The Impact of COVID-19 on Southeast Asia’s Stock Markets

by Fionne Lim Jing Wen, University of Warwick

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Abstract: The COVID-19 pandemic inflicted grave implications on public health, global economies and financial markets alike, triggering different policy responses world- wide. This paper examines the impact of growth in COVID-19 cases on daily stock market returns of South East Asian (ASEAN) countries and industries, as well as the moderating role of government intervention. Using fixed-effects regression analysis on  panel data with daily observations from January-December 2020, this paper finds that ASEAN stock market returns are not reactive towards growth in COVID-19 cases in general at both country and industry level. This paper also concludes that healthcare and outbreak containment policies helps alleviate the negative reaction of stock markets towards COVID-19. The financial industry in ASEAN appears to be comparatively more resilient towards COVID-19, which is justified by the high digitisation momentum during COVID-19 period.

1. INTRODUCTION

In December 2019, the outbreak of the novel Coronavirus disease (COVID-19) was first discovered in Wuhan, China. In default of timely containment measures, the first death case in China and first exported case to Thailand was reported in the same month of January 2020 (WHO, 2020). Since then, transmission of the infectious disease escalated worldwide, leading to the declaration of a pandemic by WHO in March 2020. To date, the unprecedented event has inflicted grave implications on global public health. This triggered policy responses such as imposition of lockdowns and social distancing measures, severely impacting the economy altogether (Zaremba, et al., 2020). Unsurprisingly, financial markets have reacted dramatically with equity prices plummeting and volatility skyrocketing worldwide at unparalleled levels in the history of epidemics (Baker et al., 2020). This resembles what Taleb (2007) described as a ‘Black Swan’: A rare event with extreme consequences which is only retrospectively predictable.

This paper focuses on the impact of COVID-19 on the stock market since it reflects future expectations, making it a useful barometer of the economy in complex and fast- evolving situations (Wagner, 2020; He, et al., 2020).

2. LITERATURE REVIEW

Black Swan Literature

Prior to COVID-19, there have been numerous literature studies on stock market reactions to Black Swan Events such as natural disasters (Wang & Kutan, 2013), international conflicts (Schneider & Troeger, 2006) and terrorist attacks (Brounen & Derwall, 2010). These catastrophes may severely impact investor sentiments and induce panic selling, costing significant wealth loss even for well-diversified investors (Burch et al., 2016; Shen & Zhang, 2020). These event studies provided insights on how stock markets reacted to particular events, allowing investors to draw parallels from the findings to form contingency portfolio strategies against potential recurrence.

Epidemics

There were few studies on epidemics as well: The Avian Influenza A(H7N9) which Jiang et al. (2017) found causal relation between reported cases and negative performance of the Shanghai Composite Index; The Ebola disease outbreak where Ichev and Marinˇc (2018) found that media coverage on the event reinforced sentiment effects, driving negative returns; Other influenzas where McTier et al. (2013) discovered association between flu incidence and decrease in both stock returns and trading volume. Given the unprecedented scale of COVID-19, drawing parallels from these early studies may be inadequate to understand its true repercussions. For instance, Nippani and Washer (2004) documented insignificant impact on stock returns for most affected countries for the 2003 SARS, whereas worldwide stock markets have already reacted dramatically before the global impact of COVID-19 was clear (Zhang et al., 2020).

With increasing financial integration to develop emerging nations over the years, countries became even more susceptible to global shocks (Lehkonen, 2015). Goldin and Vogel (2010) warns about major systematic risk that entails interdependency in a globalised world such as modern pandemics, leaving ample room for research agendas despite existing studies on epidemics. The manifestation of these academic prognostications in the COVID-19 outbreak and its unprecedented effects induced growing literature on stock market reactions towards pandemics, which I summarize as follow:

COVID-19

Worldwide stock markets have reacted to the pandemic with strong negative returns and volatility increasing at unparalleled levels. However, the magnitude of impact on stock markets is heterogeneous across different countries due to country-specific factors. This includes governance and policy interventions of institutions which played a crucial role during stock market performances in this period.

I. Stock Market Returns

Consistent with some previous findings on epidemics, the common theme identified in recent literature is the significant correlation between COVID-19 and negative stock returns. Looking solely at China, Al-Awadhia et al. (2020) found significant negative relation between stock returns and both daily growth in total confirmed cases and death cases. Pooling across 64 countries, Ashraf (2020a) found the same negative reaction of stock markets, but more proactively towards the growth in number of confirmed cases than the number of deaths. In contrast, J.Heyden and Heyden (2020) who adopted the event study methodology, found no significant reaction of the S&P 500 and S&P Europe 350 to the announcement of the first confirmed case, but significant negative reactions to the first death case. However, it is noteworthy that the event study approach may not be suitable to study the impact COVID-19 for reasons which I will clarify under the Section 5.3 of this paper.

II. Stock Market Volatility

Various papers identified a substantial increase in stock market volatility and systemic risks as well. Interestingly, Ali et al. (2020) and Zhang et al. (2020) found that the Chinese Stock Markets have reacted with relatively lower volatility compared to US and Europe. Bai et al. (2020) complements these findings with a long-term analysis on the impact of infectious diseases through 2005-2020 and discovered a significant increase on stock market volatility, with China bearing the smallest impact among big economies in all cases. These papers concluded that timely actions by authority is crucial to buffer against volatility spikes.

III. Government Intervention

Building on non-uniform stock market reactions across countries towards COVID-19, several studies analysed the role of institutional factors on stock market reactions. Zaremba et al. (2020) stated that government interventions through information campaigns and cancellations of public events significantly increased volatility of international stock markets, whereas Shanaev et al. (2020) found thst policy interventions posed a larger negative impact on stock market returns than the disease itself. While these papers criticised the efficacy of policy interventions on stock market performances, Ashraf (2020b) argued that the effects are ambiguous due to the opposing forces of direct negative effect on market sentiments and indirect positive effect through the reduction in outbreak intensity and income support. Erdem (2020) deduced that the adverse effects of COVID-19 on stock market returns and volatility is greater for countries with lower economic freedom. Clearly, these studies have important implications for policymakers which will be explored further in this paper.

3. MOTIVATION AND CONTRIBUTION

At the time of undertaking this study, most research on COVID-19 and stock markets have focused on major economies like the US and China or countries with the largest number of cases. While the negative impact on large Asian stock markets were lighter (Ali et al., 2020; Bai et al., 2020), Asian emerging markets bore the greatest impact from the pandemic (Topcu & Gulal, 2020) and yet research on the region remains scant. Hence, this paper complements Yarovaya et al. (2020)’s proposition to intensify research on emerging markets, by exploring the South East Asia – ‘ASEAN’ region which mostly comprises emerging economies. The analysis will cover countries that are included in the MSCI ASEAN Index (Figure 1a).


Figure 1a: MSCI Stock Index Composition, Country Weights

Figure 1b: MSCI Stock Index Composition, Industry Weights

Following the pandemic, the MSCI ASEAN Index was projected to face its biggest underperformance against the world over the past 7 years as of August 2020 according to Bloomberg (2020) (Section 10, Figure 7). Equity researchers have argued that the lack of technology stocks and high percentage of cyclical stocks in the index composition which can be seen from Figure 1b, led investors to overlook the region (Vishnoi et. al, 2020). On the other hand, there were also claims of no absolute correlation between number of COVID-19 cases and stock market performance which contrasts with literature findings mentioned above (Pennington, 2020).

Thus, this paper aims to bridge the gap in academic literature and financial media, by investigating the impact of COVID-19 cases on stock market performances across ASEAN countries. Building on the argument that investors have overlooked the region for its stock composition, this paper will also examine stock market reactions towards the pandemic at the industry-level as well. This is to provide investors with greater insight to form better sentiments and capitalise on performing industries in their portfolio instead of neglecting the whole ASEAN region during pandemics.

Government Intervention

Thus far, no existing consensus in literature on the impact of government policies on stock market performances were found. At the same time, the role of government intervention on stock performances has been flagged up in financial media even for ASEAN. For instance, a 6-month moratorium for loan repayments was implemented in April 2020 by the Malaysian Government to assist individuals, enterprises and corporates amidst the pandemic (BNM, 2020). According to Ruehl and Lockett (2020) in the Financial Times, this induced a spike in retail investors and drove a bull market in Malaysia’s stock exchange which helped the benchmark index, FBMKLCI cushion heavy losses incurred during a sell-off in March. Some market strategists on the other hand argued that a key factor underpinning ASEAN’s underwhelming performance in the Asia Pacific region is the management of the outbreak (Huang,2020).

It is important to understand the repercussions of government intervention on stock markets during a pandemic to mitigate negative shocks on the economy. Nonetheless, it is even more crucial to be specific on the type of government policies which have an influence over stock markets. Hence, this paper follows Mishra and Bathinda (2020)’s recommendation to extend the analysis on Asian Stock markets by including different policy parameters and exploring their impact on stock markets. This paper aims to assist policymakers in evaluating policy decisions amidst pandemics with conclusions drawn from the results.

4. OBJECTIVES

To summarize, this paper first sets out to investigate how daily stock market returns react to growth in daily COVID-19 cases in ASEAN at the country-level. Next, the role of government intervention in moderating these stock market reactions will then be examined. Finally, these reactions will be analysed at the industry-level.

5. DATA AND METHODOLOGY

5.1 Data

To analyse ASEAN stock market performances, I collected daily stock market return values of ASEAN countries included in the MSCI ASEAN Index from Bloomberg. These countries include Indonesia, Malaysia, Thailand, Philippines and Singapore. I chose to use the MSCI Index as it allows me to leverage sectoral-level data in each country to carry out the industry-level analysis.

Some industry indices are only available for certain countries in the sample. For consistency and comparability, my analysis will only include industries with stock index data available across all 5 countries. For robustness, I will be using the major stock index of each country as an alternative dependent variable which has a correlation of 0.9797 with the main dependent variable. Table 9 under Section 10 shows the stock indices collected for each country.

Next, I retrieved data on daily COVID-19 confirmed cases across the 5 countries from ourworldindata.org. Figure 2 plots the daily number of COVID-19 confirmed cases over time for each country. To capture the degree of government intervention, I used policy stringency indices developed by Oxford COVID-19 Government Response Tracker (OxCGRT, 2020). These indices scaled from 0-100 reflect the daily level of government action on different aspects (Table 1).

Table 1: OxCGRT Index Descriptions

The Overall Government Response Index captures all indicators included in the Health & Containment and Economic Support Indices. A limitation of using the OxCGRT indices as a proxy for government intervention is that they only capture the policies devised but not the level of enforcement on containment measures which has a crucial impact on outbreak management.

5.2 Timeline

While most studies have focused their period of analysis from January-April 2020 which is claimed to be the peak COVID-19 period, this paper will cover a period from January-December 2020 because daily confirmed COVID-19 cases in ASEAN were much higher in late 2020 (Figure 2). Although the government response curve across ASEAN may have declined from its peak beginning mid-April, they remained elevated until December (Figure 3).

Figure 2: Daily COVID-19 Confirmed Cases across ASEAN countries
Figure 3: Government Response Index across ASEAN countries.

With these daily observations from January-December 2020, I constructed two separate data sets (Table 2 & 3). Weekends are omitted from the analysis as they are non-trading days which are common across all countries. As for different non-trading days across these countries such as on national holidays, a fill-forward approach is used to address missing values of stock market returns where the previous stock return value is carried forward1. This is a method commonly used by data providers and financial institutions (Kokic, 2001).

Table 2: Summary Statistics (Country-level Data Set)
Table 3: Summary Statistics (Industry-level Data Set)

5.3 Methodology

This section details the empirical strategy I used to achieve the objectives presented in (Section 4). Since this paper focuses on the impact of the COVID-19 condition in a country specifically, the country’s stock market performance, I will depart from the classical event-study approach2 used in some papers (Alam, et al., 2020) because the date of the first confirmed case is different in each country and does not indicate the peak of the event (Al-Awadhia, et al., 2020).

I will adopt a standard panel regression analysis. A key advantage is that it allows me to leverage on time fixed-effects estimators to control for global time-varying shocks that may contaminate the analysis. Specifically, COVID-19 stretches across a time period, and other events during the window period such as the oil price dispute between Saudi Arabia and Russia have confounding impacts on stock markets (Ashraf, 2020a; Ramelli & Wagner, 2020). In fact, Sharif, et al. (2020) found that the oil slump had a bigger impact on US Stock Markets than the pandemic.

5.3.1 Country-Level Analysis

To examine the reaction of stock markets to COVID-19 cases, I begin by specifying the following baseline regression:

(1)   \[Y_{c,t}=\alpha_c + \beta_1 COVID_{c,t}+\beta_2 \inc GOV_{c,t} + \alpha_c + \displaystyle\sum_{t=1}^{T-1} \gamma_t D_{t} + t \]

Yc,t indicates the daily stock market returns in country c, time t, where

(2)   \[T_{c,t}=\frac{Index_{c,t}-Index{c,t-1}}{Index_{c,t-1}} \times 100\% \]

COVIDc,t indicates the daily growth in COVID-19 confirmed cases, where

(3)   \[COVID_{c,t}= log \frac{covidcases_{c,t}}{covidcases_{c,t-1}} \]

GOVc,t is the daily government response index, where

(4)   \[\inc GOV_{c,t}=OxCGRTIndex_{c,t}-OxCGRTIndex_{c,t-1} \]

ac is a country fixed-effects dummy which controls for cross-country unobservable heterogeneity. For instance, cultural biases and a country’s freedom is found to have confounding impacts on stock market reactions towards the pandemic (Ashraf, 2020;Erdem, 2020); Dt is the daily time fixed-effects which controls for daily international shock that impacts all ASEAN stock markets alike such as the oil supply shock mentioned above; t is the unobservable error term. Heteroscedastic-robust standard errors are used for all estimations in this paper to address the heteroscedasticity of t.

Here, β1 is the coefficient of interest which gives the change in stock index returns for a percentage point increase in daily growth of COVID-19 cases. A Hausman-test and Breusch-Pagan-LM test was run on the specification (Section 10, Table 10). Although the results favour a pooled-OLS model, I will trade-off efficiency for better identification with a fixed-effects model to address concerns mentioned above.

To investigate how stock market returns react to COVID-19 cases at different levels of government intervention, I made the OxCGRT government response indices into categorical variables (Table 4).

Table 4: OxCGRT Categorical Variable Classification

I proceed to interact these categorical variables with COVIDc,t, which yields the following specification:

(5)   \begin{align*} Y_{c,t}&=\alpha_c +\beta_{1}COVID_{c,t}+\beta_{2}COVID{c,t} \times Gov_{c,t}+\alpha_c \\ &+ \displaystyle\sum_{t=1}^{T-1} \gamma_t D_t + t \end{align*}

where Govc,t is the categorical variable for the Overall Government Response Index that captures all policy aspects:

To be more specific on identifying the type of policy influencing stock market reactions towards COVID-19, I then specify the following equation:

(6)   \begin{align*} Y_{c,t}&=\alpha_c +\beta_{1}COVID_{c,t}+\beta_{2}COVID{c,t} \times Econ_{c,t} + +\beta_{3}COVID_{c,t} \times \\ & Health+{c,t} + \displaystyle\sum_{t=1}^{T-1} \gamma_t D_t + t \end{align*}

where Econc,t and Healthc,t are the categorical variables for the Economic Support Index and Health & Containment Index Respectively.

The coefficients on the interaction variables, i.e. β2 for specification (2) and β2 and β3 for specification (3) are the coefficients of interest.They capture the difference in change in stock market returns for a percentage point increase in daily growth in COVID-19 cases of a certain level of intervention as compared to no intervention. β1 is now interpreted as the change in stock index returns for a percentage point increase in daily growth of COVID-19 cases, given no government intervention.

5.3.2 Industry-Level Analysis

To analyse how stock market returns react to growth in COVID-19 cases across different industries, equation 1 is estimated by different industries i using data set in Table 3

(7)   \begin{align*} Y_{c,t}^i&=\alpha_c+\beta_{1}COVID_{c,t}+\beta_{2}\inc GOV_{c,t} +\alpha_{c} \\ &+\displaystyle\sum_{t=1}^{T-1} \gamma_t D_t + t \end{align*}

indicates stock returns for industry i . β1 is again the coefficient of interest.  

6.1 Country-level Analysis

6.1.1 Correlation Analysis

Table 5 presents the correlation between all variables of interest.

Table 5: Correlation Matrix of All Variables

Daily MSCI Stock Index Returns correlates negatively but insignificantly with growth in confirmed cases. The variable correlates significantly and positively with all measures of government intervention measured with the OxCGRT indices.

On the other hand, growth in COVID-19 confirmed cases correlates negatively with all measures of government intervention. Although insignificant, this negative relationship suggests that higher levels of intervention through policies such as lockdown restrictions captured by the health and containment or the overall government response index may have helped curtail the transmission of COVID-19. Nonetheless, further analysis needs to be carried out to control for potential confounding variables driving the relationship between these variables for causal identification.

Table 6 presents the correlation matrix of Stock Market Indices of the 5 ASEAN countries across different periods.

Table 6: Correlation Matrix of ASEAN Stock Market Indices

Generally, there is a positive correlation between all indices which may be due to the geographical proximity of these countries. These positive correlations increase and become significant at the 1% level for all country pairs during the peak-COVID-19 period as compared to the pre-COVID-19 period. This is unsurprising for periods of heightened volatility as markets tend to co-move given the systematic risk from the global shock. Some of these correlations decreased after the peak period but remained at levels higher than the pre-COVID-19 period (Section 10, Table 11).

6.1.2 Regression Results

Table 7 reports the results from the country-level regression analysis.

Table 7: Country-level Analysis Regression Results

Baseline Specification

Consistent with the broad consensus of recent studies, the negative value of the COV IDc,t coefficient from specification (1) suggests that daily stock market returns react negatively to growth in COVID-19 confirmed cases. Nonetheless, this reaction is not significant as compared to existing literature finds. The weak negative relation may be due to extending the period of analysis, as markets have adapted to the shock over time, with negative sentiments dissipating. To test this hypothesis, I re-estimate the specifications on a smaller sample period and managed to find support for the conjecture, which I discuss under Section 7.

The marginally significant negative coefficient on ∆GOVc,t suggests that policy shocks have a negative contemporaneous impact on stock market returns. A possible explanation is that the announcement and implementation of these policies especially regarding containment measures induce negative sentiments due to its adverse effect on economic activity (Ashraf, 2020b).

Summarizing, I was unable to find strong evidence on COVID-19 cases within countries contributing to ASEAN’s stock market underperformance during the pandemic.

Government Intervention

Interestingly, it can be seen from the regression results of specification (2) that the negative reaction of stock market returns towards growth in COVID-19 confirmed cases becomes significant even at the 1% level once the government response index is kept constant at 0. This implies that without government intervention, stock market returns decreased significantly by approximately 4% points for a 1% point increase in growth of COVID-19 confirmed cases.

As government response increases, this negative relationship becomes weaker, which is captured by the significantly positive coefficient on Govc,t COV IDc,t. Stock markets react 3% point less negatively to growth in COVID-19 confirmed cases at low levels of government intervention and 4% point less negatively at higher levels of government intervention as compared to having no intervention.

The coefficients on Econc,t COVIDc,t and Healthc,t COVIDc,t from the regression results of specification 3 suggests that negative stock market reactions towards growth in COVID-19 cases are mainly mitigated by health and containment measures instead of economic support given the significantly positive value on the Healthc,t × COVIDc,t coefficient for all levels of intervention.

To facilitate interpretation, I illustrated the results using a predictive margins plot:

Figure 4: Predictive Margins Plot (Overall Government Response Index)

The slopes on Figure 4 each represent a different level of government intervention measured with the overall government response index Govc,t. They capture the relationship between stock returns and growth in confirmed cases at mean for the given level of intervention it represents. It can be seen clearly that when government intervention is kept constant at 0, i.e. no intervention, there is a strong negative association between stock returns and growth in COVID-19 confirmed cases captured by the negative slope. By increasing intervention to low levels, this negative slope becomes significantly less steep compared to no intervention. The negative slope becomes even flatter for moderate levels of intervention and becomes slightly positive for high and very high levels of intervention. This captures how government intervention has helped moderate the negative response of stock markets towards COVID-19. The change in the slope which diminishes with higher level of intervention captures the diminishing marginal returns to the moderating effect. A similar pattern is observed for the interaction between COV IDc,t Healthc,t (Figure 5b). However, there is no clear evidence that higher government intervention in terms of Economic Support policies helps moderate the negative relationship between Stock Returns and COVID-19 confirmed cases (Figure 5a).

Figure 5: Predictive Margins Plot

The results obtained from both specifications have important implications towards government intervention on ASEAN stock markets. They suggest that although policy shocks may have direct contemporaneous negative impact on stock returns in the short-run, higher levels of intervention, specifically on health and containment measures, moderates the negative reaction of stock returns towards growth in COVID-19 cases throughout the period of analysis.

As mentioned earlier, these health and containment policies may have helped to curtail the transmission of the disease resulting in lower number of active cases as there would have been without the restrictions. Ashraf (2020) explained that by reducing the intensity of the outbreak, these policies indirectly pose a positive impact on stock markets through its beneficial impact on the economy over time. There was no evidence supporting the conjecture of economic support being the silver bullet to cushion heavy losses on the stock market during the pandemic across the 5 ASEAN countries. However, a key limitation is that the economic support index does not capture support provided to small-medium enterprises, which may be crucial especially in the context of emerging economies. It does not capture the fiscal value of stimulus packages as well. This leads to difficulty in the identification of the true efficacy of economic support policies on stock market performances.

6.2 Industry-level Analysis

6.2.1 Regression Results

Table 8 presents the results of the regression analysis for specification 4. Figure 6 plots the results.

Table 8: Industry-level Analysis Regression Results

In general, stock market indices across the different industries do not react significantly to growth in COVID-19 confirmed cases in their country. This suggests that even at the industry-level, stock markets’ performance seems to be resilient towards the condition of the outbreak within the country.

Comparing across the insignificant coefficients, Consumer Discretionary stocks have a larger negative reaction in terms of returns. This is consistent with the results from L.E.K Consulting (2020) survey conducted on a sample of 2000 ASEAN consumers on their post-pandemic consumption behaviour. The survey finds an increase in propensity to save due to the pandemic which adversely impacts consumer’s discretionary spending. As the outbreak gets severe and the economy slumps, income decreases, and people save more. This reduces demand for non-essentials such as entertainment and leisure activity.

Although insignificant, the positive reaction of stock returns of the financial sector towards growth in COVID-19 confirmed cases is rather striking, given the cyclical nature of the industry, as well as the inherent interest rate and default risk during an economic slump. This may shed light on why ASEAN banks are poised to recover from the pandemic crisis faster than their global counterparts. According to De Gant`es (2020), ASEAN’s comparatively stronger position in the banking sector is driven by higher rates providing more margins on deposits, higher demand for sustainability loans and higher digitisation momentum created by new consumer habits formed during COVID- 19 (The Edge, 2020). The positive coefficient paints a similar picture to his narrative of a high digitisation momentum in ASEAN. He explained that the pandemic has catalysed the digitisation of consumer activity and business operations in the region, thereby accelerating the adoption of digital banking services in the region. Hence, this surge in demand for digital banking during the pandemic provides reasonable justification to the positive reaction. However, the positive coefficient is not significant enough for strong conclusions to be drawn.

While worldwide markets were able to rebound from their pandemic lows by leveraging on information technology and communications service stocks (Bloomberg, 2020), ASEAN’s communication service sector did not seem to react positively towards the pandemic. This suggests that the communication service industry in ASEAN did not benefit from the digital shift during the pandemic.

To conclude, the ASEAN financial industry seems to be comparatively resilient to the outbreak even in a global context. This fulfils the paper’s aim of providing investors with greater insight on industry performances in ASEAN during a pandemic, given that ASEAN indices have been overlooked due to the lack of technology stocks in its composition as mentioned earlier (Section 3). ASEAN’s communication service industry on the other hand did not benefit from the digital shift induced by the pandemic as opposed to other countries. ASEAN Stock markets are in general nonreactive to COVID-19 outbreak in their country even at the industry-level.

Figure 6: Coefficient Plot of Industry-Level Regression Results

7. ROBUSTNESS

7.1 Alternative Dependent Variable

As mentioned in Section 5.1, each country’s major stock indices are used as an alternative dependent variable for robustness of the country-level regression analysis. By re-estimating specification (1), (2) and (3) (Section 10, Table 12) using the major indices listed in Table 9, the coefficient on COV ID19c,t from specification (1) turns positive but remains largely insignificant. This further weakens the support of a negative country’s stock market reaction towards COVID-19 outbreak in the country in the ASEAN region. The regression results for specification (2) and (3) remain roughly similar in qualitative terms, suggesting that higher government intervention helps moderate the negative impact of COVID-19 even on the major indices.

7.2 Sub-sample analysis

Discussed under Section 6.1.2, the insignificant reaction may be due to the long window period of study, where negative sentiments may have dissipated over time. Hence, I restricted the sample to February-April 2020, a period within the time frame commonly used in papers. This also addresses the issue of MSCI Indices being reviewed quarterly in February, May, August and November, which may pose a slight change to its composition. By re-estimating the 3 country-level specifications on the restricted sample, I found that the negative relation between stock returns and confirmed cases indeed appears to be stronger which is captured by the larger negative coefficient on COV IDc,t of -0.0472 (Section 10, Table 13), but still remains insignificant.

In terms of government intervention, the results from specification (2) and (3) remain qualitatively similar, where the negative stock market reaction towards growth in COVID-19 confirmed cases decreases significantly with Health & Containment policies in place as compared to no intervention. In fact, the results were quantitatively higher as seen from the higher values of the Healthc,t COVIDc,t coefficient (Section 10, Table 13). This suggests that the moderating effects of Health & Containment policies were even higher during the early outbreak period. As for Economic Support policies, there was no evidence of intervention impacting the reaction of stock markets towards COVID-19 cases in the restricted sample.

CONCLUSION

This paper investigates the impact of growth in COVID-19 cases on daily stock market returns of ASEAN countries and industries from January-December 2020. Overall, this paper was unable to document any strong negative reaction of stock market returns reaction towards growth in COVID-19 confirmed cases as opposed to what existing study finds on other country samples. A stronger negative but insignificant reaction was identified on a February-April 2020 sub-sample suggesting that negative sentiments may have dissipated over time. This suggests that ASEAN stock markets are not reactive to growth in COVID-19 cases in their respective countries in general.

While an increase in government intervention has a negative contemporaneous impact on daily ASEAN stock market returns, this paper finds clear evidence that government policies have helped alleviate the negative reaction of stock returns towards growth in COVID-19 cases especially during the early periods of the outbreak. Specifically, policies concerning investment in healthcare and outbreak containment such as lockdown restrictions. As Ashraf (2020b) suggests, this may be due to the indirect positive impact of these policies on the economy through ameliorating the severity of the outbreak. On the other hand, there was no evidence on economic support policies such as stimulus packages moderating negative stock market reactions.

These findings have important implications for policymakers in ASEAN. In the event of a pandemic, policymakers may be concerned about the repercussions of implementing certain containment policies as they may induce instantaneous negative stock market reactions due to negative sentiments towards the policies. Nonetheless, they should consider that these policies may be more beneficial due to their positive spill-over effects from the economy through better outbreak management which helps moderate the negative reaction of stock markets.

The reaction of stock market performances towards growth in confirmed cases remains insignificant even at the industry-level. The financial industry in ASEAN appears to be more resilient towards COVID-19 relative to other industries. This may be due to the high digitisation momentum during the pandemic due to lockdown restrictions, thereby inducing demand for digital banking services. Nonetheless, the communication service sector did not benefit from this digital shift unlike in other countries as results from this paper suggest.

Future studies may examine how stock index composition impacts the resilience of a country’s stock market performance during pandemics which was not explored in this paper. Data for several industries were not available across all 5 countries and hence have been omitted from this study. Hence, future studies may also complement this paper by investigating relevant industries which were excluded such as the Healthcare and Information Technology industry as data becomes available.

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