4 þ β3 ðCOVID
19MSc;d Þ þ β4 ðMarket Functioningc Þ þ β5 ðPublic Debt Managementc Þ þ k X βk Xck þ έc;d k¼0 630 where Y 5 The dependent variable in this case stock market performance; c 5 Country; d 5 Time in days; αc 5 A constant term; β 5 Coefficient of independent/moderating variables; COVID19MSc,d 5 COVID-19 measures stringency for a given country in a given day; COVID-19MSc, d 3 Market Functioningc 5 the first interaction term which means the effects of country’s COVID19 measures stringency on stock market performance is contingent upon the imposition of the market functioning financial market interventions; COVID-19MSc, d 3 Public Debt Managementc 5 the second interaction means which means the effects of country’s COVID-19 measures stringency on stock market performance is contingent upon the imposition of the public debt management financial market interventions; Xck 5 a set of country level control variables that include national culture, investment freedom, inflation rate, interest rate and real GDP growth rate; έc,d 5 Error term. 3.4 Pre-estimation diagnostics 3.4.1 Panel VAR diagnostics. We carried out the panel unit root test to check for stationarity of the variables. We employed the Fisher unit root test based on Augmented Dickey–Fuller (ADF) for panel data using the four methods put forward by Choi (2001). This test works well with unbalanced panel data such as stock return data in our case which usually contain gaps, e.g. due to presence of non-trading days. The results from Table 3 indicate that all the variables do not contain the unit root as illustrated by their p-values of less than 0.05 for each of the four methods. 3.4.2 Regression model goodness-of-fit and estimation. We mainly postulate that the COVID-19 measurement stringency negatively affects the stock markets’ performance in terms of their returns. However, we further hypothesize the moderation role of financial market-specific interventional strategies namely market functioning and PDM as in securing the financial systems. The panel regression model was employed to examine the stated Variable Inverse Chi-squared (78) Returns 2805.60* CoMS 372.90* MFN 2055.13* PDM 573.39* N. Culture 346.25* I. Freedom 396.17* Lag. Returns 134.49* Cases 168.24* Deaths 156.00* Inflation 532.71* Table 3. Interest 948.38* Panel Fisher-type unit 146.79* root test based on ADF GDP growth Note(s): *Significant at 0.05 results Inverse normal Inverse logit (199) Modified inv. Chi-squared
49.91*
11.00*
41.27*
17.49*
9.56 12.75*
7.57*
3.71*
3.64*
15.08*
23.65*
3.12*
106.97*
12.65*
78.35*
21.56*
10.23 14.15*
9.59*
3.51*
3.38*
19.72*
35.71*
2.98* 204.85* 20.57* 148.00* 35.75* 18.96 21.87* 3.96* 5.06* 4.14* 32.67* 64.16* 3.44* relationships (Bell et al., 2019). The Hausman test results suggested the fixed effects (FE) estimator to be appropriate over the random effects (RE) estimator (Lensink et al., 2017). Prior further analyses we ensured for goodness-of-fit of the model by testing several regression assumptions (Kansheba and Marobhe, 2021). Appendix 1 provides for the summary of tested assumptions gauging the goodness-of-fit of the model. The Breusch–Pagan test results show the p-value of 0.0647 greater than the cutoff point of 0.05 indicating the absence of heteroskedasticity problem (Hausman and Taylor, 1981). The Pearson–wise correlation matrix (see Appendix 1) shows that all independent variables have the value below the cut-off point of 5, suggesting the absence of serious multicollinearity problem (Kansheba, 2020). Additionally, the variance inflation factor-(VIF) test was performed and confirmed the absence of multicollinearity problem where explanatory variables have lower VIF value below the cut-off point of 5 (Studenmund, 2011). However, the variables cases, deaths, inflation and interest rate were excluded from the analyses due to high VIF above the cut-off point. The link test for model specification results shows the p-value of 0.766 greater than 0.05 suggesting that the model is correctly specified (Lensink et al., 2017). The explanatory variables explain about 38% (R-squared-within) of the variation in the outcome variables. 4. Results 4.1 Pairwise correlations results We commenced our analyses by analyzing correlations between our variables as shown in Table 4. The results reveal significant correlation between containment policy stringency and stock returns. The number of cases and deaths were not significantly correlated with stock returns during COVID-19. The number of cases, deaths and CoMS are also significantly correlated with each other given their inherent interdependent nature. The results further reveal strong correlation between COVID-19 containment policy stringency and number of deaths and cases. This may provide some evidence to indicate the fact that the number of cases and deaths is influenced by strictness of individual countries’ COVID-19 containment measures. 4.2 ANOVA and post hoc ANOVA results We conducted the ANOVA and post hoc ANOVA to examine disparities among regions in terms of returns and containment measures stringency in both waves of COVID-19. The results are presented in Table 5 and they present evidence of non-significant differences in stock market performance during the first and second wave of COVID-19. However, the results reveal significant differences between regions in terms of containment measures stringency (CoMS) in both waves. Therefore, we present evidence to reject hypothesis 1 (H1) and accept hypothesis 2 (H2). We then proceeded to carry out post hoc ANOVA to reveal inter-regional differences in containment measures stringency since the overall differences were significant in both waves of COVID-19. During the first wave, Asian and European containment measures were stricter than those in Africa and Latin America. Furthermore, Oceania and North America containment measures stringency differed from Latin America. During the second wave of COVID-19, containment measures in Asia, Europe, Latin America and North America were stricter than those in Africa. Disparities were also observed between Latin America, North America and containment measures in Asia and Oceania. 4.3 Panel Granger causality results We commenced our Panel VAR modeling by firstly examining causality between stock returns and each of the three COVID-19 variables which are containment measures Stock market reactions to COVID-19 shocks 631 Table 4. Pairwise correlation results (1) Returns 1.00 (2) Cases 0.01 (3) Deaths 0.01 (4) CoMS 0.10* (5) Inflation 0.01 (6) Interest 0.01 (7) GDP growth 0.01 (8) MFN
0.01 (9) PDM
0.01 (10) Lag. Returns 0.03* (11) N. culture
0.02 (12) I. freedom
0.03 Note(s): *Significant at 0.05 (1) 1.00 0.94* 0.06* 0.07* 0.03* 0.32*
0.02
0.01 0.01 0.03*
0.05 (2) 1.00 0.06* 0.07* 0.03* 0.29*
0.02*
0.01 0.01 0.09*
0.02* (3) 1.00 0.15* 0.15* 0.05* 0.05* 0.04* 0.10* 0.08*
0.12* (4) 1.00 0.97* 0.02* 0.04*
0.03 0.01 0.22*
0.26* (5) 1.00
0.03* 0.05* 0.01 0.01 0.25*
0.32* (6) 1.00
0.08*
0.01 0.01
0.16*
0.11* (7) 1.00 0.04*
0.02* 0.03*
0.03* (8) 1.00 0.02*
0.02*
0.05* (9) 1.00
0.02
0.02 (10) 632 Variables 1.00
0.19* (11) 1.00 (12) CFRI 12,4 Returns CoMS ANOVA F-stat p-value F-stat p-value Region (1st Wave) Region (2nd Wave) 0.190 0.596 0.967 0.703 25.54 48.6 0.000* 0.000* Post Hoc ANOVA Containment measures stringency First wave Second wave Contrast p-value Contrast p-value Regions Asia–Africa Europe–Africa Latin America-Africa North America–Africa Oceania-Africa Europe–Asia Latin America-Asia North America–Asia Oceania-Asia Latin America-Europe North America–Europe Oceania-Europe North America-Latin Oceania-Latin America Oceania-North America Note(s): *Significant at 0.05
6.723
6.851 2.326
4.053
3.709
0.128 9.049 2.671 3.014 9.177 2.799 3.143
6.378
6.034 0.344 0.000* 0.000* 0.577 0.095 0.363 1.000 0.000* 0.199 0.401 0.000* 0.135 0.336 0.000* 0.008* 1.000 9.628 9.034 13.576 13.549 9.663
0.594 3.949 3.922 0.036 4.543 4.516 0.630 2.719
3.913
3.886 0.000* 0.000* 0.000* 0.000* 0.000* 0.648 0.000* 0.000* 1.000 0.000* 0.000* 0.992 1.000 0.017* 0.031* Stock market reactions to COVID-19 shocks 633 Table 5. ANOVA and post hoc ANOVA stringency, number of cases and number of deaths. The Panel Granger non-causality test using the (Dumitrescu and Hurlin, 2012) approach results are presented in Table 6. The results reveal that number of COVID-19 cases and deaths do not ranger cause stock returns. Containment measures stringency however appears to Granger cause stock returns during COVID-19 which provides evidence to demonstrate the importance of this variable in forecasting stock returns. These results are supported by preliminary evidence of significant correlation between policy stringency and stock returns. 4.4 Panel VAR modeling results Our panel VAR modeling commenced with the selection of the lag length. The results presented in Table 7 show a lag length of one day for all the three models and moments selection criteria (MMSC) namely: Bayesian Information Criterion (BIC), Akaike Information Criterion (AIC) and Hannah and Quinn Information Criterion (HQIC). We then continued to carry out an important stability test prior to panel VAR modeling. The results are visualized in Figure 1 and signify satisfaction of the stability conditions for the postulated relationships in our Panel VAR model. This can be explained by the Eigen values which are all within the unit circles. Returns W-bar Z-bar Z-bar tilde Cases Deaths CoMS Note(s): *Significant at 0.05 0.5849 0.5468 5.5354
0.0566
0.2117 21.7993*
0.0646
0.2169 19.7548* Table 6. Panel Granger noncausality test results CFRI 12,4 634 After selecting the lag length and satisfying the stability condition, we conducted Panel VAR modeling. The results are presented in Figure 2 and they reveal variations among COVID-19 containment policy stringency, number of cases and deaths in causing stock returns shocks. Figure 2 specifically present orthogonalized IRFs of stock returns resulting from shocks originating from COVID-19 containment policy stringency with 95% confidence interval. The timeframe for IRFs is divided into seven main quarters starting from January 2020 ending on September 2021. The results firstly reveal a linear but negative response of stock returns to a shock from COVID-19 deaths. Secondly, shocks from COVID-19 cases also appear to have a linear negative impact on stock returns during the entire timeframe. On the other hand, COVID-19 containment measures stringency shocks caused positive linear shocks in returns as opposed to cases and deaths. These results provide early evidence to indicate rejection of hypothesis 3 (H2) which postulates a negative impact of COVID-19 containment measures on stock market performance. 4.5 Fixed effects regression results We lastly conducted the fixed effects (FE) regression to examine the moderation role of the financial market interventions on the relationship between COVID-19 measures stringency and stock market performance during both waves of COVID-19. Model 1 in each analysis is the base line model comprised of independent variables (CoMS), control variables and the dependent variable returns. Under model 2, the moderating variable, i.e. market functioning (MFN) or PDM, is added to the regression model. Lastly, the interaction variables namely (CoMS 3 MF and CoMS 3 PDM) are each added to its respective model for final analysis. Table 8 presents FE results for the moderation role of market functioning interventions on the relationship between COVID-19 containment measures stringency and stock market performance. The results firstly reveal a statistically significant positive impact of COVID-19 containment measures stringency on stock returns in both waves of COVID-19 as shown in models 1. We thus reject hypothesis 3 (H3) which postulates the negative relationship between the two variables. In models 2 and 3 for both the first and second wave of COVID-19, we introduce the moderating role of market functioning interventions. We observed a statistically significant positive moderation role of market functioning interventions on the relationship between COVID-19 containment measures and returns during the first wave of COVID-19 alone. This signifies the fact that imposition of market functioning interventions amplifies the positive effects of COVID-19 containment measures on stock market performance. We therefore do not reject hypothesis 4 (H4) for the first wave alone while we reject H4 for the second wave of COVID-19. We then examined the moderation role of PDM interventions on the relationship between COVID-19 containment measures stringency and stock market performance. The FE results are presented in Table 9 and they reveal a non-significant moderation role of PDM in both the first and second wave of COVID-19. We thus reject hypothesis 5 (H5) that postulates a significant moderation role of PDM on the relationship between containment measures stringency and stock market performance. Lag Table 7. Lag length selection results CD J BIC 1 0.9782
14742.85
13107.23* Note(s): *Lag length selection based on the three (3) criteria AIC HQIC
28987.29*
67238.67* Roots of the companion matrix Stock market reactions to COVID-19 shocks 1.0 Imaginary 0.5 635 0.0 –0.5 –1.0 –1.0 –0.5 0.0 Real 0.5 1.0 Note(s): The PVAR model is stable when all three (3) dots lie within the circle 5. Discussions In this article, we examine the effects of the novel COVID-19 on stock market performance and the role that different financial market interventions play in protecting stock markets. We firstly do not find evidence of significant differences between regions in terms of stock market performance during both waves of COVID-19 as opposed to (Hui and Chan, 2022; Szczygielski et al., 2021; Harjoto et al., 2021). We also provide evidence to demonstrate the positive impact of COVID-19 containment measures stringency on stock market performance during the first wave of COVID-19 only. Our results support those by Aggarwal et al. (2021), Haroon and Rizvi (2020), Deng et al. (2021) that depict positive stock market reaction to COVID-19 containment measures. These results signify the fact that investors view containment measures as necessary steps toward slowing down the virus and therefore help economies to bounce back from COVID-19-induced recessions. We also demonstrate a significant and positive moderation role of market functioning interventions in further amplifying the positive role of COVID-19 containment measures stringency on stock market performance. However, this was observed during the first wave of COVID-19 as prolonged containment measures can slowly diminish investors’ hopes of economic recovery in the long run, i.e. during the second wave (Bouri et al., 2021). Evidence suggests that market functioning interventions enable investors to reduce fear and panic amid bullish conditions which reduces their propensity to engage in panic selling of stocks (Chen et al., 2005). This is by being able to receive and absorb news which improves their inclination toward making more informed trading decisions. On the other hand, effects of countries’ PDM interventions on stock market performance were observed to be weak during COVID-19. PDM strategies may influence stock market performance but not as direct and instantaneously as market functioning strategies. This is attributed to Figure 1. PVAR stability test results CFRI 12,4 12,50,000 10,00,000 7,50,000 5,00,000 2,50,000 636 0 12,50,000 10,00,000 7,50,000 5,00,000 2,50,000 0 12,50,000 10,00,000 7,50,000 5,00,000 2,50,000 0 12,50,000 10,00,000 7,50,000 5,00,000 Figure 2. Panel VAR impulse response functions results 2,50,000 0 1.0 1.5 2.0 2.5 3.0 3.5 4.0 1.0 1.5 2.0 2.5 3.0 3.5 4.0 1.0 1.5 2.0 2.5 3.0 3.5 4.0 1.0 1.5 2.0 2.5 3.0 3.5 4.0 steps Returns Variables Containment measures stringency Market functioning Containment measures stringency 3 market functioning interventions Table 8. Investment freedom FE estimates for the National culture linkage between GDP growth COVID-19 containment measures stringency, Lag of returns Constant market functioning interventions and stock R-squared No. of observations market performance: Note(s): *Significant at 0.05 FE estimates COVID-19 (first wave) Model 1 Model 2 Model 3 COVID-19 (second wave) Model 1 Model 2 Model 3 0.014* – – 0.015*
0.315 – 0.014*
1.453* 0.017* 0.002* – – 0.002* 0.191 – 0.002** 0.638
0.006 0.002
0.001 0.008
0.044*
0.835* 0.19 8,086 0.002
0.001 0.007
0.044*
0.835* 0.19 8,086 0.002
0.001 0.008
0.044*
0.829* 0.38 8,086
0.000 0.000
0.008*
0.0459* 0.026 0.13 8,220
0.000 0.000
0.008*
0.046* 0.027 0.14 8,220
0.000 0.000
0.008*
0.046* 0.027 0.14 8,220 the fact that they are imposed to inject liquidity in the overall financial systems predominantly through financial institutions (The World Bank, 2021). However, market functioning strategies such as bans on short selling are imposed specifically to instantly alter securities trading in the markets thus leading to immediate changes in volatility (Zhang et al., 2015). Our findings appear to be in contrast to those of (Beber and Pagano, 2013; Bohl et al., 2012) which showed the insignificancy of market functioning interventions such as short selling bans to reduce stock volatilities during the GFC of 2008. Boehmer and Wu (2009) depict that interventions of this sort causes deterioration in market liquidity and inhibit price discovery as investors exit from the market. Other studies such as (Eom et al., 2021) have revealed that these interventions neither increased volatility nor reduced liquidity during the GFC of 2008 which questions their relevance. However the GFC of 2008 was foreseen as the US economy had already exhibited structural problems leading to the crisis (Li et al., 2021). On the other hand, COVID-19 pandemic could not be predicted and its economic effects have been far devastating than that of the GFC of 2008. This can possibly explain disparities between our findings and those of previous studies that covered the GFC of 2008. A crucial caveat for understanding our findings is based on the premise that COVID-19 has caused deterioration in stock market indices around the world which is a common occurrence during crises. However, the adverse effects are less pronounced in those countries with stricter containment measures and higher magnitude of market functioning interventions. Our findings are profound in relation to those of past studies (Zhang et al., 2020; Baek et al., 2020; Szczygielski et al., 2021) that depict a direct relationship between COVID-19 cases, fatalities or government measures and stock market performance. We provide robust evidence to support findings by Uddin et al. (2021); Ashraf (2021), who have also postulated the influence of other factors/moderators for instance economic strength and national culture on the strength and direction of relationship between COVID-19 and stock market performance. Stock market reactions to COVID-19 shocks 637 6. Conclusions, implications and avenues for future research 6.1 Conclusions In this article, we investigate the impact of daily country-level COVID-19 containment measures stringency on performance of stock markets from 39 economies across the globe. We moderate this relationship using specific financial market interventions imposed by Returns Variables Containment measures stringency Public debt management Containment measures stringency 3 public debt management Investment freedom National culture GDP growth Lag of returns Constant R-squared No. of observations Note(s): *Significant at 0.05 COVID-19 (first wave) Model 1 Model 2 Model 3 COVID-19 (second wave) Model 1 Model 2 Model 3 0.014* – – 0.014*
0.275 – 0.014* 4.676
0.063 0.002* – – 0.002* 2.902 – 0.002
0.001 0.008
0.044*
0.835* 0.19 8,086 0.002
0.001 0.008
0.044*
0.830* 0.21 8,086 0.002
0.001 0.008
0.044
0.829 0.21 8,086
0.000 0.000
0.008*
0.046* 0.026 0.13 8,220
0.000 0.000
0.007*
0.044* 0.024 0.13 8,220 0.002* 2.815 0.037 Table 9.
0.000 FE estimates for the 0.000 linkage between
0.007* COVID-19 containment
0.044* measures stringency, 0.023 public debt 0.13 management 8,220 interventions and stock market performance CFRI 12,4 638 countries to reduce volatility of stock prices which suffered immensely during COVID-19. Unlike previous studies, we use COVID-19 stringency index as a proxy for COVID-19 measure because the economic effects of COVID-19 are a result of measures imposed by governments for instance lockdowns and social distancing measures. Thus, we opine that the pandemic’s impact on stock markets is well understood by focusing on stringency of country’s COVID-19 containment measures. Our findings firstly portray a significant positive impact of COVID-19 measures stringency on stock market performance. We further show that the improvement in stock market performance due to changes in COVID-19 measures stringency is stronger in countries with higher magnitude of market functioning interventions. 6.2 Theoretical implications Over recent decades different regions have been rocked with pandemics such as SARS-COV, MERS-COV and Ebola which have had social and economic repercussions. The current COVID-19 pandemic is different from the past health crisis as explained by the magnitude of spread and severity of its economic repercussions on the global economy. These events have had adverse effects on stock markets around the globe due to elevated panic and fear among market participants. Our findings have enormous theoretical implications as they firstly provide evidence to support the Black Swan theory (Taleb, 2007). The theory provides a depiction of how occurrence of major events such as financial crises, pandemics, natural disasters leads into rising stock market volatility due to panic trading. However, imposition of policies that help to curb the problem is instrumental in reducing investors’ panic despite the fact that these policies may lead to short-run recessions as seen in the case of COVID-19. Nevertheless, these policies diminish in significance in the long run as the problem continues to persist. Furthermore, we show that panic during major crisis is reduced further by interventions made by regulators to protect stock markets. 6.3 Practical implications The empirical results presented in our article have tremendous practical implications. Firstly, we urge policymakers to timely intervene during crisis to protect the economy and stock markets. Bad news has the potential to cause panic among stock market investors as observed during COVID-19. However timely imposition of containment measures to contain the crisis is vital in building confidence among investors which inhibits their propensity to engage in panic selling which can cause deterioration in stock market performance. Secondly, stock market regulators should make interventions specifically focused on the functioning of stock markets during crises. Some practices such as short selling have been found to induce market volatility even in non-crisis periods. Prohibition of practices like this during crises is crucial for protecting stock markets from further deterioration. Moreover, relaxation of regulations such as removal of tariffs for issue of corporate bonds during major crises is crucial in keeping the market active and liquid. Thirdly, portfolio managers, institutional and individual investors need to take initiatives to prepare for the adverse effects of crises such as COVID-19. This is by diversifying their portfolios in countries that show better initiatives to protect stock markets through proper market functioning interventions as observed during COVID-19. 6.4 Avenue for future research The one inherent limitation of our findings is the treatment of financial market interventions as a group despite having different strategies. The financial markets interventions data compiled by The World Bank, 2021 present different types of data for individual financial market interventions imposed on particular dates along the timeframe. The number of these interventions was large and diverse which made it impractical to capture the interruption effects of each individual of these such as circuit breaks, ban on short selling and delays in disclosure of audited financial statements by listed companies. Thus for analysis purposes, all market functioning and PDM strategies were treated as a group and not a specific individual strategy. Due to the multiplicity of market functioning strategies, further studies should focus on the most common ones such as ban on short selling and proceed to examine their moderation role on stock market performance of individual countries during COVID-19. The findings imply that buying stocks from markets in countries with higher magnitude of market functioning interventions during major crises may be safer for investors. Future research direction should also be directed toward studying the role of other financial sector interventions during COVID-19 in protecting stock markets during these times of economic turmoil. These include firstly, liquidity/funding interventions that comprise of policy rate, asset purchases and providing liquidity in foreign exchange (FX). 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(2020), “Middle East respiratory syndrome coronavirus (MERS-CoV) - surveillance and testing in North England from 2012 to 2019”, International Journal of Infectious Diseases, Vol. 93, pp. 239-248, doi: 10.1016/j.ijid.2020.01.043. Karlsson, M., Therese, M. and Pichler, S. (2014), “The impact of the 1918 Spanish Flu epidemic on economic performance in Sweden”, Journal of Health Economics, Vol. 36, pp. 432-444, doi: 10. 1016/j.jhealeco.2014.03.005. Stock market reactions to COVID-19 shocks Kimura, H., Tsukagoshi, H., Ryo, A., Oda, Y., Kawabata, T., Majima, T., Kozawa, K. and Shimojima, M. (2015), “Ebola virus disease: a literature review”, Journal of Coastal Life Medicine, Vol. 3, pp. 86-89, doi: 10.12980/JCLM.3.201514J93. 643 Liu, M., Ning, Y., Cao, D., Zhang, D. and Wang, J. and Chen, M.(2020b). “Modelling the evolution trajectory of COVID-19 in Wuhan, China: experience and suggestions”, Public Health, Vol. 183, pp. 76-80, doi: 10.1016/j.puhe.2020.05.001. Sarstedt, M. and Mooi, E. (2014), A Concise Guide to Market Research: The Process, Data, and Methods Using IBM SPSS Statistics, Springer, Heidelberg, New York. World Health Organization (2021), “WHO coronavirus disease (COVID-19)”, Dashboard, available at: https://covid19.who.int/?gclid5CjwKCAjwzIH7BRAbEiwAoDxxTp0vB4gFQLw30RQ728DrRiQ_ 2lo_imViyTLk1SfYFEYQxRrMbZQDdBoC5acQAvD_BwE (accessed 2 August 2021). Zraick, K. and Garcia, S. (2020), “Canceled because of corona virus: a brief list”, New York Times, available at: https://www.nytimes.com/article/cancelled-events-coronavirus.html (accessed 16 August 2021). Appendix 1 S/N Regression assumptions 1 No heteroskedasticity problem 2 No multicollinearity problem 3 No specification problem 4 No influential observations Test(s) Breusch–Pagan test Chi2(1): 2.083 p-value: 0.0647 VIF (See Appendix 1) Link test t: 0.766 p-value: 0.444 Cook’s distance no distance is above the cut-off We seek values >0.05<5.00 >0.05<1.00 Table A1. Regression model assumptions CFRI 12,4 Appendix 2 No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 Table A2. 37 Selected countries and their respective stock 38 39 indices 644 Country Region Stock market Index South Africa Nigeria India Malaysia Singapore Hong Kong Indonesia South Korea Qatar Japan Philippines China Thailand Taiwan Austria Ireland Estonia Netherlands Greece Belgium Turkey France Great Britain Italy Germany Russia Denmark Finland Sweden Norway Portugal Israel Spain Argentina Brazil Mexico USA Canada Australia Africa Africa Asia Asia Asia Asia Asia Asia Asia Asia Asia Asia Asia Asia Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Latin America Latin America Latin America North America North America Oceania Johannesburg Stock Exchange Nigeria Stock Exchange Bombay Stock Exchange Bursa Malaysia Singapore Exchange Hong Kong Exchange Indonesia Stock Exchange Korea Stock Exchange Qatar Stock Exchange Tokyo Stock Exchange Philippine Stock Exchange Shenzhen Stock Exchange Stock Exchange of Thailand Taiwan Stock Exchange Vienna Stock Exchange Irish Stock Exchange Tallinn Stock Exchange The Amsterdam Stock Exchange Athens Stock Exchange Brussels Stock Exchange Borsa Istanbul Paris Stock Exchange London Stock Exchange Borsa Italiana Frankfurt Stock Exchange Moscow Exchange Copenhagen Stock Exchange Helsinki Stock Exchange Stockholm Stock Exchange Oslo Stock Exchange Portugal Stock market Tel Aviv Stock Exchange The Madrid Stock Exchange Buenos Aires Stock Exchange Sao Paulo Stock Exchange The Mexican Stock Exchange New York Stock Exchange Toronto Stock Exchange Australian Securities Exchange FTSE JSE NSE 30 BSE SENSEX 50 FTSE Bursa FTSE Straits Times Hang Seng Jakarta Composite KOSPI Composite MSCI Qatar Nikkei 225 PSEI Shenzhen Composite SETI TSEC Weighted ATX ISEQ OMXTGI AEX Athex Composite BEL 20 BFX BIST 100 CAC 40 FTSE 100 FTSE MIB GDAXI MOEX OMX Copenhagen 20 OMX Helsinki OMX Stockholm All Share Oslo Bors All Share PSI 20 Tel Aviv 35 IBEX 35 MERV IBOVESPA IPC NYSE composite TSX Composite ASX 200 About the authors Mutaju Isaack Marobhe. He is a currently a PhD research fellow at Swiss School of Management in Switzerland. He works as a lecturer in the Finance and Accounting Department at Tanzania Institute of Accountancy. He completed his Master of Business Administration at University of Dar es Salaam. He also finished his Bachelor of Business Administration at University of Iringa formerly Tumaini University-Iringa University College. He is a Certified Public Accountant (CPA-T). His research activities are focused on the areas of behavioral finance, economics and entrepreneurship. Mutaju Isaack Marobhe is the corresponding author and can be contacted at: mutaju.marobhe@tia.ac.tz Jonathan Mukiza Peter Kansheba. Recently a PhD research fellow at the department of management in the School of Business and Law of the University of Agder (Norway). He completed his Master’s degree in Finance and Accounting in oil and gas at the University of Dar Es Salaam Business School (Tanzania). He gained his Bachelor’s degree in accounting and Finance at Ardhi University (Tanzania). Mr. Kansheba works as an assistant lecturer in the department of business studies at Ardhi university majoring in Accounting, Finance, Entrepreneurship and Innovation courses. He is CPA(T) holder (Certified Public Accountant), the professional certificate offered by the Tanzania National Board of Accountants and Auditors (NBAA).His research interest focuses on entrepreneurship and innovation ecosystems, business models and strategies, entrepreneurial finance, organizational leadership and strategies, quantitative research techniques, bibliometric and meta-analytics. 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: permissions@emeraldinsight.com Stock market reactions to COVID-19 shocks 645