Impact of COVID-19 on stock price crash risk: Evidence from Chinese energy firms

Summary

In this 2021 Energy Economics article, Shoujun Huang of the University of Science and Technology of China and Hezhe Liu of Shandong Normal University study how the outbreak of COVID-19 affected the stock price crash risk of energy firms in China. They find that crash risk for energy firms significantly decreased in the post-COVID-19 period. Firms engaging in more corporate social responsibility activities were less exposed to crash risk afterward, and the effect of COVID-19 on crash risk was less severe for state-owned enterprises than for other firms. The authors say the results reflect China's economic recovery and have implications for how firms can improve resilience to shocks.

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Energy Econ
. 2021 Jul 7;101:105431. doi: 10.1016/j.eneco.2021.105431

Impact of COVID-19 on stock price crash risk: Evidence from Chinese energy firms

Shoujun Huang
Shoujun Huang

a School of Public Affairs, University of Science and Technology of China, Hefei 230026, China

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a , Hezhe Liu
Hezhe Liu

b Law School, Shandong Normal University, Jinan 250358, China

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b, ⁎

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a School of Public Affairs, University of Science and Technology of China, Hefei 230026, China

b Law School, Shandong Normal University, Jinan 250358, China

⁎ Corresponding author.

Received 2021 Jan 23; Revised 2021 Jun 10; Accepted 2021 Jul 3; Issue date 2021 Sep.

© 2021 Elsevier B.V. All rights reserved.

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Abstract

This paper studies the impact of the outbreak of coronavirus disease 2019 (COVID-19) on the stock price crash risk of energy firms in China. We find that the stock price crash risk of energy firms significantly decreases in the post-COVID-19 period. We also find that firms that engage in more corporate social responsibility (CSR) activities are less exposed to stock price crash risk in the post-COVID-19 period than those that engage in less CSR activities. Finally, we show that the effect of COVID-19 on stock price crash risk is less severe for state-owned enterprises (SOEs) than for non-SOEs in the post-COVID-19 period. Our findings demonstrate China's economic recovery in the post-COVID-19 period and have policy implications for firms to improve their resilience to exogenous shocks.

Keywords: Stock price crash risk, COVID-19, Corporate social responsibility, SOEs

1. Introduction

In this study, we analyze the effect of COVID-19 on the stock price performance of Chinese energy firms. As a key production factor, energy resources are necessary for economic growth. The global economic crisis triggered by COVID-19 has resulted in huge and heterogeneous economic losses for all countries and industries ( Alfaro et al., 2020 ; Baker et al., 2020 ; Ding et al., 2020 ), and governments around the world have taken measures to prevent the spread of the virus ( Yang and Deng, 2021 ). In particular, most countries have called on the public to self-isolate, forced businesses to close, and imposed city-wide lockdowns ( Fang et al., 2020 ; Liu et al., 2020a ). Due to the manufacturing lockout, the demand for oil has declined dramatically. 1
Liu et al. (2020a) show that prohibiting human mobility and economic activity results in lower energy consumption, thereby reducing air pollution by 12% on average. Thus, the energy industry has taken a heavy hit due to COVID-19 ( Fu and Shen, 2020 ).

The literature shows that the COVID-19 pandemic has affected the energy industry on all fronts. For example, Ertuğrul et al. (2020) analyze the impact of the COVID-19 outbreak on diesel consumption in Turkey. Iyke (2020a) examines the reaction of US oil and gas producers during COVID-19. Gil-Alana and Monge (2020) demonstrate that based on crude oil prices, the COVID-19 crisis has reduced market efficiency. Kartal (2021) also investigates the effect of COVID-19 on oil prices in Turkey. In other words, the spread of COVID-19 and oil price news have significantly influenced oil prices ( Devpura and Narayan, 2020 ; Narayan, 2020a ; Prabheesh et al., 2020 ). And COVID-19 pandemic has had a complex and significant influence on the foreign-exchange market ( Narayan, 2020a ; Narayan, 2020b ), such as the relationship between the Japanese yen and the stock market ( Narayan et al., 2020 ), and the predictability of oil prices for the Japanese yen ( Devpura, 2020 ). Regarding investors' expectations of oil prices, Huang and Zheng (2020) provide evidence that the COVID-19 outbreak has led to a structural change in the relationship between investor sentiment and crude oil futures. However, rare literature focus on the market reaction to COVID-19 in the energy industry.

The financial market volatility has attracted the attention of investors during COVID-19. In this study, we examine a crucial feature of capital markets, stock price crash risk, to evaluate the economic consequences of COVID-19 on the Chinese capital market. We follow Chen et al. (2001) and define stock price crash risk as the negative skewness in the distribution of stock returns, which captures the higher moments of stock returns. A stock price crash severely affects investor confidence in the capital market, the stability of the financial market, and even triggers resource misallocation in the economy. In particular, energy is a key driver of economic growth, which means that the stock price crash risk of energy firms may reflect changes in investors' economic expectations for the future, leading to the following question: How do energy firms perform in the Chinese stock market after the COVID-19 outbreak?

China is the world's largest oil importer and imports a large amount of crude oil from overseas every year. On the one hand, production in China could benefit from the sustained decline in international oil prices. Indeed, the COVID-19 pandemic has increased crude oil returns and stock returns ( Liu et al., 2020b ). On the other hand, the pandemic has increased economic uncertainty ( Iyke, 2020b ), thus affecting the performance of energy firms. For example, Fu and Shen (2020) demonstrate the negative impact of COVID-19 on corporate performance in Chinese energy firms. According to this discussion, the economic consequences of COVID-19 on the energy industry and the underlying mechanism of such effect remain unclear.

Thus, we use stock price crash risk to investigate the economic consequences of COVID-19 on energy firms in China. Moreover, we use the propensity score matching (PSM) approach to select firms comparable to energy firms for one-to-one nearest neighbor matching, to eliminate individual differences and explore the net effect of COVID-19 on energy firms.

Chinese energy firms are ideal for conducting our empirical analysis for the following reasons. First, as the largest manufacturing country, the development of the Chinese economy requires huge amounts of energy ( Du et al., 2020 ); for instance, in 2019, crude oil consumption exceeded 696 million tons. As the Chinese economy focuses mainly on manufacturing, its development is closely linked to changes in the energy market. Second, China was the first country to experience a large-scale COVID-19 outbreak and to issue city-wide lockdowns; this allows us to exclude the spillover effects of the pandemic from other countries in our analysis. Thus, China offers a unique setting to study the stock market reaction to COVID-19 among energy firms.

To conduct our empirical analysis, we use the model developed by Chen et al. (2001) to measure stock price crash risk. Specifically, we take the date of the Wuhan lockdown (January 23, 2020) as the origin of the COVID-19 outbreak 2
to calculate stock price crash risk three months before and three months after the event. Therefore, we consider the week from January 18, 2020 to January 24, 2020 as the event date, which is the week when COVID-19 came to be widely known by people in China and even around the world. As we use weekly stock returns in this paper, we do not need to identify the specific day of the COVID-19 outbreak.

First, this paper uses PSM to select firms comparable to energy firms for one-to-one nearest neighbor matching. We identify the net effect of COVID-19 on energy firms by comparing energy firms and the matched firms. Second, using short-term market reactions based on daily stock returns, we find that the stock price crash of energy firms and the matched firms occurs one day after the COVID-19 outbreak, involving almost all Chinese listed firms. Third, to capture medium-term market reactions to COVID-19, we use stock price crash risk three months before and three months after the event date. We find that stock price crash risk in both types of firms decreases in the post-COVID-19 period and that COVID-19 has a significant and negative effect on stock price crash risk only for energy firms, after controlling for individual differences; these results indicate China's economic recovery thanks to its successful control of the spread of the virus. Moreover, energy firms may benefit from the sustained decline in international oil prices.

Next, we verify the identification assumption using firm-specific weekly returns. The results show that energy firms and their matched counterparts have the same weekly return trend in the pre-COVID-19 period. The results of a placebo test, which consists of randomly choosing the treatment firms, support our main findings. Finally, using one-to-two nearest neighbor matching, we obtain consistent results that the stock price crash risk of energy firms decreases much more than that of their matched counterparts in the post-COVID-19 period.

To account for heterogeneous effects of firm characteristics, we perform two tests to identify the effect of corporate social responsibility (CSR) activities and firm ownership on our results. CSR activities are generally defined as social actions required by law and morality, beyond the profit of firms. With economic development, firms are aware of the importance of their corporate image and increasingly engage in CSR activities. Firms typically invest in CSR to build trust with stakeholders, thereby obtaining support from workers, suppliers, customers, and other stakeholders, which illustrates that social and environmental activities can create value for firms ( Borghesi et al., 2014 ; Ferrell et al., 2016 ). In particular, CSR performance before a crisis can be seen as insurance-like protection when negative events occur in a firm ( Kong, 2012 ; Deng et al., 2013 ; Lins et al., 2017 ). According to the CSR activities of Chinese listed firms, we find that CSR performance moderates the stock price crash risk of energy firms during the COVID-19 period, which reveals that CSR performance before a crisis can reduce the loss of firm value when suffering adverse events.

Next, we examine the differences between state-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs). First, state ownership may improve the performance of SOEs relative to that of non-SOEs during the COVID-19 period, as SOEs are required to support the work of the government to control the spread of the virus. In addition, SOEs tend to be larger than non-SOEs, so SOEs are likely to be more resilient to stock price crash risk in a crisis. Second, SOEs may obtain more government support ( Bai et al., 2006 ) because of their political burdens. Based on international data, Ding et al. (2020) find that the stock returns of government-controlled firms are higher than those of non-SOEs during COVID-19. In addition, studies show that Chinese SOEs perform better than non-SOEs during the COVID-19 epidemic ( Gu et al., 2020 ; Wu and Xu, 2021 ). However, there are no studies on the effect of ownership on publicly traded Chinese energy firms. 3
Thus, we use stock price crash risk to study the stock price performance of energy firms whose ultimate controller is the government during the epidemic. Using empirical tests, we provide evidence that the negative effect of COVID-19 on the stock price crash risk of energy firms is stronger for SOEs than for non-SOEs, suggesting that state-owned energy firms are less exposed to stock price crash risk during the post-COVID-19 period.

This paper contributes to the literature in the following ways. First, unlike previous studies, which mainly focus on human mobility and air pollution ( Fang et al., 2020 ; Liu et al., 2020a ), we focus on the impact of COVID-19 on energy firms because energy is a key production factor for economic growth. Based on rigorous estimation, we find that energy firms suffer less from stock price crash risk in the post-COVID-19 period, complementing the literature on the heterogeneous effects of COVID-19 across different industries ( Gil-Alana and Claudio-Quiroga, 2020 ; Lan et al., 2020 ; Phan and Narayan, 2020 ; Ramelli and Wagner, 2020 ; Yan and Qian, 2020 ).

Second, we study the stock price performance of energy firms three months after the COVID-19 outbreak and compare it with short-term market reactions to COVID-19 ( He et al., 2020 ; Liu et al., 2020c ; Polemis and Soursou, 2020 ), which reflects the impact of COVID-19 over a longer period on the Chinese stock market. China was the first country to effectively manage the spread of the virus, which allows us to comprehensively study the effect of COVID-19 on energy firms from its outbreak to the end of the pandemic. In addition, we present strong evidence that the CSR activities of listed firms can reduce their losses due to COVID-19, which supports the findings of Albuquerque et al. (2020) . This finding demonstrates the importance of CSR on firm value when negative events occur.

The remainder of this paper is organized as follows. Section 2 presents the data and our sample, including sample selection, variable definitions, and identification strategy. Section 3 reports the results of the effect of COVID-19 on the stock price crash risk of energy firms and the robustness tests. Section 4 presents additional tests, including the effects of CSR and firm ownership. Finally, Section 5 concludes the study.

2. Data

2.1. Data and sample

We use the PSM approach to select the treatment and control firms. The initial sample includes all Chinese listed firms on the A-share market, including 248 energy firms covered by the State Intellectual Property Office (SIPO) database and 3420 non-energy firms. Other financial data come from the China Stock Market and Accounting Research (CSMAR) database. Finally, we obtain the firms' CSR information from Rankins CSR ratings (RKS), which is the most commonly used and authoritative database in China for CSR activities.

We take the following steps to select our final sample. First, we remove all listed firms marked “ST (special treatment)” or “*ST,” as they will start delisting procedures according to the rules of the China Securities Regulatory Commission (CSRC). Second, we exclude all financial firms, all firms whose total debt is greater than their total assets, and all firms with missing data; therefore, the sample consists of 198 energy firms and 2362 non-energy firms. Third, based on the PSM approach ( Rosenbaum and Rubin, 1983 ), we use one-to-one nearest neighbor matching to select firms similar to energy firms, which reduces the individual differences between the treatment and control groups. Specifically, we use a probit regression that adds a set of firm accounting variables, including share turnover, stock returns, stock return volatility, firm size, leverage, firm profitability, and growth opportunities, to select the firms that most closely resemble the energy firms in the sample in terms of individual characteristics. The energy firms are assigned to the treatment group and the matched firms are assigned to the control group.

Our final sample includes 166 energy firms and 164 matched firms. To avoid the effect of outliers of variables, we winsorize all of the continuous variables at the 1st and 99th percentiles.

2.2. Variable definitions

2.2.1. Measuring stock price crash risk

Referring to the model developed by Chen et al. (2001) and Kong et al. (2021) , we use two measures to calculate stock price crash risk.

First, we use the weekly return of firm i to estimate Model (1) and obtain the residual ϵ
i , τ .

R i , τ = α i + β 1 R m , τ − 2 + β 2 R m , τ − 1 + β 3 R m , τ + β 4 R m , τ + 1 + β 5 R m , τ + 2 + ϵ i , τ
(1)

where i denotes the firm, m denotes the market, and τ denotes the week. R
i , τ is the real weekly return of firm i in week τ . R
m , τ is the real weekly market return in week τ , which is the sum of the tradable value-weighted stock returns of all listed firms. The residual ϵ
i , τ reflects the part of a firm's stock return that cannot be explained by the market factor, representing firm-specific weekly returns.

Second, based on ϵ
i , τ , we construct firm-specific weekly returns using W
i , τ = Ln (1 + ϵ
i , τ ), where i and τ are defined as above. This allows us to calculate stock price crash risk using two measures. The first stock price crash risk measure is measured using Model (2) and is called the negative coefficient of skewness ( NCSKEW ).

NCSKEW i , T 1 T 2 = − n n − 1 3 2 ∑ T 1 T 2 W i , τ 3 / n − 1 n − 2 ∑ T 1 T 2 W i , τ 2 3 / 2
(2)

The second stock price crash risk measure is called down-to-up volatility ( DUVOL ) and is calculated by using Model (3). NCSKEW and DUVOL are positively related to stock price crash risk; that is, firms with higher NCSKEW and DUVOL values have higher stock price crash risk.

DUVOL i , T 1 T 2 = log n u − 1 ∑ DOWN W i , τ 2 / n d − 1 ∑ U P W i , τ 2
(3)

where n is the number of weeks over the [T1, T2] period for each firm. For the [T1, T2] period, we separate all the weeks with firm-specific weekly returns above the period mean (up weeks) from those with firm-specific weekly returns below the period mean (down weeks). n
u is the number of up weeks over the [T1, T2] period. n
d is the number of down weeks over the [T1, T2] period.

Specifically, in this paper, we use the week from January 18, 2020 to January 24, 2020 as the event date and calculate stock price crash risk three months before and three months after this event date, respectively. Namely, we use the weekly stock returns from October 28, 2019 to January 17, 2020 to capture stock price crash risk during the pre-COVID-19 period, and from January 18, 2020 to April 24, 2020 4
to capture stock price crash risk during the post-COVID-19 period.

2.2.2. Control variables

Referring to Chen et al. (2001) and Hutton et al. (2009) , we include the following variables to control for the impact of other factors on our results.

We control for the average weekly share turnover ( Turnover ) over the sample period, defined as the weekly trading volume scaled by the total number of shares outstanding at the end of the week, the average firm-specific weekly returns ( Ret ), and the standard deviation of firm-specific weekly returns ( Sigma ). We also include firm size ( Size ), defined as the natural logarithm of the market value of equity; leverage ( Lev ), defined as total liabilities scaled by total assets; return on assets ( Roa ) to measure firms' financial performance; and firms' growth opportunities ( MTB ), calculated as the sum of the market value of tradable shares and the book value of non-tradable shares and total debt, scaled by total assets.

2.3. Model

Following Liu et al. (2020a) and Fang et al. (2020) , 5
we use the difference-in-differences (DID) method, which is generally used to evaluate the net impact of policy or exogenous events, to investigate changes in the stock price crash risk of energy firms in the post-COVID-19 period. The DID specification is shown in Model (4):

Crash i , t = α i + β 1 Treat i ∗ Post t + β 2 Treat i + β 3 Post t + β 4 Turnover i , t + β 5 Ret i , t + β 6 Sigma i , t + β 7 Size i + β 8 Lev i + β 9 Roa i + β 10 MTB i + ∑ IndustryDummy + ϵ i , t
(4)

where Treat
i is a independent variable that takes a value of 1 for energy firms and 0 for matched firms. The independent variable Post
t is a dummy variable that takes a value of 1 for the post-COVID-19 period and 0 for the pre-COVID-19 period. The dependent variable Crash
i , t is the stock price crash risk of firm i during the three months before/after the event date, measured by NCSKEW and DUVOL . The variable Treat
i ∗ Post
t represents the net effect of COVID-19 on energy firms, which is the key variable in our regression. In addition, we control for Turnover , Ret , Sigma , Size , Leverage , Roa , MTB , and industry fixed effects.

3. Estimation results

3.1. Descriptive statistics

Table 1 presents the summary statistics for the treatment and control groups. As shown in Panel A, the mean of stock price crash risk ( NCSKEW or DUVOL ) for energy firms is 0.048 and 0.048, whereas that of their matched counterparts is 0.039 and 0.027, respectively. The results show that the means of stock price crash risk ( NCSKEW or DUVOL ) for the treatment group are greater than those for the control group, which may be caused by the characteristics of the energy industry, such as domestic energy price regulated by government, the cyclicity of energy industry. Furthermore, the distribution of the control variables for the two groups is almost identical, suggesting that the PSM approach has sufficient power to eliminate systematic differences.

Table 1.

Summary statistics.

Panel A Descriptive statistics

Variables
Energy firm group
Matched firm group

Obs
Mean
Std. Dev.
Obs
Mean
Std. Dev.

NCSKEW
332
0.048
0.585
328
0.039
0.574

DUVOL
332
0.048
0.725
328
0.027
0.668

Turnover
332
0.084
0.071
328
0.077
0.072

Ret
332
−0.065
0.082
328
−0.063
0.089

Sigma
332
0.032
0.019
328
0.031
0.020

Size
332
22.22
1.017
328
22.22
1.005

Lev
332
0.469
0.168
328
0.449
0.181

Roa
332
0.028
0.0830
328
0.033
0.0720

MTB
332
1.536
0.590
328
1.500
0.590

Panel B The univariate analysis of stock price crash risk

Group
NCSKEW
DUVOL

Before
After
Diff1
Before
After
Diff1

Energy firms
0.179
−0.084
−0.263***
0.224
−0.127
−0.351***

(0.000)

(0.000)

The matched firms
0.077
0.001
−0.076
0.081
−0.028
−0.109

(0.229)

(0.150)

Diff2
0.102
−0.085
−0.187**
0.142*
−0.100
−0.242**

(0.105)
(0.177)
(0.036)
(0.060)
(0.187)
(0.024)

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Notes: Panel A reports the descriptive statistics of treatment and control group, respectively. The energy firms are assigned to the treatment group and the matched firms by using PSM are assigned to the control group. Panel B present the univariate analysis of stock price crash risk. Before refers to stock price crash risk before COVID-19. After represents stock price crash risk after COVID-19. Diff1 is calculated as the mean of crash risk after COVID-19 minus before COVID-19. Diff 2 is measured as the mean of treatment group minus the mean of control group. P -value are in parentheses. The definitions of all variables are presented in Appendix. Significance at 10%, 5%, and 1% levels was indicated by *, **, and ***, respectively.

Panel B reports the results of the univariate analysis of stock price crash risk. First, based on the values of Diff2 that indicate the differences in stock price crash risk between energy firm and the matched counterparts, we find that stock price crash risk in the treatment group is higher than in the control group only during the pre-COVID-19 period. This result again supports the argument that stock price crash risk in the energy industry is generally more severe than in other industries, which may be related to government regulatory interventions in the energy industry. Second, the values of Diff1 indicate the differences between the stock price crash risk before and after the COVID-19. Comparing with the pre-COVID-19 period, we find that the stock price crash risk of energy firms falls by 0.263 or 0.351 in the post-COVID-19 period and that the stock price crash risk of matched firms decreases by 0.076 or 0.109. Namely, the firms in both groups show a decrease in their stock price crash risk in the post-COVID-19 period, and energy firms show a larger reduction than their matched counterparts. Third, comparing with the matched firm, the stock price crash risk of energy firms decreases by 0.187 or 0.242 in the post-COVID-19 period, which the decrease is statistically significant. These results show the significant and negative effect of COVID-19 on stock price crash risk, especially for energy firms.

3.2. Market reactions to COVID-19

In the date of Wuhan lockdown, COVID-19 came to be widely known by people in China and even around the world, then resulting in serious social panic and economic instability. And the market reaction in this date can better and timely reflect the unexpected and short-term shock of COVID-19 outbreak on stock market. Hence, we set the date of the Wuhan lockdown (January 23, 2020) as the event date ( T
0 = 0) to observe the variation of daily stock return around the COVID-19, thereby studying the shock of COVID-19 on short-term market reactions.

Fig. 1 illustrates the stock market reactions for the treatment and control groups during the event period [T 0 –5, T 0 + 10]. We find that the daily average stock returns of energy firms and matched firms start to decline rapidly over [T 0 , T 0 + 1] and that these negative stock returns last three days for all firms. In addition, the variation in daily stock returns is consistent with the following two points: (1) stock price crash risk indeed occurs one day after the event day ( T = T
0 + 1), and (2) most of the listed firms in China are affected by the COVID-19 shock. In fact, over 1000 firms experience a slump that day ( T = T
0 + 1) due to the COVID-19 outbreak. In summary, these results indicate that the COVID-19 outbreak and the implementation of lockdown policies dramatically reduce economic activity around the world, leading investors to expect an economic recession and resulting in a stock price crash one day after the COVID-19 event day.

Fig. 1.

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The daily stock return of treatment and control group.

Note: This figure shows the trend of average daily stock return for treatment and control groups around the outbreak of COVID-19. The energy firms are assigned to the treatment group and the matched firms by using PSM are assigned to the control group. X-axis is regarded as the timeline that is defined by the outbreak of COVID-19, which set the event day ( T 0 = 0) as the first day when COVID-19 came to be widely known by people in China and even around the world. Y-axis refers to the means of daily stock return in treatment and control group, respectively.

3.3. COVID-19 and energy firms' stock price crash risk

Table 2 reports the results of the effect of COVID-19 on energy firms' stock price crash risk. First, we use the sample of energy firms only to study the effect of COVID-19 on energy firms. In columns (1) and (2), we find that the coefficient of Post is significant and negative when stock price crash risk is measured by NCSKEW or DUVOL , which suggests that the stock price crash risk of energy firms falls significantly in the post-COVID-19 period.

Table 2.

Stock price crash risk of COVID-19 on energy firms.

Variables
Energy firms

All sample

NCSKEW

DUVOL

NCSKEW

DUVOL

(1)
(2)
(3)
(4)

Treat*Post

−0.194**
−0.248**

(0.089)
(0.107)

Treat

0.105*
0.141*

(0.060)
(0.075)

Post
−0.233***
−0.317***
−0.037
−0.073

(0.067)
(0.084)
(0.068)
(0.081)

Turnover
0.939
0.625
0.494
0.381

(0.661)
(0.783)
(0.451)
(0.504)

Ret
0.800
1.281
0.320
1.307

(1.324)
(1.480)
(0.944)
(1.010)

Sigma
−2.574
0.075
−2.937
1.338

(5.991)
(7.187)
(4.262)
(4.966)

Size
−0.015
−0.067
−0.030
−0.058*

(0.036)
(0.046)
(0.025)
(0.030)

Lev
−0.197
0.157
−0.037
0.126

(0.246)
(0.287)
(0.157)
(0.177)

Roa
−0.782**
−0.479
−0.169
0.068

(0.376)
(0.481)
(0.322)
(0.369)

MB
0.055
0.058
0.049
0.046

(0.062)
(0.077)
(0.044)
(0.055)

Industry effects
Yes
Yes
Yes
Yes

Constant
0.703
1.831*
0.843
1.409**

(0.802)
(1.033)
(0.562)
(0.685)

Obs
332
332
660
660

R-squared
0.068
0.061
0.026
0.033

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Note: This table presents the stock price crash risk of COVID-19 on energy firms. The definitions of all variables are presented in Appendix. Adjusted robust standard errors are in parentheses. Significance at 10%, 5%, and 1% levels was indicated by *, **, and ***, respectively.

Second, we investigate the net effect of COVID-19 using Model (4), and the results are reported in columns (3) and (4). When using NCSKEW or DUVOL , the coefficient of Treat*Post is −0.194 or − 0.248, respectively. This reveals that the stock price crash risk of energy firms decreases more than that of other firms in the post-COVID-19 period. This result represents the net impact of COVID-19 on energy firms' stock price crash risk after using matched firms to eliminate individual differences, such as firm size, leverage, profitability, and growth. Based on the consistent results obtained with different measures, we can conclude that compared with other firms, the stock price crash risk of energy firms decreases significantly by 108%–111% 6
on average in the post-COVID-19 period.

In fact, the Chinese stock market continued its upward trend in the first half of 2020. First, on April 8, 2020, the Chinese government reported that Wuhan city had fully recovered and was no longer under lockdown, suggesting that the COVID-19 pandemic in China was under control as of April 2020. As the sample period is from November 2019 to April 2020, the stock price crash risk that we capture also reflects the effect of successfully stopping the spread of the virus. Second, the sustained decline in international oil prices greatly reduced the cost of factories in China during the sample period.

In summary, our results show that most Chinese listed firms experience a slump on the day of the outbreak and the day after. With the government being able to control the spread of the virus by April 2020, the stock price crash risk of energy firms is significantly less affected in the post-COVID-19 period, indicating an economic recovery. In other words, COVID-19 has no serious effect on the Chinese economy in the long term.

3.4. Robustness checks

First, to verify the parallel trends assumption between the treatment and control groups, we now examine the dynamic changes in firm-specific weekly returns ( W
i , τ ) to measure stock price crash risk before and after COVID-19. Fig. 2
illustrates the variations in average weekly returns at the firm level between the treatment and control groups, with the event week ( T
0 = 0) defined as the first week of the COVID-19 outbreak.

Fig. 2.

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The firm-specific weekly return of treatment and control group.

Note: This figure shows the trend of average firm-specific weekly return that is used to measure stock price crash risk for treatment and control groups around the outbreak of COVID-19. The energy firms are assigned to the treatment group and the matched firms by using PSM are assigned to the control group. X-axis is regarded as the timeline that is defined by the outbreak of COVID-19, which set the event week ( T 0 = 0) as the first week when COVID-19 begun outbreak. Y-axis refers to the means of firm-specific weekly return in treatment and control group, respectively.
In Fig. 2 , the solid and dotted lines (referring to energy firms and the matched firms, respectively) during the [−7, −1] period show the trends before the COVID-19 outbreak. We find no obvious difference in the firm-specific weekly returns between the treatment and control groups. This result suggests that energy firms and their matched counterparts have the same weekly return trend, thus supporting the identification assumption associated with our baseline DID specification. Namely, the difference in stock price crash risk between the treatment and control groups identified in the baseline results is likely to appear in the post-COVID-19 period.

The solid and dotted lines (referring to energy firms and the matched firms, respectively) during the [0, 7] period represent dynamic variations after the COVID-19 outbreak. As Fig. 2 shows, the variations in firm-specific weekly returns are obviously larger for energy firms than for matched firms even though the two groups have a similar trend in the post-COVID-19 period. Moreover, the variations in the two lines show that the absolute value of positive weekly returns is greater than that of negative weekly returns, which reveals that the stock price crash risk of both types of firms is likely to decrease, and that the stock price crash risk of energy firms is likely to decrease more than that of their matched counterparts.

Next, to check the robustness of our main results, we conduct a placebo test by randomly choosing the treatment firms. Specifically, we randomly choose 50% of the listed firms from the sample of 330 firms (166 energy firms and 164 matched firms) and assign them to the treatment group, with the rest being assigned to the control group, to construct a new Treat variable. Then, based on the same method as above, we estimate Model (3) to obtain the coefficient of Treat*Post using the new Treat variable. Theoretically, the coefficient of Treat*Post is not significant, suggesting that the randomly constructed variable has no impact on stock price crash risk. Next, we repeat this process 500 times and obtain 500 estimated coefficients of Treat*Post . Fig. 3
shows the kernel density distribution of the estimated coefficients of Treat*Post and a scatter diagram of the corresponding p -values.

Fig. 3.

Open in a new tab

Placebo test.

Note: This figure describes the result of placebo test. X-axis represents coefficients of Treat*Post , which Treat is random distributed. The red curve shows the kernel density distribution of estimated coefficients. The blue scatter diagram is p -value distribution of estimated coefficients. The red vertical line represents the real value of estimated coefficients in Table 2 . (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
We find that when using NCSKEW and DUVOL as the dependent variable, the corresponding p -values of the coefficients of Treat*Post are generally greater than 0.1. Moreover, the real estimated coefficients of Treat*Post (reported in Table 2 ) are obviously abnormal values in the placebo test, which suggests that our main results are not driven by unobserved factors.

Finally, we use one-to-two nearest neighbor matching to reselect the treatment and control groups to further test the robustness of our main results. We obtain a sample of 195 energy firms and 287 matched firms and run the regression using Model (4). Table 3
reports the results. The regression results show that the negative impact of COVID-19 on the stock price crash risk of energy firms is significant in columns (1)–(4). Thus, we obtain consistent results that the stock price crash risk of energy firms decreases more than that of their matched counterparts in the post-COVID-19 period.

Table 3.

Robustness checks.

Variables
NCSKEW

DUVOL

(1)
(2)
(3)
(4)

Treat*Post
−0.149*
−0.165**
−0.219**
−0.236***

(0.077)
(0.076)
(0.090)
(0.090)

Treat
0.114**
0.120**
0.137**
0.139**

(0.052)
(0.052)
(0.064)
(0.064)

Post
−0.072
−0.013
−0.086
−0.037

(0.050)
(0.055)
(0.057)
(0.062)

Turnover

0.247

0.194

(0.366)

(0.407)

Ret

0.688

1.628**

(0.683)

(0.702)

Sigma

−1.548

2.887

(3.214)

(3.621)

Size

−0.024

−0.053**

(0.020)

(0.023)

Lev

−0.233*

−0.056

(0.134)

(0.147)

Roa

−0.391

−0.037

(0.245)

(0.283)

MTB

0.017

0.024

(0.039)

(0.046)

Industry effects
Yes
Yes
Yes
Yes

Constant
0.183**
0.879*
0.200**
1.421***

(0.087)
(0.458)
(0.100)
(0.537)

Obs
964
964
964
964

R-squared
0.014
0.034
0.020
0.036

Open in a new tab

Note: This table shows the robustness checks by using a one-to-two neighbor matching to reselect treatment and control group. The definitions of all variables are presented in Appendix. Adjusted robust standard errors are in parentheses. Significance at 10%, 5%, and 1% levels is indicated by *, **, and ***, respectively.

In summary, based on the results of different robustness tests, we present strong evidence that the stock price crash risk of energy firms decreases more than that of other firms in the post-COVID-19 period.

4. Additional tests

4.1. Effect of CSR

Studies show the importance of CSR on shareholders' wealth ( Deng et al., 2013 ; Lins et al., 2017 ). For example, Kong (2012) finds that CSR has a positive effect on investors' trading behaviors when they experience adverse shocks. Using food product recall events, Kong et al. (2019) show that pre-crisis CSR performance can serve as insurance-like protection when negative events occur in firms. Hence, we expect the CSR activities of listed firms to affect our main results.

To examine the effect of CSR, we collect the CSR performance of listed firms from the RKS database. Listed firms publish annual CSR reports based on their CSR activities in the current year, which are used by this rating agency to assess their CSR performance. Listed firms that do not engage in CSR activities do not publish an annual CSR report. Following the literature ( Kong et al., 2019 ), we use the RKS CSR scores to measure CSR performance (firms without a CSR score are assigned a value of 0). Next, we run Model (5) to study the impact of CSR during COVID-19.

Crash i , t = α i + β 1 Treat i ∗ Post t ∗ CSR i + β 2 Treat i ∗ CSR i + β 3 Post t ∗ CSR i + β 4 Treat i ∗ Post t + β 5 Treat i + β 6 Post t + β 7 CSR i + β 8 Turnover i , t + β 9 Ret i , t + β 10 Sigma i , t + β 11 Size i + β 12 Lev i + β 13 Roa i + β 14 MTB i + ∑ IndustryDummy + ϵ i , t
(5)

where CSR
i is the CSR performance of a given firm, with a higher value indicating better CSR performance. The key variable is Treat
i ∗ Post
t ∗ CSR
i , which captures the impact of CSR activities during COVID-19.

Table 4 reports the results. First, we divide our sample into firms with and without CSR activities and run the regression using Model (4). By comparing the results in columns (1) and (2), we find that the negative impact of COVID-19 on the stock price crash risk of energy firms is only significant for firms that engage in CSR activities. This suggests that firms that engage in CSR activities and invest more in CSR may be less exposed to stock price crash risk in the event of a crisis. When DUVOL is used to measure stock price crash risk in columns (3) and (4), we obtain consistent results. Next, we use the continuous variable CSR to capture the absolute level of CSR performance, then further examining the effect of CSR activities by using Model (5). In columns (5) and (6), the coefficients of Treat*Post*CSR are negative and significant, suggesting that energy firms with better CSR performance are less exposed to stock price crash risk.

Table 4.

Effect of CSR.

Variables
NCSKEW

DUVOL

NCSKEW

DUVOL

(1)

(2)

(3)

(4)

(5)
(6)

With CSR
Without CSR
With CSR
Without CSR

Treat*Post*CSR

−0.015***
−0.011*

(0.005)
(0.006)

Treat*CSR

0.007**
0.006

(0.003)
(0.004)

Post*CSR

0.007**
0.006

(0.003)
(0.004)

CSR

−0.002
−0.005*

(0.002)
(0.003)

Treat*Post
−0.715***
−0.040
−0.606***
−0.144
−0.045
−0.136

(0.186)
(0.103)
(0.231)
(0.122)
(0.102)
(0.121)

Treat
0.359***
0.034
0.386**
0.080
0.041
0.079

(0.129)
(0.067)
(0.169)
(0.084)
(0.066)
(0.083)

Post
0.125
−0.092
0.138
−0.144
−0.096
−0.141

(0.122)
(0.082)
(0.149)
(0.096)
(0.080)
(0.094)

Turnover
1.286
0.343
1.233
0.197
0.455
0.313

(1.374)
(0.485)
(1.571)
(0.552)
(0.453)
(0.515)

Ret
0.024
−0.001
−0.568
1.548
0.023
1.057

(1.714)
(1.124)
(1.872)
(1.191)
(0.955)
(1.025)

Sigma
−5.451
−3.993
−8.054
2.898
−4.177
0.361

(9.111)
(5.013)
(10.386)
(5.736)
(4.324)
(5.014)

Size
0.007
−0.048
−0.020
−0.059
−0.028
−0.041

(0.047)
(0.034)
(0.060)
(0.040)
(0.028)
(0.034)

Lev
0.082
−0.070
−0.135
0.171
−0.039
0.115

(0.396)
(0.173)
(0.464)
(0.195)
(0.154)
(0.175)

Roa
−0.376
−0.139
−1.034
0.207
−0.176
0.013

(1.390)
(0.314)
(1.488)
(0.378)
(0.319)
(0.369)

MTB
0.051
0.056
−0.017
0.054
0.051
0.040

(0.105)
(0.048)
(0.143)
(0.059)
(0.043)
(0.055)

Industry effects
Yes
Yes
Yes
Yes
Yes
Yes

Constant
−0.098
1.206
0.809
1.310
0.846
1.127

(1.100)
(0.740)
(1.423)
(0.888)
(0.619)
(0.745)

Obs
156
504
156
504
660
660

R-squared
0.085
0.010
0.024
0.027
0.036
0.035

Open in a new tab

Note: This table reports the impact of CSR on crash risk during COVID-19. We split the sample into two subsamples according to firm conduct CSR activities (in columns (1) and (3)) or not (in columns (2) and (4)). Columns (5) and (6) present the effect of the absolute level of CSR by using Model (5). The definitions of all variables are presented in Appendix. Adjusted robust standard errors are in parentheses. Significance at 10%, 5%, and 1% levels is indicated by *, **, and ***, respectively.

In summary, these results suggest that energy firms that engaged in CSR activities in the pre-COVID-19 period lost less firm value during the COVID-19 outbreak, especially for the energy firms with better CSR performance, which confirms the importance of CSR activities in the face of adverse events.

4.2. Effect of ownership

Thus far, we have demonstrated the differential effects of the COVID-19 shock on stock price crash risk between energy firms and other firms, with energy firms being less exposed to stock price crash risk in the post-COVID-19 period. In addition to industry differences, we expect firm ownership to have an effect on our main results. In general, SOEs are larger than non-SOEs, suggesting that SOEs are likely to be more resilient to stock price crash risk during a crisis. In addition, SOEs may perform better than non-SOEs during COVID-19, as SOEs are required to support the work of the government to control the spread of the virus. Thus, SOEs are likely to be less exposed to stock price crash risk in the post-COVID-19 period.

To test the effect of ownership, we partition the sample into SOE and non-SOE groups based on the ultimate controller of the firms and run the regression for each subsample using Model (4). Table 5
reports the results. Columns (1) and (2) compare the difference between SOEs and non-SOEs. We find that the coefficient of Treat*Post is significant and negative in column (1) but not significant in column (2), suggesting that the negative impact of COVID-19 on the stock price crash risk of energy firms is stronger in SOEs than in non-SOEs. Columns (3) and (4) compare the difference in stock price crash risk measured by DUVOL between SOEs and non-SOEs. We find that the significant and negative impact of COVID-19 on the stock price crash risk of energy firms only exists in SOEs, as the coefficient of Treat*Post is −0.531 and significant at the 1% level. These results indicate that energy firms whose ultimate controller is the government perform better than other types of firms in terms of stock returns in the post-COVID-19 period, and they are therefore less exposed to stock price crash risk.

Table 5.

Effects of ownership.

Variables
NCSKEW

DUVOL

(1)

(2)

(3)

(4)

SOE
Non-SOE
SOE
Non-SOE

Treat*Post
−0.482***
−0.052
−0.531***
−0.097

(0.148)
(0.112)
(0.180)
(0.133)

Treat
0.350***
0.001
0.414***
0.020

(0.102)
(0.075)
(0.129)
(0.093)

Post
0.154
−0.156*
0.161
−0.219**

(0.102)
(0.089)
(0.125)
(0.104)

Turnover
0.722
0.461
0.953
0.340

(0.898)
(0.514)
(1.098)
(0.561)

Ret
−0.043
1.276
1.007
2.272*

(1.613)
(1.149)
(1.923)
(1.242)

Sigma
−11.193
3.505
−8.301
8.052

(7.053)
(5.285)
(8.109)
(6.304)

Size
0.009
−0.065*
−0.034
−0.082*

(0.040)
(0.036)
(0.048)
(0.044)

Lev
0.046
−0.072
0.283
0.048

(0.215)
(0.218)
(0.261)
(0.239)

Roa
0.660
−0.306
1.238
−0.148

(0.624)
(0.375)
(0.785)
(0.426)

MTB
0.132
0.049
0.107
0.053

(0.092)
(0.051)
(0.110)
(0.064)

Industry effects
Yes
Yes
Yes
Yes

Constant
−0.198
1.519*
0.616
2.074**

(0.945)
(0.800)
(1.158)
(0.989)

Obs
214
446
214
446

R-squared
0.099
0.018
0.077
0.031

Open in a new tab

Note: This table present the result about the effect of ownership during COVID-19. We divide the sample into SOEs (in columns (1) and (3)) and Non-SOEs (in columns (2) and (4)). The definitions of all variables are presented in Appendix. Adjusted robust standard errors are in parentheses. Significance at 10%, 5%, and 1% levels is indicated by *, **, and ***, respectively.

5. Conclusions

This study investigates how COVID-19 affects the stock price crash risk of energy firms. First, we find that the stock price crash risk of energy firms significantly decreases in the post-COVID-19 period. Second, we show that energy firms with better CSR performance in the pre-COVID-19 period are less exposed to stock price crash risk in the post-COVID-19 period. Finally, we show that the negative impact of COVID-19 on stock price crash risk in the energy industry is stronger for SOEs than for non-SOEs.

The results have two important implications. First, energy is a key factor in economic development. The stock price crash risk of energy firms is significantly lower than other firms in the three months after the COVID-19 outbreak, signaling China's economic recovery. This indicates that when the government can successfully deal with a public health crisis, that crisis will not seriously affect the economy in the long term. Second, by investigating the effect of CSR, we illustrate the importance of CSR performance, showing that firms with better CSR performance are less susceptible to adverse shocks. Thus, firms should engage in CSR activities to build trust with stakeholders and a good reputation, thereby strengthening their risk resistance. Moreover, the government should encourage firms to engage in CSR activities and should promote the development of CSR activities. Reducing the vulnerability of listed firms to exogenous shocks can make the stock market more stable and healthier.

The limitation of this paper is that our study only focus on energy firms in the context of China where the government have successfully stopped the spread of the virus. Due to the levels of COVID-19 pandemic that vary from countries, our findings should be applied to other countries, thereby investigating the impact of COVID-19 pandemic in the context of different settings. Future studies can be conducted by exploring the stock price crash risk of energy firms in other countries with the different levels of COVID-19 pandemic.

Acknowledgments

Acknowledgement

The research is supported by the Major Program of National Social Science Foundation of China (19ZDA082).

Footnotes

1 To stabilize the price of oil, the Organization of the Petroleum Exporting Countries (OPEC) announced production cuts to respond to the drop in demand caused by COVID-19 ( Gil-Alana and Monge, 2020 ). Despite this measure, oil prices continued to decline, and crude oil futures prices reached negative values for the first time on April 20, 2020. Related URLs can be found at: https://www.bbc.com/news/business-52350082 .

2 On January 23, 2020, Wuhan, Hubei Province, was the first city in China to issue a lockdown policy to prevent the spread of COVID-19. Thereafter, the COVID-19 epidemic began to attract more and more attention from China and, eventually, from the world. Hence, we consider the Wuhan lockdown date as the start of the COVID-19 epidemic in this study.

3 Wu and Xu (2021) only use an online survey of company executives, and Gu et al. (2020) only focus on one city in China.

4 Except for the COVID-19 outbreak, there is no major incident happened in the week from January 18, 2020 to January 24, 2020, suggesting that the market reaction in the week of event date may be caused by the COVID-19 outbreak. Hence, this paper regards the week of event date as the post-COVID-19 period.

5 Liu et al. (2020a) use a DID model to identify the effect of COVID-19 on air pollution, and Fang et al. (2020) use a DID model to investigate the effect of lockdown policies on reduced human mobility during COVID-19.

6 Given that the average value of NCSKEW is 0.179 before the COVID-19 outbreak (as shown in Panel B of Table 1 ), and that the coefficient of Treat*Post is −0.194 in column (2) of Table 2 , the average value of stock price crash risk for energy firms decreases by 108% (0.194/0.179 = 1.08) in the post-COVID-19 period. In addition, the average value of DUVOL is 0.224, as shown in Panel B of Table 1 , and the coefficient of Treat*Post is −0.248 in column (4) of Table 2 ; this suggests that the stock price crash risk of energy firms decreases by 111% (0.248/0.224 = 1.11).

Appendix B Supplementary data to this article can be found online at https://doi.org/10.1016/j.eneco.2021.105431 .

Appendix A. Appendix

Variables.

Variables
Definition

NCSKEW
The stock price crash risk, which is measured by Model (2)

DUVOL
The stock price crash risk, which is measured by Model (3)

Treat
The dummy variable that equals 1 for energy firms, and 0 for the matched firms selected by PSM.

Post
The dummy variable that equals 1 after the outbreak of COVID-19, and 0 otherwise.

Turnover
The average weekly stock turnover during the period of measuring crash risk, which the weekly stock turnover is defined as the trading volume over the weekly scaled by the total number of shares outstanding at the end of the week.

Ret
The mean of firm-specific weekly returns during the period of measuring crash risk, which is multiplied by 100.

Sigma
The standard deviation of firm-specific weekly returns during the period of measuring crash risk

Size
The natural logarithm of firm’ market value

Lev
The ratio of total debts to total assets.

Roa
Net profits divided by the lagged total assets

MTB
The sum of the market value of tradable shares; and the book value of non-tradable shares; and total debts, which is scaled by total assets.

CSR
The level of CSR performance, which is from the RKS data

SOE
The dummy variable that equals 1 for the state-owned enterprises, and 0 for the non-state-owned enterprises.

Open in a new tab

Appendix B. Supplementary data

Supplementary material.

mmc1.zip (92.4KB, zip)

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