The impact of COVID-19 on the Chinese stock market: Sentimental or substantial?

doi:10.1016/j.frl.2020.101838

Finance Research Letters 金融研究快报

IF 7.4SSCIJCR Q1经济学2区TopJCI 2.73

Volume 38, January 2021, 101838
第 38 卷，2021 年 1 月，101838

https://doi.org/10.1016/j.frl.2020.101838

Get rights and content 获取权利和内容

Highlights 强调

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COVID-19 outbreak causes widespread negative abnormal returns in Chinese stock market during the post-event window of 36 trading days.
COVID-19的爆发导致中国股市在36个交易日的事后窗口期内普遍出现负异常回报。
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The overall individual sentiment effect during the merging window of event and post-event is positive and stronger than usual.
事件和事件后合并窗口期间的整体个人情绪效应是积极的，并且比平时更强。
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Stocks with high PB, PE and CMV, low institutional shareholding ratio and low net assets are found to be more sensitive to the turbulence.
PB、PE、CMV较高、机构持股比例较低、净资产较低的股票对波动更为敏感。
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Only 7 industries related to Pharmacy, Digitalization, and Agriculture are boosted during the merging window of event and post-event.
只有制药、数字化和农业相关的 7 个行业在活动和活动后的合并窗口期间得到提振。

Abstract 抽象的

We investigate the impact of COVID-19 on Chinese stock market by an event study and examine the effect of individual investor sentiment on returns. The pandemic has an overall negative effect on stock market during the post-event window, which can't be explained by real losses.
我们通过事件研究调查了COVID-19对中国股市的影响，并考察了个人投资者情绪对回报的影响。疫情在事后窗口期对股市产生了整体负面影响，这不能用实际损失来解释。
Results show a stronger positive correlation between individual investor sentiment and stock returns than usual.
结果显示，个人投资者情绪与股票回报之间的正相关性比平时更强。
The impact on individual investor sentiment on stock returns is more significant for enterprises with high PB, PE and CMV, low net asset, and low institutional shareholding. Only 7 industries related to pharmacy, digitalization, and agriculture are boosted.
对于PB、PE、CMV较高、净资产较低、机构持股比例较低的企业，个人投资者情绪对股票收益的影响更为显着。只有医药、数字化、农业相关7个产业得到提振。

Keywords 关键词

COVID-19

Event study

Investor sentiment

Stock returns

COVID-19事件研究投资者情绪股票回报

1. Introduction 一、简介

The COVID-19 pandemic that started in early 2020 has led to a turbulence of financial market. The American stock market experienced circuit breakers twice in one week¹, and the cases in other countries were not much better. Most researchers have observed plummets during the pandemic, but the reasons remain unclear (Al-Awadhi et al., 2020; Fallahgoul, 2020; Nadeem Ashraf, 2020; Shehzad et al., 2020).
2020年初开始的COVID-19大流行导致金融市场动荡。美国股市一周内两次熔断 ¹ ，其他国家的情况也好不了多少。大多数研究人员都观察到疫情期间人口急剧下降，但原因仍不清楚（Al-Awadhi 等人，2020 年；Fallahgoul，2020 年；Nadeem Ashraf，2020 年；Shehzad 等人，2020 年）。

A rational explanation for the volatility would be substantial economic loss according to the efficient market hypothesis. If it holds, the region with more confirmed cases would suffer more substantial losses. Naturally, the profitability of companies in that area would be weakened, and their stock returns would decrease. From this perspective, as the center of the epidemic, the stock returns of companies in Hubei Province should be significantly lower than the average. This gap should continue to broaden as the situation worsens.
根据有效市场假说，对波动性的合理解释是巨大的经济损失。如果成立，确诊病例较多的地区将遭受更重大的损失。自然，该领域公司的盈利能力会减弱，股票回报率也会下降。从这个角度来看，作为疫情中心的湖北省企业股票回报率应该明显低于平均水平。随着局势的恶化，这种差距应该会继续扩大。
In the same way, as the number of confirmed cases and the demand for medical supplies increase, the abnormal return rate of the pharmaceutical industry should also go significantly up correspondingly. However, our study shows that these are not the case. The stock returns of companies in Hubei show no differences with the market. The abnormal returns of pharmaceutical stocks did not last as well.
同样，随着确诊病例数和医疗物资需求的增加，医药行业的异常退货率也应相应大幅上升。然而，我们的研究表明情况并非如此。湖北地区企业股票回报率与市场无差异。医药股的超额收益也没有持续。
This anomaly gives credence to the belief that stock market volatility during the COVID-19 epidemic cannot be explained simply by economic loss.
这一异常现象证实了这样一种信念：COVID-19 疫情期间股市的波动不能简单地用经济损失来解释。

This paper explores the contribution of sentiment to stock market volatility during the epidemic by testing the following hypotheses. Two conditions should be met when major events affect stock returns through sentiment (Shan and Gong, 2012). First, the event leads to strong negative sentiment, such as panic and anxiety. Previous studies argued that public health hazards such as SARS and Ebola can affect market sentiment (Tao, 2010). In the case of COVID-19, Liu et al. (2020) found that the virus outbreak had raised investors' fear of uncertainty. Baig et al. (2020) found that the overall sentiment declined during the pandemic. Second, the event causes lower yields on related stocks than usual. Donadelli et al. (2017) and Ichev and Marinč (2018) found that media coverage of pandemics had an impact on the stock prices of companies closer to the origin area and in the pharmaceutical industry. As for the pandemic this time, Baker et al. (2020) examined the US stock market and held that no previous infectious disease outbreak impacted the stock market as powerfully as COVID-19 did.
本文通过检验以下假设来探讨疫情期间情绪对股市波动的贡献。当重大事件通过情绪影响股票回报时，需要满足两个条件（Shan and Gong，2012）。首先，事件引发强烈的恐慌、焦虑等负面情绪。之前的研究认为，SARS 和埃博拉等公共卫生危害会影响市场情绪（Tao，2010 年）。就 COVID-19 而言，Liu 等人。（2020）发现病毒爆发引发了投资者对不确定性的恐惧。拜格等人。（2020）发现，大流行期间整体情绪下降。其次，该事件导致相关股票的收益率低于平时。多纳德利等人。 (2017) 以及 Ichev 和 Marinč (2018) 发现，媒体对流行病的报道对靠近原产地和制药行业的公司的股价产生了影响。至于这次的大流行，贝克等人。（2020）研究了美国股市，认为之前没有任何传染病爆发像 COVID-19 那样对股市产生如此强烈的影响。

Event analysis is often used to measure the influence of major health emergencies. However, it fails to explore the contribution of different factors. Herein, we apply both event study and regression analysis in this study. First, we calculate the abnormal returns of the stock market during the pandemic and conduct a significance test. Then, we explore whether sentiment is explanatory to abnormal returns by regression.
事件分析通常用于衡量重大突发卫生事件的影响。然而，它未能探讨不同因素的贡献。在此，我们在本研究中应用事件研究和回归分析。首先，我们计算了疫情期间股市的超常收益，并进行了显着性检验。然后，我们通过回归探讨情绪是否可以解释异常回报。
We also make an investigation on which kinds of stocks are more susceptible to sentiment during the pandemic.
我们还调查了哪些股票在疫情期间更容易受到市场情绪的影响。
The main findings suggest that the epidemic has no significant effect on Chinese stock market except the pharmaceutical industry at first. As the epidemic spreads, significantly negative abnormal returns appear.
主要研究结果表明，疫情初期对除医药行业外的中国股市没有显着影响。随着疫情蔓延，出现显着负的异常收益。
The abnormal returns cannot be explained simply by real economic loss, so we turn to the spotlight on investor sentiment. Our results show that individual investor sentiment is positively correlated with stock market returns during the epidemic.
异常回报不能简单地用实际经济损失来解释，因此我们将目光转向投资者情绪。我们的研究结果表明，疫情期间个人投资者情绪与股市回报呈正相关。
Furthermore, stocks with high PB, PE, CMV, net asset and institutional shareholder ratios, as well as long listed years are more likely to be affected by the epidemic.
此外，PB、PE、CMV、净资产和机构股东比率高、上市年限长的股票更容易受到疫情影响。

This study contributes to the literature in three ways. First, we contribute to the studies that have examined the stock market response to widespread disasters and provide empirical evidences for the sentiment effect in the Chinese stock market.
这项研究在三个方面对文献做出了贡献。首先，我们致力于研究股市对大范围灾难的反应，并为中国股市的情绪效应提供实证证据。
Second, unlike most existing works that focus on overall market performance, we examine the effect of COVID-19 on individual investors, who are more susceptible to emotion and account for over 80% of the trading volume in the Chinese stock market.² Third, the sentiment is derived from analysis on big data of opinions text extracted from social platforms, in this way ensuring the sample size and credibility compared with investigations and market indicators.
其次，与大多数关注整体市场表现的现有作品不同，我们研究了COVID-19对个人投资者的影响，他们更容易受到情绪影响，占中国股市交易量的80%以上。 ² 第三，情绪是通过对社交平台上提取的观点文本大数据进行分析得出的，这样可以保证样本量和与调查和市场指标相比的可信度。

2. Data and methodology 2. 数据和方法

2.1. Data 2.1.数据

Stock-related financial data are from the CSMAR database³ covering the period from 25 July 2019 to 31 March 2020. A-share listed companies are selected as samples. In the panel data, we exclude samples with negative net assets in the annual report, with ST or PT designations,⁴ within the financial sector and with missing values. The final sample number is 1914. The variables of enterprise features are processed by winsorizing at the level of 1% to avoid the influence of extreme values. The companies are then divided into 71 industries according to the China Securities Regulatory Commission.⁵
股票相关财务数据来自CSMAR数据库 ³ ，涵盖2019年7月25日至2020年3月31日，选取A股上市公司作为样本。在面板数据中，我们排除了年报净资产为负、ST或PT名称、 ⁴ 属于金融行业且存在缺失值的样本。最终样本数为1914。企业特征变量按1%水平进行缩尾处理，避免极值的影响。根据中国证监会的规定，这些公司被分为71个行业。 ⁵

The sentiment data used in this work is GubaSenti established by International Institute of Big Data in Finance, BNU(http://ifind.bnu.edu.cn/), which captures the individual investor sentiment by text analytics on opinions from Guba – the biggest online financial social platform in China for individual investors to share and exchange their opinions and experiences on stocks (Sun et al., 2018, Sun et al., 2017).
本文使用的情绪数据是北京师范大学国际金融大数据研究院（http://ifind.bnu.edu.cn/）建立的GubaSenti，通过对来自Guba的观点进行文本分析来捕捉个人投资者的情绪。中国最大的在线金融社交平台，供个人投资者分享和交流他们对股票的看法和经验（Sun et al., 2018；Sun et al., 2017）。

2.2. Event study 2.2.事件研究

Event study is applied in this work to identify abnormal returns in the stock market from the outbreak of COVID-19.
这项工作应用事件研究来识别 COVID-19 爆发后股市的异常回报。
January 20, 2020 is set as the event day when Nanshan Zhong, the senior expert on infectious disease in China, announced in a public interview that COVID-19 could be transmitted among people.
2020年1月20日被定为中国传染病高级专家钟南山在公开采访中宣布COVID-19可能在人际传播的事件日。
It is the first time that human-to-human transmission has been confirmed possible officially. Existing literatures are not unanimous on the length of the estimation window. In this study, as shown in
这是首次正式确认人传人的可能性。现有文献对于估计窗口的长度并不一致。在本研究中，如图所示Fig. 1, we choose the 100 days before the event date as the estimation window. To observe the reversal effect, we also define the event window as 10 trading days after the event day and the post-event window as 10 days to 45 days after the event date.
图 1，我们选择事件日期前 100 天作为估计窗口。为了观察反转效果，我们还将事件窗口定义为事件日后10个交易日，将事件后窗口定义为事件日后10天至45天。
To avoid contaminated data, a 20-day gap is set between the event date and estimation window so that to capture the true abnormal returns. In the sensitive test, we estimate the 100, 50 and 150-day estimation window and gap respectively. The conclusions are robust.
为了避免数据受到污染，事件日期和估计窗口之间设置了20天的差距，以便捕获真实的异常收益。在敏感测试中，我们分别估计100天、50天和150天的估计窗口和缺口。结论是稳健的。

The expected returns are derived using the Fama–French model. The ordinary least squares (OLS) regression is based on the following model:(1) $R_{i, t} = α + γ M K T_{t} + δ S M B_{t} + η H M L_{t} + ε_{t}$ where R_i,t represents the return of index i on date t in the estimation window, and MKT_t, SMB_t and HML_t are the three factors of the Fama–French model. Abnormal returns are calculated as follows:(2) $A R_{t} = R_{t} - [\hat{α} + \hat{γ} M K T_{t} + \hat{δ} S M B_{t} + \hat{η} H M L_{t}]$ where R_t represents the actual return on date t in the event window. To measure the total impact of an event over a particular period (termed the “event window”), we add up individual abnormal returns to create a “cumulative abnormal return (CAR)” as follows:(3) $C A R = \sum_{t = 1}^{n} A R_{t}$
预期回报是使用 Fama-French 模型得出的。普通最小二乘 (OLS) 回归基于以下模型： (1) $R_{i, t} = α + γ M K T_{t} + δ S M B_{t} + η H M L_{t} + ε_{t}$ ，其中 R _i,t 表示估计窗口中日期 t 的索引 i 的返回值，MKT _t 、SMB _t 和 HML _t 是 Fama-French 模型的三个因素。异常收益计算如下： (2) $A R_{t} = R_{t} - [\hat{α} + \hat{γ} M K T_{t} + \hat{δ} S M B_{t} + \hat{η} H M L_{t}]$ ，其中R _t 表示事件窗口中t日的实际收益。为了衡量某一事件在特定时期（称为“事件窗口”）内的总体影响，我们将各个异常收益相加以创建“累积异常收益（CAR）”，如下所示： (3) $C A R = \sum_{t = 1}^{n} A R_{t}$

Finally, after identifying all abnormal returns and cumulative abnormal returns during the event window, parametric tests are conducted to test significance. Evidence generally suggests that distributions of daily abnormal returns are relative to a normal distribution (Fama, 1965). According to it, our null hypothesis is CAR=0. If the epidemic has a significant positive impact on the stock price, the t-statistic should be significantly positive and vice versa.
最后，在识别出事件窗口内的所有异常收益和累计异常收益后，进行参数检验以检验显着性。证据通常表明每日异常收益的分布与正态分布相关（Fama，1965）。据此，我们的零假设是 CAR=0。如果疫情对股价有显着的正向影响，则t统计量应该显着为正，反之亦然。

In the robustness test, we change the length of the estimated window and the OLS regression model. The results remain consistent.
在稳健性测试中，我们改变了估计窗口的长度和OLS回归模型。结果保持一致。

2.3. Panel regression model
2.3.面板回归模型

Compared with an event study, a panel regression can better capture the time-varying relationship between dependent and independent variables due to its ability to extract changes from panel data and minimize estimation bias.
与事件研究相比，面板回归能够更好地捕捉因变量和自变量之间的时变关系，因为它能够从面板数据中提取变化并最大限度地减少估计偏差。
Therefore, panel regression is used to control heterogeneity during estimation of the sentiment effect. The model is as follows:(4) $R_{i, t} = α_{0} + φ M K T_{t} + δ S M B_{t} + η H M L_{t} + γ_{1} S E N T_{i, t} + γ_{2} E W_{t} + γ_{3} L_{i} + ε_{i, t}$ where R_i,t represents the return of individual stock i on day t and SENT_i,t represents the sentiment index of individual stock i on day t. EW_t is a dummy variable representing the event window (0: the event window, 1: the post-event window). L_i is the dummy variable representing the region, and the values are 2, 1 and 0, which respectively indicate whether the company is located in Wuhan, other cities in Hubei and other regions in China. First, we run the regression using SENT as the independent variable alone and then gradually add EW and L to test the regional and reversal effects. To select between random or fixed effects, the Hausman test was implemented to verify the following hypothesis for each group. The use of the random or fixed effects model depended completely on the p-value.
因此，面板回归用于在情绪效应估计过程中控制异质性。模型如下： (4) $R_{i, t} = α_{0} + φ M K T_{t} + δ S M B_{t} + η H M L_{t} + γ_{1} S E N T_{i, t} + γ_{2} E W_{t} + γ_{3} L_{i} + ε_{i, t}$ ，其中R _i,t 代表个股i在t天的收益，SENT _i,t 代表个股i在t天的情绪指数天t。 EW _t 是代表事件窗口的虚拟变量（0：事件窗口，1：事件后窗口）。 L为代表地区的虚拟变量，取值为2、1、0，分别表示该公司是否位于武汉、湖北其他城市以及国内其他地区。首先，我们单独使用 SENT 作为自变量进行回归，然后逐渐添加 EW 和 L 来测试区域效应和反转效应。为了在随机效应或固定效应之间进行选择，实施豪斯曼检验来验证每组的以下假设。随机或固定效应模型的使用完全取决于 p 值。
In the current study, the p-values are significant, so the null hypothesis is rejected, and we move on to the fixed effects model. To ensure the robustness of the results, we also conduct feasible generalized least squares (FGLS) estimation.
在当前的研究中，p 值显着，因此拒绝零假设，我们继续使用固定效应模型。为了确保结果的稳健性，我们还进行了可行的广义最小二乘（FGLS）估计。

Considering that the investor sentiment and stock return may synchronously affect each other, endogeneity might exist and cause estimation bias.
考虑到投资者情绪和股票收益可能同步影响，可能存在内生性并导致估计偏差。
Therefore, the number of newly diagnosed sentiments in the previous day was taken as a proxy for investor sentiment in the robustness test. The results show that though endogeneity exists, the estimation results are consistent.
因此，在稳健性检验中，将前一日新增情绪诊断数量作为投资者情绪的代理。结果表明，虽然存在内生性，但估计结果是一致的。

3. Empirical findings 3. 实证结果

3.1. Event-study reports 3.1.事件研究报告

Table 1 reports descriptive statistics. According to Panel B, stock returns and individual investor sentiment both react negatively after the event day. The standard deviation increases during the event window, which indicates that Chinese stock market yield decreases and volatility increases due to the epidemic. In addition to the event effect, the reversal effect is also observed.
表 1 报告了描述性统计数据。根据 B 组的数据，活动当天之后，股票回报率和个人投资者情绪均出现负面反应。事件窗口内标准差增大，表明受疫情影响，中国股市收益率下降，波动性增大。除了事件效应之外，还观察到逆转效应。
In the post-event window, both the return and investor sentiment rise, even exceeding the average level before the outbreak of COVID-19.
在事后窗口，回报率和投资者情绪均有所上升，甚至超过了COVID-19爆发前的平均水平。

Table 1. Summary Statistics.
表 1. 统计摘要。

Variable 多变的	Observations 观察结果	Mean 意思是	SD	Min 最小	Max 最大限度
Panel A: Estimation Window A 组：估计窗口
Market return 市场回报	100	0.001	0.008	-0.019	0.022
Stock return 库存回报	324,729	0.001	0.022	-0.102	0.103
Sentiment 情绪	324,729	0.486	2.636	-19.880	21.463
Panel B: Event Window 面板 B：事件窗口
Market return 市场回报	10	-0.005	0.030	-0.080	0.020
Stock return 库存回报	31,475	-0.005	0.046	-0.103	0.104
Sentiment 情绪	31,475	0.431	2.728	-10.912	12.024
Panel C: Post-event Window 面板 C：事件后窗口
Market return 市场回报	36	-0.001	0.018	-0.039	0.034
Stock return 库存回报	106,164	0.000	0.034	-0.109	0.103
Sentiment 情绪	106,164	0.627	2.550	-10.533	11.527

Notes: Table 1 reports summary statistics of the comprehensive A-share market daily return, sample stock daily return and investor sentiment. In panel A, the sample period is from July 25, 2019 to December 19, 2019. In panel B, the sample period is from January 20, 2020 to February 10, 2020.
注：表1为A股市场综合日收益、样本股日收益及投资者情绪的汇总统计。在面板A中，样本期为2019年7月25日至2019年12月19日。在面板B中，样本期为2020年1月20日至2020年2月10日。
In panel C, the sample period is from February 11, 2020 to March 31, 2020. The market return and stock return data are derived from CSMAR database. The sentiment data is GubaSenti established by International Institute of Big Data in Finance, BNU(http://ifind.bnu.edu.cn/).
图C中，样本期为2020年2月11日至2020年3月31日。市场收益和股票收益数据来源于CSMAR数据库。情绪数据为北京师范大学国际金融大数据研究院建立的GubaSenti（http://ifind.bnu.edu.cn/）。

Table 2 reports the cumulative abnormal returns of the different event windows and market divisions.
表 2 报告了不同事件窗口和市场划分的累积异常回报。
The results show that the cumulative abnormal return in the event window is positive, which means that the epidemic has a significant positive impact on the stock price in the short term.
结果显示，事件窗口内累计超常收益为正，意味着疫情短期内对股价产生显着的正向影响。
The second result, about pharmaceutical stocks, shows that the t-value is significantly positive, indicating the significant positive impact the epidemic has on the stock prices of pharmaceutical manufacturers. The event effect is insignificant for companies in Hubei.
第二个关于医药股的结果显示，t值显着为正，表明疫情对医药制造商的股价产生显着的积极影响。该事件对湖北企业影响不大。

Table 2. Cumulative abnormal return for different event windows.
表 2. 不同事件窗口的累计异常回报。

Indices 指数	Average CAR 平均汽车	T-Stats. T-统计数据。	Average CAR 平均汽车	T-Stats. T-统计数据。
Event Windows 事件窗口	[0,9]	Empty Cell	[10,45]	Empty Cell
Overall Market 整体市场	0.007	4.010***	-0.016	-6.991***
Non-Pharmaceutical Industry 非医药行业	0.000	-0.062	-0.015	-6.305***
Pharmaceutical Industry 医药行业	0.120	16.372***	-0.033	-3.441***
Hubei Province 湖北省	0.009	0.729	-0.013	-0.880

Notes: Table 2 provides the event-study result. The event dates and windows are defined as in Fig. 1. CAR denotes mean cumulative abnormal returns. ***, **, * indicate significance at the 1%, 5%, and 10% levels, respectively.
注：表 2 提供了事件研究结果。事件日期和窗口的定义如图 1 所示。CAR 表示平均累积异常收益。 ***、**、* 分别表示在 1%、5% 和 10% 水平下的显着性。

Table 3. Cumulative abnormal return in different industries during event windows.
表3 事件窗口期各行业累计超额收益。

Empty Cell	Obs. 观察。	Average CAR 平均汽车	T-Stats. T-统计数据。	Weight 重量
Pharmaceutical Industry 医药行业	197	0.120	16.372***	23.12%
Manufacture of computers, communication and other electronic equipment 计算机、通讯及其他电子设备制造	293	0.033	4.879***	9.46%
Software and information technology services 软件和信息技术服务	177	0.044	5.159***	7.62%
Real estate 房地产	108	-0.052	-10.837***	5.49%
Manufacture of special purpose machinery 专用机械制造	177	0.025	2.849***	4.33%
Manufacture of chemical raw materials and chemical products 化学原料及化学制品制造业	203	0.015	2.098**	2.98%
Internet and related services 互联网及相关服务	49	0.051	2.747***	2.44%
Business Service Industry 商业服务业	43	-0.057	-6.695***	2.40%
Other Industries 其他行业	1919	-0.010	-5.002***	42.16%

Notes: Table 3 provides the event-study result in different industries during event windows. The event dates and windows are defined as in Fig. 1. CAR denotes cumulative abnormal returns. Obs. denotes the number of stocks in the industry. Weight measures the extent of the impact on the average CAR of the overall market and is calculated as below.
注：表 3 提供了事件窗口期间不同行业的事件研究结果。事件日期和窗口的定义如图1所示。CAR表示累积异常收益。观察。表示该行业的股票数量。权重衡量的是对整个市场平均CAR的影响程度，计算公式如下。

$W e i g h t = \frac{The number of stocks in industry i \times Average C A R of industry i}{\sum_{n = 75}^{i} The number of stocks in industry i \times Average C A R of industry i}$

***, **, * indicate significance at the 1%, 5%, and 10% levels, respectively. In the event-study data, we do not exclude samples within the financial sector. Other Industries include all the 66 industries except the 9 industries listed above. See Appendix B for further details.
***、**、* 分别表示在 1%、5% 和 10% 水平下的显着性。在事件研究数据中，我们不排除金融领域的样本。其他行业包括除上述9个行业之外的全部66个行业。详细信息请参见附录 B。

These conclusions seem to be opposed to results of stock returns. Therefore, we explore the pharmaceutical industry and found that the cumulative abnormal return in pharmaceutical industry is far higher than the average and the sample size from pharmaceutical industry is large.
这些结论似乎与股票收益的结果相反。因此，我们对医药行业进行探索，发现医药行业的累计异常收益远高于平均水平，且医药行业样本量较大。
As a result, its impact on the average CAR of the whole market reached 23.12%, leading to a bias in the overall results. Excluding the pharmaceutical industry, there was no significant cumulative abnormal return in the overall market during the event window.
其对整个市场平均CAR的影响达到23.12%，导致整体结果出现偏差。除医药行业外，事件窗口期整体市场不存在重大累计异常收益。
The results can be explained perfectly by the fact that the stocks in pharmaceutical industry were highly addressed by the investors during the epidemic.
医药行业个股在疫情期间受到了投资者的高度关注，就可以完美地解释这一结果。
Similarly, due to quarantine, digitalization and information technology have become a magnet of investment, thus the abnormal returns of related
同样，由于隔离，数字化和信息技术成为投资磁石，相关的超常回报也随之而来。industries are also significantly higher than other industries. In addition, during the event window, the epidemic is still at an early stage and its future is uncertain, so its impact on other industries is relatively limited.
行业也明显高于其他行业。此外，在活动窗口期，疫情仍处于早期阶段，未来存在不确定性，因此对其他行业的影响相对有限。

In the post-event window, the cumulative abnormal return of the overall market decreases.
事后窗口期，整体市场累计超额收益下降。
The cumulative abnormal return of the overall market is significantly negative in this period, which means that the impact of the epidemic still exists in the long term and has a significant positive impact on the stock price.
本期整体市场累计超常收益显着为负，意味着疫情影响长期依然存在，对股价产生显着正向影响。
This phenomenon can be explained by the spread of the epidemic, which results in the extensive impact on the stock market and economy.
这种现象可以用疫情蔓延对股市和经济造成广泛影响来解释。

We then test the differences in the cumulative abnormal return of stocks belonging and not belonging to firms in the pharmaceutical industry, and the registered places are in Hubei or other areas.
然后我们检验注册地为湖北或其他地区的医药行业股票与非医药行业股票的累计超常收益差异。
The result shows that the abnormal return of pharmaceutical stocks is significantly higher than that of nonpharmaceutical stocks, while the cumulative abnormal returns of stocks registered in Hubei Province show no significant differences between those of other regions.
结果显示，医药股超额收益率显着高于非医药股，而湖北省注册股票累计超额收益率与其他地区差异不显着。
One possible explanation is that the stocks of the pharmaceutical industry have some inherent characteristics that lead to excess returns that have never been discovered. However, the report in Table 4 shows that the difference between them is not as significant as it is before the pandemic, which supports the viewpoint that the industrial differences are caused by this epidemic and that the market is irrational.
一种可能的解释是，医药行业的股票有一些固有的特征，导致了从未被发现的超额收益。但从表4的报告来看，两者之间的差异并不像疫情前那么显着，这支持了行业差异是由这次疫情造成的、市场非理性的观点。

Table 4. Difference tests between industries and regions.
表4.行业和地区之间的差异测试。

Indices 指数	CAR	P-Value P值	CAR	P-Value P值	CAR	P-Value P值
Event Windows 事件窗口	[-120, -21] [-120，-21]	Empty Cell	[0,9]	Empty Cell	[10,45]	Empty Cell
Pharmaceutical industry 医药行业	0.024	-0.031***	0.120	-0.120***	-0.015	0.018*
Non-Pharmaceutical 非医药品	-0.007	-0.031***	-0.000	-0.120***	-0.033	0.018*

Notes: Table 4 provides the results of tests of difference. The event dates and windows are defined as in Fig. 1. Pharmaceutical (Non-pharmaceutical) industry stocks are grouped according to the Industry Classification Scheme released by China Securities Regulatory Commission in 2020 Q1. CAR denotes mean cumulative abnormal returns. ***, **, * indicate significance at the 1%, 5%, and 10% levels, respectively.
注：表 4 提供了差异检验的结果。活动日期和窗口定义如图1。医药（非医药）行业股票按照中国证监会2020年第一季度发布的行业分类方案进行分组。 CAR表示平均累积异常收益。 ***、**、* 分别表示在 1%、5% 和 10% 水平下的显着性。

3.2. Panel regression reports
3.2.面板回归报告

The results in Table 5 prove that sentiment can significantly affect the overall market return during the epidemic. It also supports the position that the reversal effect is significant, indicating that stock returns depreciated during the post-event window.
表5的结果证明，疫情期间情绪可以显着影响整体市场回报。它还支持反转效应显着的立场，表明股票回报在事件后窗口期间贬值。

Table 5. Panel data regression results of sentiment, reverse and region effect.
表5.情绪、反向和区域效应的面板数据回归结果。

Empty Cell	Sentiment Effect 情绪效应		Reverse Effect 反向效应		Region Effect 区域效应
Empty Cell	FE	FGLS	FE	FGLS	FE	FGLS
SENT 发送_i	0.005***	0.004***	0.005***	0.004***	0.005***	0.004***
Empty Cell	(114.55)	(113.85)	(114.79)	(114.07)	(114.79)	(114.07)
MKT	1.010***	1.011***	1.016***	1.017***	1.016***	1.017***
Empty Cell	(187.81)	(187.86)	(187.52)	(187.57)	(187.52)	(187.57)
SMB	0.604***	0.607***	0.625***	0.628***	0.625***	0.628***
Empty Cell	(54.62)	(54.86)	(55.40)	(55.65)	(55.40)	(55.65)
HML	-0.108***	-0.120***	-0.058***	-0.070***	-0.058***	-0.070***
Empty Cell	(-5.09)	(-5.63)	(-2.67)	(-3.18)	(-2.67)	(-3.18)
EW			-0.002***	-0.002***	-0.002***	-0.002***
Empty Cell			(-9.27)	(-9.31)	(-9.27)	(-9.31)
Location 地点
Other Hubei regions 湖北其他地区					0	-0.000
Other Hubei regions 湖北其他地区					(.)	(-0.29)
Wuhan City 武汉市					0	-0.001
Empty Cell					(.)	(-0.77)
α₀ α ₀	-0.003***	-0.002***	-0.001***	-0.001***	-0.001***	-0.001***
Empty Cell	(-25.43)	(-23.89)	(-4.74)	(-3.92)	(-4.74)	(-3.85)
adj. R-sq 形容词R平方	0.477		0.477		0.477
F	17404.1		13956.8		13956.8
N	74455	74455	74455	74455	74455	74455

Notes: Table 5 provides the results from the estimation of the following regression specification.
注：表 5 提供了以下回归规范的估计结果。

$R_{i, t} = α_{0} + φ M K T_{t} + δ S M B_{t} + η H M L_{t} + γ_{1} S E N T_{i, t} + γ_{2} E W_{t} + γ_{3} L_{i} + ε_{i, t}$

Dependent variable R_i,t represents the return of individual stock i on day t and SENT_i,t represents the sentiment index of individual stock i on day t. EW_t is a dummy variable representing the event window(0: the event window, 1: the post-event window). L_i is the dummy variable representing the region, and the values are 2, 1 and 0, which respectively indicate whether the company is located in Wuhan, other cities in Hubei and other regions in China. First, we run the regression using SENT as the independent variable alone and then gradually add EW and L to test the regional and reversal effects. The adjusted R squares of the three fixed effect models are approximately equal. That is to say, the reverse and regional effect dummy variables have little effect on improving the fitting degree. Accordingly, the coefficients of EW and L are close to zero. T-stats are in parentheses below the coefficient estimates. ***, **, * indicate significance at the 1%, 5%, and 10% levels, respectively.
因变量R _i,t 代表个股i在t日的收益，SENT _i,t 代表个股i在t日的情绪指数。 EW 是代表事件窗口的虚拟变量（0：事件窗口，1：事件后窗口）。 L为代表地区的虚拟变量，取值为2、1、0，分别表示该公司是否位于武汉、湖北其他城市以及国内其他地区。首先，我们单独使用 SENT 作为自变量进行回归，然后逐渐添加 EW 和 L 来测试区域效应和反转效应。三个固定效应模型的调整后的 R 平方大致相等。也就是说，反向虚拟变量和区域效应虚拟变量对于提高拟合度作用不大。因此，EW和L的系数接近于零。 T 统计量位于系数估计值下方的括号中。 ***、**、* 分别表示在 1%、5% 和 10% 水平下的显着性。

There are three possible explanations for this phenomenon. First, as a result of the high infection rate and large volume of news concerning COVID-19, negative feelings are transmitted across China without distinction.
对于这种现象有三种可能的解释。首先，由于有关COVID-19的高感染率和大量新闻，负面情绪在中国各地无差别地传播。
Second, the Spring Festival is included in the event window, which is the transportation peak during the year.
二是春节纳入活动窗口，是年内的交通高峰。
A large number of people traveled between Wuhan, Hubei Province and other Chinese cities during that time, which improved seriously the risk of infection and anxiety about the future.
期间大量人员往返于湖北省武汉市和中国其他城市之间，大大提高了感染风险和对未来的焦虑。
In addition, due to poor data availability, only companies registered in Hubei and Wuhan are included in the sample, thus excluding those who are not registered but conduct major business there.
此外，由于数据可获得性较差，样本中仅包含在湖北和武汉注册的公司，从而排除了那些未注册但在当地开展主要业务的公司。

To explore the special impact of the epidemic on investor sentiment, the data within the estimation window for regression were used. Table 6 shows that sentiment both before and after the event day has a significant impact on the return of individual stocks. We further conduct the Chows test to examine the coefficient difference.
为探究疫情对投资者情绪的特殊影响，采用估计窗口内的数据进行回归。表6显示，活动日前后的情绪对个股的回报有显着影响。我们进一步进行 Chows 检验来检查系数差异。
For FE and FGLS, the P values are both 0.000. The results indicate that the impact of sentiment during the epidemic has increased significantly from its usual level.
对于 FE 和 FGLS，P 值均为 0.000。结果表明，疫情期间情绪影响较平时水平显着上升。

Table 6. Panel data regression results of sentiment before and after the pandemic.
表6. 疫情前后情绪面板数据回归结果。

Empty Cell	[-100, -1] [-100，-1]	[0, 45]	[-100, -1] [-100，-1]	[0, 45]
Empty Cell	FE	FE	FGLS	FGLS
SENT_i,t 已发送 _i,t	0.003***	0.005***	0.002***	0.004***
Empty Cell	(-194)	(-114.55)	(-191.07)	(-113.85)
MKT	1.020***	1.010***	1.023***	1.011***
Empty Cell	(-199.69)	(-187.81)	(-199.81)	(-187.86)
SMB	0.733***	0.604***	0.738***	0.607***
Empty Cell	(-110.44)	(-54.62)	(-110.86)	(-54.86)
HML	-0.102***	-0.108***	-0.0996***	-0.120***
Empty Cell	(-11.94)	(-5.09)	(-11.67)	(-5.63)
α₀ α ₀	-0.001***	-0.002***	-0.001***	-0.002***
Empty Cell	(-35.58)	(-25.43)	(-33.34)	(-23.89)
adj. R-sq 形容词R平方	0.272	0.477
F	30474.9	17404.1
N	317901	74455	317901	74455
F	2988.71		754.78
P-value P值	0.000***		0.000***

Notes: Table 6 provides the results from the estimation of the following regression specification.
注：表 6 提供了以下回归规范的估计结果。

$R_{i, t} = α_{0} + φ M K T_{t} + δ S M B_{t} + η H M L_{t} + γ_{1} S E N T_{i, t} + ε_{i, t}$

Dependent variable R_i,t represents the return of individual stock i on day t and SENT_i,t represents the sentiment index of individual stock i on day t. In panel [-100, -1], the sample period is from August 22, 2019 to January 19, 2020. In panel [0, 45], the sample period from is from January 20, 2020 to to March 31, 2020. T-stats are in parentheses below the coefficient estimates.
因变量R _i,t 代表个股i在t日的收益，SENT _i,t 代表个股i在t日的情绪指数。在面板[-100, -1]中，样本期为2019年8月22日至2020年1月19日。在面板[0, 45]中，样本期为2020年1月20日至2020年3月31日。 T 统计量位于系数估计值下方的括号中。
***, **, * indicate significance at the 1%, 5%, and 10% levels, respectively. The last two rows are the results of the coefficient difference test.
***、**、* 分别表示在 1%、5% 和 10% 水平下的显着性。最后两行是系数差异检验的结果。

3.3. Characteristic effect
3.3.特色效应

We set up an interactive variable in model (5) to study the performance of stocks with different characteristics during the epidemic.(5) $R_{i, t} = φ_{0} + φ_{1} S E N T_{i, t} + φ_{2} S E N T_{i, t} \times F e a t u r e_{i} + γ M K T_{t} + δ S M B_{t} + η H M L_{t} + ε_{t}$
我们在模型（5）中设置了一个交互变量来研究疫情期间不同特征股票的表现。 (5) $R_{i, t} = φ_{0} + φ_{1} S E N T_{i, t} + φ_{2} S E N T_{i, t} \times F e a t u r e_{i} + γ M K T_{t} + δ S M B_{t} + η H M L_{t} + ε_{t}$

In the above, Feature_i represents characteristics of stock i. Table 7 indicates that stocks with higher P/E ratio, P/B ratio, and circulating market value, and lower institutional shareholding ratio and net asset, are more susceptible to sentiment during the epidemic.
其中，Feature代表股票i的特征。表7表明，市盈率、市净率和流通市值较高、机构持股比例和净资产较低的股票在疫情期间更容易受到情绪影响。
This is possibly because the more resilient a company is to unexpected risks, the less anxious its investors are.
这可能是因为公司对意外风险的抵御能力越强，投资者的焦虑就越少。

Table 7. Panel data regression results of stocks with different characteristics.
表7.不同特征股票的面板数据回归结果。

Empty Cell	FE	FGLS
Empty Cell	R_i,t	R_i,t
SENT_i,t 已发送 _i,t	0.003***	0.003***
Empty Cell	(23.66)	(25.30)
SENT_i,t × PE_i 已发送 _i,t × PE	8.29E-06***	6.96E-06***
Empty Cell	(14.64)	(13.23)
SENT_i,t × PB_i,t 已发送 _i,t × PB _i,t	3.06E-04***	3.08E-04***
Empty Cell	(13.84)	(15.27)
SENT_i,t × CMV_i,t 已发送 _i,t × CMV _i,t	2.84E-14***	1.12E-14***
Empty Cell	(8.44)	(3.97)
SENT_i,t × Year_i 已发送 _i,t × 年	1.63E-05***	1.57E-05***
Empty Cell	(2.61)	(2.73)
SENT_i,t × ISR_i 已发送 _i,t × ISR	-1.40E-05	-1.55E-05*
Empty Cell	(-1.47)	(-1.78)
SENT_i,t × NA_i 已发送 _i,t × NA	-3.22E-14***	-2.34E-14***
Empty Cell	(-6.31)	(-5.07)
MKT_t	1.013***	1.015***
Empty Cell	(174.33)	(174.33)
SMB_t 中小型企业 _t	0.581***	0.585***
Empty Cell	(48.57)	(48.85)
HML_t	-0.111***	-0.123***
Empty Cell	(-4.84)	(-5.35)
ϕ₀ φ ₀	-0.003***	-0.002***
Empty Cell	(-23.68)	(-21.77)
adj. R-sq 形容词R平方	0.487
F	6023.2
N	61832	61832

Notes: Table 7 provides the results from the estimation of the following regression specification.
注：表 7 提供了以下回归规范的估计结果。

$R_{i, t} = φ_{0} + φ_{1} S E N T_{i, t} + φ_{2} S E N T_{i, t} \times F e a t u r e_{i} + γ M K T_{t} + δ S M B_{t} + η H M L_{t} + ε_{t}$

Dependent variable R_i,t represents the return of individual stock i on day t and SENT_i,t represents the sentiment index of individual stock i on day t. Feature_i represents characteristics of stock i, including PE, PB, CMV, Year, ISR and NA. PE is the price earnings ratio calculated by daily stock market value and net income attributable to the parent company for the last four quarters. PB is the Price to book ratio calculated by daily stock market value and net asset published in the latest financial report. CMV is the current market value calculated by the daily number of tradable shares time the daily stock price. ISR denotes the institutional shareholding ratio. Year denotes the listed year by the event day January 20, 2020. NA denotes the amount of company net asset according to the latest annual report. The stock characteristics data are derived from CSMAR. T-stats are in parentheses below the coefficient estimates. ***, **, * indicate significance at the 1%, 5%, and 10% levels, respectively.
因变量R _i,t 代表个股i在t日的收益，SENT _i,t 代表个股i在t日的情绪指数。 feature代表股票i的特征，包括PE、PB、CMV、Year、ISR和NA。 PE是根据每日股票市值和最近四个季度归属于母公司的净利润计算得出的市盈率。 PB是根据每日股票市值和最新财务报告公布的净资产计算得出的市净率。 CMV 是按每日流通股数量乘以每日股价计算得出的当前市值。 ISR表示机构持股比例。年份为截止2020年1月20日的上市年度。NA为根据最近一期年报的公司净资产数额。股票特征数据来源于CSMAR。 T 统计量位于系数估计值下方的括号中。 ***、**、* 分别表示在 1%、5% 和 10% 水平下的显着性。

3.4. Industry effect 3.4.产业效应

During the epidemic, different industries have experienced different turbulences (Baek et al., 2020; Mazur et al., 2020). From our observation, due to the significant increase in demand for pharmaceutical products, most of the top 10 stocks with a cumulative excess return during the event window are related to the pharmaceutical industry.
疫情期间，不同行业经历了不同的动荡（Baek et al., 2020；Mazur et al., 2020）。据我们观察，由于医药产品需求大幅增长，事件窗口期累计超额收益前10名的个股大多与医药行业相关。
In the post-event window, the food industry has become a new focus due to the food shortage alert released by the UN. Therefore, the role of industry effects in epidemics is worth exploring.
事后窗口期，食品行业因联合国发布粮食短缺警报而成为新焦点。因此，行业效应在疫情中的作用值得探讨。

We take the comprehensive class in the industry category as the benchmark group, thereby adding the industry dummy variable to model 4. The results show that nearly half of the industries are significantly affected by sentiment, as 37 of the 71 dummy variables are significant.
我们以行业类别中的综合类作为基准组，从而在模型4中添加行业虚拟变量。结果显示，近一半的行业受到情绪的显着影响，71个虚拟变量中有37个显着。
Among them, 30 industries have a weakening effect on the positive impact of sentiment. The five industries with the most negative impact are listed in Table 8. The reason may lie in the oversupply of oil and the economic downturn, as well as the restrictions on travel.
其中，30个行业对情绪的积极影响呈减弱趋势。表8列出了负面影响最大的五个行业。原因可能在于石油供应过剩和经济低迷，以及旅行限制。
The sentiment effect of only 7 industries—the Pharmaceutical Industry, Internet and Related Services, Processing of Food from Agricultural Products, Software and Information Technology Services, Manufacturing of Computers, Communication and Other Electronic Equipment, Farming and Education—is boosted.
仅医药工业、互联网及相关服务业、农产品食品加工、软件和信息技术服务业、计算机、通信及其他电子设备制造业、农业、教育等7个行业景气效应有所提升。
We can find these industries are related to pharmacy, digitalization including online education, and food crises. Only parts of the regression results are shown in Table 8 (see more in Appendix C).
我们可以发现这些行业与制药、包括在线教育在内的数字化以及粮食危机有关。表 8 中仅显示了部分回归结果（更多内容请参见附录 C）。

Table 8. Panel data regression results of stocks in different industries.
表8. 不同行业股票面板数据回归结果。

Empty Cell	FE	FGLS
Empty Cell	R_i,t	R_i,t
SENT_i,t 已发送 _i,t	0.005***	0.004***
Empty Cell	(8.09)	(7.96)
SENT_i,t × Industry_i 已发送 _i,t × 行业
Empty Cell
Oil and gas extraction 石油和天然气开采	-0.004***	-0.004***
Empty Cell	(-3.23)	(-3.25)
Petroleum and nuclear power processing 石油和核电加工	-0.004***	-0.003**
Empty Cell	(-2.76)	(-2.42)
Road transport 公路运输	-0.004***	-0.003***
Empty Cell	(-4.79)	(-4.42)
Railway transport industry 铁路运输业	-0.004**	-0.003*
Empty Cell	(-2.00)	(-1.84)
Coal mining and washing 煤炭开采和洗选	-0.006***	-0.003***
Empty Cell	(-4.43)	(-4.04)
MKT_t	1.014***	1.015***
Empty Cell	(184.07)	(184.16)
SMB_t 中小型企业 _t	0.566***	0.571***
Empty Cell	(49.99)	(50.35)
HML_t	-0.068***	-0.080***
Empty Cell	(-3.11)	(-3.69)
ϕ₀ φ ₀	-0.003***	-0.003***
Empty Cell	(-25.22)	(-23.46)
adj. R-sq 形容词R平方	0.488
F	888.0
N	68083	68083

Notes: Table 8 provides the results from the estimation of the following regression specification.
注：表 8 提供了以下回归规范的估计结果。

$R_{i, t} = φ_{0} + φ_{1} S E N T_{i, t} + φ_{2} S E N T_{i, t} \times I n d u s t r y_{i} + γ M K T_{t} + δ S M B_{t} + η H M L_{t} + ε_{t}$

Dependent variable R_i,t represents the return of individual stock i on day t and SENT_i,t represents the sentiment index of individual stock i on day t. Industry_i is the dummy variable based on the Industry Classification Scheme released by China Securities Regulatory Commission in 2020 Q1. Integrated industry is used as the reference group.
因变量R _i,t 代表个股i在t日的收益，SENT _i,t 代表个股i在t日的情绪指数。行业为虚拟变量，依据证监会2020年一季度发布的行业分类方案。综合工业作为参照组。
Companies of the integrated industry have similar revenue and profit in many industries, and no obvious development planning and share holding background. T-stats are in parentheses below the coefficient estimates.
综合行业公司在多个行业的收入和利润相似，没有明显的发展规划和控股背景。 T 统计量位于系数估计值下方的括号中。
***, **, * indicate significance at the 1%, 5%, and 10% levels, respectively. See Appendix C for further details.
***、**、* 分别表示在 1%、5% 和 10% 水平下的显着性。有关更多详细信息，请参阅附录 C。

4. Conclusion 4。结论

This paper evaluates the influence of COVID-19 on China's stock market based on an event study and panel regression. This research adds to the literature, as it explores the unexpected outbreak effects on Chinese financial markets of a feared disease.
本文基于事件研究和面板回归评估了COVID-19对中国股市的影响。这项研究丰富了文献，探讨了一种令人担忧的疾病对中国金融市场的意外爆发影响。
We further prove that pandemics can cause widespread negative sentiment, thus leading to investor anxiety and market turbulence. The volatility of stock returns during the epidemic is influenced by sentiment and can't be explained solely by economic losses.
我们进一步证明，流行病会导致广泛的负面情绪，从而导致投资者焦虑和市场动荡。疫情期间股票收益波动受情绪影响，不能仅用经济损失来解释。
Stocks with different financial characteristics and in different industries are affected differently.
不同财务特征、不同行业的股票受到的影响不同。
Among the thirty seven industries with significant industry effects, the sentiment effects of only seven industries, related to the Internet, education, medical manufacturing and agricultural production, are boosted.
在行业效应显着的37个行业中，只有互联网、教育、医疗制造、农业生产等7个行业的景气效应有所提升。
The five industries in which the positive sentiment effects have been significantly weakened are all related to oil, fuel and transportation.
积极情绪效应显着减弱的五个行业均与石油、燃料和交通运输相关。

Due to the high unpredictability of epidemics, investors could get excess return by holding bellwether stocks of the pharmaceutical industry in the first stage.
由于疫情的不可预测性较高，投资者在第一阶段持有医药行业龙头股可以获得超额收益。
Then, investors should gradually reduce stockholdings in the pharmaceutical industry and increase stockholdings highlighted by government. In addition, stocks with high risk factors, such as high P/E and P/B ratios, high CMV, low institutional shareholding ratio, and low net assets, should be avoided during the middle and late stages of the epidemic.
然后，投资者应逐步减持医药行业股票，并增持政府强调的股票。此外，疫情中后期应回避高市盈率、高市净率、高CMV、低机构持股比例、低净资产等高风险因素的股票。

CRediT authorship contribution statement
CRediT 作者贡献声明

Yunchuan Sun: Supervision, Writing - review & editing, Project administration, Resources, Conceptualization, Methodology, Software, Data curation, Writing - original draft. Mengyuan Wu: Conceptualization, Software, Validation, Visualization, Investigation, Formal analysis, Writing - original draft, Writing - review & editing. Xiaoping Zeng: Supervision, Writing - review & editing. Zihan Peng: Writing - original draft, Validation, Investigation.
孙云川：监督、写作 - 审查和编辑、项目管理、资源、概念化、方法论、软件、数据管理、写作 - 初稿。吴孟源：概念化、软件、验证、可视化、调查、形式分析、写作 - 初稿、写作 - 评论和编辑。曾小平：监督、写作、审稿和编辑。彭子涵：写作——初稿、验证、调查。

Acknowledgements 致谢

We thank all members of the International Institute of Big Data in Finance, Business School, BNU for the sentiment data and fruitful discussions.
感谢北京师范大学商学院金融大数据国际研究院全体成员提供的情感数据和富有成效的讨论。

Appendix A. Nomenclature 附录 A 术语

Nomenclature
SENT_i,t_:Individual investor sentiment index of stock i on day t.
SENT_m,t_:Individual investor sentiment index of the overall market on day t.
R_i_:The daily return of stock i considering cash dividends reinvested.
Nnind: The industry code announced by China Securities Regulatory Commission.
PB: Price to book ratio. Calculated by daily stock market value and net asset published in the latest periodic report.
PE: Price earnings ratio. Calculated by daily stock market value and net income attributable to the parent company for the last four quarters.
CMV: Current market value. Calculated by the daily number of tradable shares time the daily stock price.
ISR: Institutional shareholding ratio. Quarterly data.
Year: The listed year by the event day.
NA: Net asset according to the latest annual report.
Regplc: Company registration place dummy variables. Wuhan =2, Hubei =1, other places =0.
EW: Event window dummy variables. Event window [0,10)=0, post-event window [10,46)=1.

Appendix B. Cumulative abnormal return in different industries during event windows
附录B 事件窗口期各行业累计超额收益

Table 9. 表 9.

Table 9. Cumulative abnormal return in different industries during event windows
表9 事件窗口期各行业累计超额收益

Empty Cell	Obs. 观察。	CAR	T-value T值	Weight 重量
Pharmaceutical Industry 医药行业	197	0.12	16.372***	23.12%
Manufacture of computers, communication and other electronic equipment 计算机、通讯及其他电子设备制造	293	0.033	4.879***	9.46%
Software and information technology services 软件和信息技术服务	177	0.044	5.159***	7.62%
Real estate 房地产	108	-0.052	-10.837***	5.49%
Manufacture of special purpose machinery 专用机械制造	177	0.025	2.849***	4.33%
Manufacture of chemical raw materials and chemical products 化学原料及化学制品制造业	203	0.015	2.098**	2.98%
Internet and related services 互联网及相关服务	49	0.051	2.747***	2.44%
Business Service Industry 商业服务业	43	-0.057	-6.695***	2.40%
Manufacture of alcohol, beverages, and refined tea 酒精、饮料和精制茶的制造	34	-0.064	-7.331***	2.13%
Production and distribution of electric power and heat power 电力、热力的生产和分配	66	-0.028	-5.059***	1.81%
Water transport 水运	26	-0.062	-5.282***	1.58%
Civil engineering 土木工程	57	-0.026	-3.791***	1.45%
Manufacture of non-metallic mineral products 非金属矿产品制造业	77	-0.019	-2.303**	1.43%
Capital markets services 资本市场服务	41	-0.035	-6.657***	1.40%
Manufacture of general-purpose machinery 通用机械制造	109	-0.013	-1.384	1.39%
Ecological protection and environmental management 生态保护与环境管理	35	0.04	2.601**	1.37%
Decoration 装饰	23	-0.058	-3.716***	1.30%
Road transport 公路运输	33	-0.04	-4.196***	1.29%
Manufacture of chemical fibers 化学纤维制造	20	0.06	2.026*	1.17%
Manufacture of textiles 纺织品制造	30	0.038	1.978*	1.12%
Retail trade 零售业	76	-0.015	-1.368	1.12%
Management of public facilities 公共设施管理	15	-0.075	-5.492***	1.10%
Mining and washing of coal 煤炭开采和洗选	24	-0.046	-8.944***	1.08%
Manufacture of furniture 家具制造	19	-0.054	-3.202***	1.00%
Wholesale trade 批发贸易	70	0.014	1.173	0.96%
Smelting and processing of non-ferrous metals 有色金属冶炼及加工	61	-0.016	-1.365	0.95%
Processing of food from agric. products 农产品食品加工。产品	44	0.022	2.063**	0.95%
Manufacture of railway, ships, aerospace and other transportation equipment 铁路、船舶、航空航天等交通运输设备制造	41	-0.022	-1.51	0.88%
Manufacture of metal products 金属制品制造	53	-0.015	-1.45	0.78%
Other financial activities 其他金融活动	15	-0.051	-3.361***	0.75%
Air transport 航空运输	11	-0.066	-4.659***	0.71%
Production and distribution of gas 天然气的生产和分配	18	-0.04	-3.428***	0.70%
Manufacture of articles for culture, education, art, sports, and entertainment 文化、教育、艺术、体育、娱乐用品制造业	12	0.058	1.562	0.68%
Manufacture of automobiles 汽车制造	114	-0.006	-0.575	0.67%
Postal services 邮政服务	4	0.169	3.856**	0.66%
Ancillary mining activities 辅助采矿活动	14	-0.044	-2.033*	0.60%
Manufacture of foods 食品制造	38	0.015	1.217	0.56%
Smelting and processing of ferrous metals 黑色金属冶炼及加工	28	-0.02	-1.54	0.55%
News and publishing 新闻出版	22	0.025	1.431	0.54%
Manufacture of leather, fur, feather and related products; footwear industry 皮革、毛皮、羽毛及相关产品的制造；鞋业	11	-0.048	-1.796	0.52%
Other manufacturing 其他制造业	13	-0.038	-2.009*	0.48%
Manufacture of textiles, clothing; apparel industry 纺织品、服装制造；服装行业	32	-0.015	-0.716	0.47%
Processing of timber, manufacture of wood, bamboo, rattan, palm, and straw products 木材加工、木、竹、藤、棕、草制品制造	7	-0.068	-3.292**	0.47%
Comprehensive use of waste resources 废弃物资源综合利用	3	0.157	5.038**	0.46%
Farming 农业	14	0.033	1.924*	0.45%
Monetary and financial services 货币和金融服务	25	-0.018	-4.92***	0.44%
Research and experimental development 研究和实验发展	4	0.112	4.076**	0.44%
Accommodation 住宿	6	-0.074	-5.503***	0.43%
Production and distribution of tap water 自来水的生产和分配	14	0.03	1.311	0.41%
Complex 复杂的	15	0.028	1.012	0.41%
Printing and recorded media 印刷和记录媒体	11	0.034	0.999	0.37%
Healthcare 卫生保健	12	0.029	1.112	0.34%
Manufacture of rubber and plastics 橡胶和塑料制造	65	-0.005	-0.507	0.32%
Extraction of petroleum and natural gas 石油和天然气的开采	6	-0.054	-3.542**	0.32%
Professional technical service industry 专业技术服务行业	42	-0.007	-0.591	0.29%
Education 教育	8	0.035	0.888	0.27%
Insurance 保险	6	-0.04	-3.017**	0.23%
Storage 贮存	7	-0.034	-1.042	0.23%
Railway transport 铁路运输	4	-0.057	-4.621**	0.22%
Telecommunications, radio, television, and satellite transmission services 电信、广播、电视和卫星传输服务	14	-0.016	-1.395	0.22%
Manufacture of measuring instruments 测量仪器制造	42	-0.005	-0.413	0.21%
Manufacture of electrical machinery and equipment 电气机械及设备制造	203	-0.001	-0.175	0.20%
Animal husbandry 畜牧业	12	0.015	0.717	0.18%
Catering 餐饮	2	-0.086	-7.094*	0.17%
Leasing 租赁	3	-0.057	-6.141**	0.17%
Mining and processing of non-ferrous metal ores 有色金属矿采选	21	-0.007	-0.389	0.14%
Construction of buildings 建筑物建造	2	-0.064	-9.806*	0.13%
Mining and processing of ferrous metal ores 黑色金属矿石的开采和加工	5	-0.022	-0.617	0.11%
Loading/unloading, removal, and other transport services 装卸、搬运等运输服务	2	-0.053	-2.574	0.10%
Radio, film, television, and (other) audio-visual media 广播、电影、电视和（其他）视听媒体	19	-0.005	-0.147	0.09%
Processing of petroleum, coking, processing of nuclear fuel 石油加工、焦化、核燃料加工	14	0.006	0.141	0.08%
Manufacture of paper and paper prod. 纸及纸制品制造	24	0.003	0.132	0.07%
Culture and arts 文化艺术	8	-0.003	-0.049	0.02%
Forestry 林业	2	-0.008	-0.544	0.02%
Fisheries 渔业	7	0.002	0.177	0.01%

Notes: This table provides the event-study result in different industries during event windows. The event dates and windows are defined as in Fig. 1. CAR denotes mean cumulative abnormal returns. Obs. denotes the number of stocks in the industry. Weight measures the extent of the impact on the average CAR of the overall market and is calculated as below.
注：本表提供了事件窗口期间不同行业的事件研究结果。事件日期和窗口的定义如图 1 所示。CAR 表示平均累积异常收益。观察。表示该行业的股票数量。权重衡量的是对整个市场平均CAR的影响程度，计算公式如下。

$W e i g h t = \frac{The number of stocks in industry i \times Average C A R of industry i}{\sum_{n = 75}^{i} The number of stocks in industry i \times Average C A R of industry i}$

***, **, * indicate significance at the 1%, 5%, and 10% levels, respectively.
***、**、* 分别表示在 1%、5% 和 10% 水平下的显着性。

Appendix C. Panel data regression results of stocks in different industries
附录C. 不同行业股票面板数据回归结果

Table 10 表 10

Table 10. Panel data regression results of stocks in different industries
表10 不同行业股票面板数据回归结果

Empty Cell	FE	FGLS
Empty Cell	R_i,t	R_i,t
SENT_i,t 已发送 _i,t	0.00483***	0.00419***
Empty Cell	(8.09)	(7.96)
SENT_i,t × Industry_i 已发送 _i,t × 行业
Farming 农业	0.002*	0.002***
Empty Cell	(1.82)	(2.80)
Forestry 林业	-0.001	-0.001
Empty Cell	(-0.66)	(-0.31)
Animal husbandry 畜牧业	0.001	0.001
Empty Cell	(1.47)	(1.47)
Fisheries 渔业	-0.003*	-0.002
Empty Cell	(-1.79)	(-1.27)
Mining and washing of coal 煤炭开采和洗选	-0.004***	-0.003***
Empty Cell	(-4.43)	(-4.04)
Extraction of petroleum and natural gas 石油和天然气的开采	-0.004***	-0.004***
Empty Cell	(-3.23)	(-3.25)
Mining and processing of ferrous metal ores 黑色金属矿石的开采和加工	-0.003	-0.003
Empty Cell	(-1.47)	(-1.37)
Mining and processing of non-ferrous metal ores 有色金属矿采选	-0.000	0.000
Empty Cell	(-0.56)	(0.17)
Ancillary mining activities 辅助采矿活动	-0.003***	-0.002***
Empty Cell	(-3.20)	(-2.83)
Processing of food from agric. products 农产品食品加工。产品	0.001**	0.002***
Empty Cell	(2.06)	(3.50)
Manufacture of foods 食品制造	0.000	0.000
Empty Cell	(0.50)	(0.45)
Manufacture of alcohol, beverages, and refined tea 酒精、饮料和精制茶的制造	-0.003***	-0.002***
Empty Cell	(-3.51)	(-3.18)
Manufacture of textiles 纺织品制造	-0.001*	-0.001
Empty Cell	(-1.93)	(-1.25)
Manufacture of textiles, clothing; apparel industry 纺织品、服装制造；服装行业	0.000	0.001
Empty Cell	(0.08)	(1.11)
Manufacture of leather, fur, feather and related products; footwear industry 皮革、毛皮、羽毛及相关产品的制造；鞋业	-0.002**	-0.002**
Empty Cell	(-2.46)	(-2.01)
Processing of timber, manufacture of wood, bamboo, rattan, palm, and straw products 木材加工、木、竹、藤、棕、草制品制造	-0.001	-0.001
Empty Cell	(-1.15)	(-0.56)
Manufacture of furniture 家具制造	-0.002***	-0.002**
Empty Cell	(-2.58)	(-2.25)
Manufacture of paper and paper prod. 纸及纸制品制造	-0.002***	-0.001**
Empty Cell	(-2.67)	(-2.03)
Printing and recorded media 印刷和记录媒体	-0.001	-0.001
Empty Cell	(-1.45)	(-1.12)
Manufacture of articles for culture, education, art, sports, and entertainment 文化、教育、艺术、体育、娱乐用品制造业	-0.001	0.000
Empty Cell	(-0.52)	(0.18)
Processing of petroleum, coking, processing of nuclear fuel 石油加工、焦化、核燃料加工	-0.004***	-0.003**
Empty Cell	(-2.76)	(-2.42)
Manufacture of chemical raw materials and chemical products 化学原料及化学制品制造业	-0.000	0.000
Empty Cell	(-0.22)	(0.48)
Pharmaceutical Industry 医药行业	0.001*	0.001***
Empty Cell	(1.73)	(2.71)
Manufacture of chemical fibers 化学纤维制造	0.001	0.001
Empty Cell	(0.69)	(1.19)
Manufacture of rubber and plastics 橡胶和塑料制造	-0.000	0.000
Empty Cell	(-0.64)	(0.17)
Manufacture of non-metallic mineral products 非金属矿产品制造业	-0.000	0.000
Empty Cell	(-0.00)	(0.87)
Smelting and processing of ferrous metals 黑色金属冶炼及加工	-0.003***	-0.002***
Empty Cell	(-3.38)	(-2.90)
Smelting and processing of non-ferrous metals 有色金属冶炼及加工	-0.000	-0.000
Empty Cell	(-0.66)	(-0.09)
Manufacture of metal products 金属制品制造	-0.002***	-0.001**
Empty Cell	(-2.81)	(-2.21)
Manufacture of general-purpose machinery 通用机械制造	-0.001**	-0.001
Empty Cell	(-2.09)	(-1.24)
Manufacture of special purpose machinery 专用机械制造	-0.000	0.001
Empty Cell	(-0.15)	(0.97)
Manufacture of automobiles 汽车制造	-0.001	-0.001
Empty Cell	(-1.07)	(-1.12)
Manufacture of railway, ships, aerospace and other transportation equipment 铁路、船舶、航空航天等交通运输设备制造	-0.001	-0.000
Empty Cell	(-1.00)	(-0.45)
Manufacture of electrical machinery and equipment 电气机械及设备制造	0.000	0.001
Empty Cell	(0.49)	(1.01)
Manufacture of computers, communication and other electronic equipment 计算机、通讯及其他电子设备制造	0.002***	0.002***
Empty Cell	(2.68)	(3.20)
Manufacture of measuring instruments 测量仪器制造	-0.001**	-0.001
Empty Cell	(-1.98)	(-1.29)
Other manufacturing 其他制造业	0.001	0.001
Empty Cell	(0.63)	(1.47)
Comprehensive use of waste resources 废弃物资源综合利用	-0.001	-0.001
Empty Cell	(-0.81)	(-0.88)
Production and distribution of electric power and heat power 电力、热力的生产和分配	-0.003***	-0.003***
Empty Cell	(-4.93)	(-4.63)
Production and distribution of gas 天然气的生产和分配	-0.002*	-0.001
Empty Cell	(-1.71)	(-0.82)
Production and distribution of tap water 自来水的生产和分配	-0.003***	-0.003***
Empty Cell	(-3.72)	(-3.36)
Civil engineering 土木工程	-0.001	-0.000
Empty Cell	(-1.27)	(-0.37)
Renovation 装修	0.002	0.003*
Empty Cell	(1.24)	(1.65)
Decoration 装饰	-0.002**	-0.001*
Empty Cell	(-2.29)	(-1.86)
Wholesale trade 批发贸易	-0.000	0.000
Empty Cell	(-0.35)	(0.24)
Retail trade 零售业	-0.001	-0.000
Empty Cell	(-1.07)	(-0.73)
Railway transport 铁路运输	-0.004**	-0.003*
Empty Cell	(-2.00)	(-1.84)
Road transport 公路运输	-0.004***	-0.003***
Empty Cell	(-4.79)	(-4.42)
Water transport 水运	-0.002***	-0.002***
Empty Cell	(-3.29)	(-2.92)
Air transport 航空运输	-0.002*	-0.002**
Empty Cell	(-1.76)	(-2.06)
Loading/unloading, removal, and other transport services 装卸、搬运等运输服务	-0.001	-0.001
Empty Cell	(-0.96)	(-0.48)
Storage 贮存	-0.003**	-0.002*
Empty Cell	(-2.55)	(-1.93)
Postal services 邮政服务	0.001	0.001
Empty Cell	(0.83)	(1.15)
Accommodation 住宿	-0.000	-0.000
Empty Cell	(-0.18)	(-0.38)
Catering 餐饮	-0.001	-0.001
Empty Cell	(-0.41)	(-0.67)
Telecommunications, radio, television, and satellite transmission services 电信、广播、电视和卫星传输服务	-0.000	-0.001
Empty Cell	(-0.47)	(-0.58)
Internet and related services 互联网及相关服务	0.001**	0.001**
Empty Cell	(1.98)	(2.30)
Software and information technology services 软件和信息技术服务	0.001**	0.001***
Empty Cell	(2.33)	(2.70)
Real estate 房地产	-0.002**	-0.001**
Empty Cell	(-2.47)	(-2.38)
Leasing 租赁	-0.003*	-0.002
Empty Cell	(-1.65)	(-1.34)
Business Service Industry 商业服务业	-0.001*	-0.001
Empty Cell	(-1.81)	(-1.55)
Research and experimental development 研究和实验发展	-0.001	-0.001
Empty Cell	(-0.94)	(-0.58)
Professional technical service industry 专业技术服务行业	-0.000	0.000
Empty Cell	(-0.69)	(0.21)
Ecological protection and environmental management 生态保护与环境管理	-0.002**	-0.001
Empty Cell	(-2.20)	(-1.62)
Management of public facilities 公共设施管理	-0.002**	-0.001*
Empty Cell	(-1.97)	(-1.74)
Education 教育	0.002**	0.003***
Empty Cell	(2.15)	(2.69)
Healthcare 卫生保健	-0.001	-0.001
Empty Cell	(-1.37)	(-0.81)
News and publishing 新闻出版	-0.002**	-0.001*
Empty Cell	(-2.23)	(-1.74)
Radio, film, television, and other audio-visual media 广播、电影、电视及其他视听媒体	-0.001	-0.001
Empty Cell	(-0.99)	(-1.43)
Culture and arts 文化艺术	-0.000	0.000
Empty Cell	(-0.41)	(0.14)
Sports 运动的	-0.003**	-0.003*
Empty Cell	(-2.11)	(-1.72)
MKT_t	1.014***	1.015***
Empty Cell	(184.07)	(184.16)
SMB_t 中小型企业 _t	0.566***	0.571***
Empty Cell	(49.99)	(50.35)
HML_t	-0.0678***	-0.0803***
Empty Cell	(-3.11)	(-3.69)
ϕ₀ φ ₀	-0.00270***	-0.00250***
Empty Cell	(-25.22)	(-23.46)
adj. R-sq 形容词R平方	0.488
F	888.0
N	68083	68083

Notes: This table provides the results from the estimation of the following regression specification.
注：此表提供了以下回归规范的估计结果。

$R_{i, t} = φ_{0} + φ_{1} S E N T_{i, t} + φ_{2} S E N T_{i, t} \times I n d u s t r y_{i} + γ M K T_{t} + δ S M B_{t} + η H M L_{t} + ε_{t}$

Dependent variable R_i,t represents the return of individual stock i on day t and SENT_i,t represents the sentiment index of individual stock i on day t. Industry_i is the dummy variable based on the Industry Classification Scheme released by China Securities Regulatory Commission in 2020 Q1. Integrated industry is used as the reference group.
因变量R _i,t 代表个股i在t日的收益，SENT _i,t 代表个股i在t日的情绪指数。行业为虚拟变量，依据证监会2020年一季度发布的行业分类方案。综合工业作为参照组。
Companies of the integrated industry have similar revenue and profit in many industries, and no obvious development planning and share holding background. T-stats are in parentheses below the coefficient estimates.
综合行业公司在多个行业的收入和利润相似，没有明显的发展规划和控股背景。 T 统计量位于系数估计值下方的括号中。
***, **, * indicate significance at the 1%, 5%, and 10% levels, respectively.
***、**、* 分别表示在 1%、5% 和 10% 水平下的显着性。

References

Al-Awadhi et al., 2020
A.M. Al-Awadhi, K. Al-Saifi, A. Al-Awadhi, S. Alhamadi
Death and contagious infectious diseases: impact of the COVID-19 virus on stock market returns
J. Behav. Exp. Financ. (2020), Article 100326
IF 4.3SSCIJCR Q1经济学2区JCI 1.74
View PDF View articleView in Scopus Google Scholar
Baek et al., 2020
S. Baek, S.K. Mohanty, G. Mina
COVID-19 and stock market volatility: an industry level analysis
Financ. Res. Lett. (2020), Article 101748, 10.1016/j.frl.2020.101748
IF 7.4SSCIJCR Q1经济学2区TopJCI 2.73
View PDF View articleView in Scopus Google Scholar
Baig et al., 2020
A.S. Baig, H.A. Butt, O. Haroon, S.A.R. Rizvi
Deaths, panic, lockdowns and US equity markets: the case of COVID-19 pandemic
Financ. Res. Lett. (2020), Article 101701, 10.1016/j.frl.2020.101701
IF 7.4SSCIJCR Q1经济学2区TopJCI 2.73
Google Scholar
Baker et al., 2020
S.R. Baker, N. Bloom, S.J. Davis, K.J. Kost, M.C. Sammon, T. Viratyosin
The Unprecedented Stock Market Impact of COVID-19
National Bureau of Economic Research (2020)
Google Scholar
Donadelli et al., 2017
M. Donadelli, R. Kizys, M. Riedel
Dangerous infectious diseases: bad news for Main Street, good news for Wall Street?
J. Financ. Mark., 35 (2017), pp. 84-103
IF 2.1SSCIJCR Q2经济学2区JCI 0.68
View PDF View articleView in Scopus Google Scholar
Fallahgoul, 2020
H. Fallahgoul
Inside the mind of investors during the COVID-19 Pandemic: evidence from the stocktwits data
arXiv Prepr. arXiv, 2004 (2020), p. 11686
Google Scholar
Fama, 1965
E.F. Fama
Portfolio analysis in a stable Paretian market
Manage. Sci., 11 (1965), pp. 404-419
IF 4.6SCIE/SSCIJCR Q1管理学2区TopJCI 1.11EI
CrossrefGoogle Scholar
Ichev and Marinč, 2018
R. Ichev, M. Marinč
Stock prices and geographic proximity of information: evidence from the Ebola outbreak
Int. Rev. Financ. Anal., 56 (2018), pp. 153-166
IF 7.5SSCIJCR Q1经济学1区TopJCI 2.32
View PDF View articleView in Scopus Google Scholar
Liu et al., 2020
H. Liu, A. Manzoor, C. Wang, L. Zhang, Z. Manzoor
The COVID-19 outbreak and affected countries stock markets response
Int. J. Environ. Res. Public Health, 17 (2020), p. 2800
CrossrefView in Scopus Google Scholar
Mazur et al., 2020
M. Mazur, M. Dang, M. Vega
COVID-19 and the march 2020 stock market crash. Evidence from S&P1500
Financ. Res. Lett. (2020), Article 101690, 10.1016/j.frl.2020.101690
IF 7.4SSCIJCR Q1经济学2区TopJCI 2.73
Google Scholar
Nadeem Ashraf, 2020
B. Nadeem Ashraf
Stock markets’ reaction to COVID-19: cases or fatalities?
Res. Int. Bus. Financ (2020), Article 101249
IF 6.3SSCIJCR Q1经济学2区TopJCI 1.92
Google Scholar
Shan and Gong, 2012
L. Shan, S.X. Gong
Investor sentiment and stock returns: wenchuan earthquake
Financ. Res. Lett., 9 (2012), pp. 36-47
IF 7.4SSCIJCR Q1经济学2区TopJCI 2.73
View PDF View articleView in Scopus Google Scholar
Shehzad et al., 2020
K. Shehzad, X. Liu, H. Kazouz
COVID-19’s disasters are perilous than global financial crisis: a rumor or fact?
Financ. Res. Lett., 36 (2020), Article 101669, 10.1016/j.frl.2020.101669
IF 7.4SSCIJCR Q1经济学2区TopJCI 2.73
View PDF View articleView in Scopus Google Scholar
Sun et al., 2018
Yunchuan Sun, Mengting Fang, Xinyu Wang
A novel stock recommendation system using Guba sentiment analysis, (2018), Volume 22 Issue 3, June 2018, Pages 575-587.
Personal and Ubiquitous Computing, 22 (3) (2018), pp. 575-587, 10.1007/s00779-018-1121-x
EI
View in Scopus Google Scholar
Tao, 2010
B. Tao
Analysis of the impact of unconventional and unexpected disasters on Chinese stock market volatility – an empirical study based on unit root test of exogenous structural abrupt changes
Knowl. Econ. (2010), pp. 42-43
CrossrefGoogle Scholar
Sun et al., 2017
Y. Sun, M. Fang, X. Wang and S. Diao, GubaLex: Guba-Oriented Sentiment Lexicon for Big Texts in Finance, 2017 13th International Conference on Semantics, Knowledge and Grids (SKG), Beijing, 2017, pp. 25-32.
Google Scholar

Cited by (93)

How does Chinese stock market react to breaking news about COVID-19? Evidence from event study
2024, Heliyon
Although the COVID-19 pandemic is no longer considered a public health emergency of international concern, research is ongoing to determine whether and how financial markets have been affected. Using an event study methodology, we examine Chinese stock reactions to breaking news about COVID-19 during 2020–2023, covering nine important events from the first declaration of the novel coronavirus to classifying COVID-19 from top category A to B in China. The empirical results show that the Chinese stock market reacts negatively to bad news, such as the breakout of COVID-19 and the dynamic zero-COVID strategy, and positively to good news, such as COVID-19 vaccine approvals, ending the zero-COVID strategy, and reopening boards. The overall intensity of reactions to bad news is stronger than that to good news. Small-sized stocks react more positively to good news and more negatively to bad news. Different industries have different reactions to different events; however, the sector that responds strongly and negatively to bad news does not have to respond strongly and positively to good news, which, to some extent, reflects their profitability performance during the pandemic. In China, consumer discretionary, utilities and real estate are the most negatively affected by the pandemic, while materials, health care, and energy are the most positively affected. This study has important implications for reminding market participants to pay attention to breaking news announcements about COVID-19.
Impact of coronavirus pandemic on stock index: A polynomial regression with time delay
2024, Heliyon
Under contemporary market conditions in China, the stock index has been volatile and highly reflect trends in the coronavirus pandemic, but rare scientific research has been conducted to model the possible nonlinear relations between the two indicators. Added, on the advent that covid-related news in one time period impacts the stock market in another period, time delay can be an equally good predictor of the stock index but rarely investigated.
To contribute to filling the gaps identified in existing research, this study models relationship between the stock market index and coronavirus pandemic by leveraging volatility in the stock market and covid data through time delay and best degree in a polynomial environment. The resultant optimal time delay and best degree model is used to derive a high-accuracy prediction of stock market index.
In line with the possible relations, the novelty of this study is that it proposes, validates and implements polynomial regression with time delay to model nonlinear relationship between the stock index and covid.
This study utilizes high-frequency data from January 2020 to the first week of July 2022 to model the nonlinear relationship between the stock index, new covid cases and time delay under polynomial regression environment.
The empirical results show that time delay and new covid cases, when modelled in a polynomial environment with optimal degree and delay, do present better representation of the nonlinear relationship such predictors have with stock index for China. Relative to results from the polynomial regression without delay, the empirical evidence from the model with delay show that an optimal time delay of 17 weeks makes it possible to predict the stock index at high accuracy and record improvements of 16-fold or higher. The representative delay model is used to project for up to 17 weeks for future trends in the stock index.
The implication of the findings herein is that the prowess of the time delay polynomial regression is heavily dependent on instability in covid-related time trends and that researchers and decision-makers should consider modeling to cover for the unsteadiness in coronavirus cases to achieve better results.
Investor sentiment response to COVID-19 outbreak-related news: A sectoral analysis of US firms
2024, North American Journal of Economics and Finance
Capital markets literature has challenged the efficient market hypothesis, and one of the factors that explain unpredictable changes in stock prices is investor sentiment. In this study, we examine the investor sentiment index’s response to COVID-19 outbreak-related events in 2020, based on a sample of the S&P 500 companies from the sectors most severely affected by the pandemic: healthcare, industrials, consumer goods and consumer services. Using principal component analysis, we determine objective market-based measures of the investor sentiment, both for sectors, and individual companies. Taking advantage of the event study methodology and using the Daily News Sentiment Index as a benchmark, we found that the abnormal investor sentiment index’s response to COVID-19 outbreak-related news was highly negative, with a few exceptions of positive reaction on selected announcements in the healthcare and industrials sectors.
How the pandemic-led volatility in the natural resource commodity indices affect U.S and China markets
2024, Resources Policy
Using asymmetric and Markov switching GARCH model on daily time series data from January 22, 2020 to March 22, 2022, the study analyzes the impact of COVID-19 on natural resource commodity index returns in the U.S and China. The results find strong evidence of COVID-19 on the volatility of natural resource commodity index return for both China and the U.S. However, the intensity of this effect varies based on volatility regimes, with the Chinese market experiencing stronger effects during calmer regimes (periods with low intensity in COVID-19 cases) and weaker effects in turbulent regimes (periods with high intensity in COVID-19 cases). This could be due to market control measures in China. In contrast, the U.S market shows a more pronounced COVID-19 effect in turbulent regimes, attributed to the openness of the market and the prevalence of speculative participants. The study suggests that the U.S market participants, driven by a goal to profit from price differences, amplify their activity during turbulent periods, leading to increased volatility transfers during the consecutive spells of high COVID-19 cases. The findings highlight the difference in the structures of financial markets in China and the U.S., thus possessing important policy implications for investors in the sector.
Sensitivity of Chinese stock markets to individual investor sentiment: An analysis of Sina Weibo mood related to COVID-19
2024, Journal of Behavioral and Experimental Finance
This research explores the impact of individual investor sentiment derived from social networks on stock market returns. Using keyword-based techniques, we collect and analyze Sina Weibo posts related to COVID-19, extracting daily influential weighted sentiment indexes from a dataset of over 2.4 million posts in 2020. Empirical tests utilizing a sentiment-augmented three-factor model reveal that individual investor sentiment exerts an independent influence on Chinese financial markets, after controlling for market risk, size, and value effects. We further find that negative sentiment carries a stronger impact on stock returns, which is in line with the loss-averse behavior commonly observed among individual investors. We also find an asymmetric pattern in the sentiment-return relation across different industry types. While positive sentiment affects both types of industries that suffer or benefit from COVID-19, negative sentiment affects only the industries that suffer from the pandemic. Overall, our empirical results provide robust support for the significance of individual investor sentiment in explaining the behavior of the Chinese financial markets.
Systematic COVID risk, idiosyncratic COVID risk and stock returns
2024, North American Journal of Economics and Finance
This paper proposes an event-based approach to estimate the systematic and idiosyncratic COVID risk for individual stocks. The systematic COVID risk is based on the excess co-variance between stock returns and market returns during the event window when the COVID shock occurred. The idiosyncratic COVID risk is based on the excess idiosyncratic volatility during the event window. The approach also allows us to estimate abnormal returns associated with COVID shock for individual stocks. Moreover, we apply the event-based approach to separately estimate the risks associated with domestic COVID shock and foreign COVID shock. Based on the estimates of COVID risk and abnormal COVID returns, we examine their relations with future stock returns. Our results show that both systematic domestic and foreign COVID risks are positively related to future stock returns, suggesting that investors demand higher expected returns for stocks with high exposure to the COVID shock. We also find a negative relation between abnormal returns associated with domestic COVID and future stock returns and the relation becomes stronger over longer horizon, indicating initial overreaction to COVID shock. We perform various robustness checks to confirm our main findings. Our findings have important implications on risk management for investors, firms, and regulators.

View all citing articles on Scopus

¹: The S&P 500 hit the 7% threshold decline on March 9 and March 12, halting trade during regular market hours for 15 minutes to ensure trading order. The last and only previous time of the "circuit breaker" was back in 1997.

²: Shanghai Stock Exchange Statistical Yearbook.

³: https://www.gtarsc.com

⁴: A stock receiving special treatment or delisting risk warning will be prefixed with “ST” or “PT”. This prefix is used to indicate the risk of abnormal financial conditions or other abnormal conditions.

⁵: http://www.csrc.gov.cn/pub/newsite/scb/ssgshyfljg/202004/t20200414_373793.html

View Abstract

[1] Al-Awadhi et al., 2020
A.M. Al-Awadhi, K. Al-Saifi, A. Al-Awadhi, S. Alhamadi
Death and contagious infectious diseases: impact of the COVID-19 virus on stock market returns
J. Behav. Exp. Financ. (2020), Article 100326
IF 4.3SSCIJCR Q1经济学2区JCI 1.74
View PDF View articleView in Scopus Google Scholar

[2] Baek et al., 2020
S. Baek, S.K. Mohanty, G. Mina
COVID-19 and stock market volatility: an industry level analysis
Financ. Res. Lett. (2020), Article 101748, 10.1016/j.frl.2020.101748
IF 7.4SSCIJCR Q1经济学2区TopJCI 2.73
View PDF View articleView in Scopus Google Scholar

[3] Baig et al., 2020
A.S. Baig, H.A. Butt, O. Haroon, S.A.R. Rizvi
Deaths, panic, lockdowns and US equity markets: the case of COVID-19 pandemic
Financ. Res. Lett. (2020), Article 101701, 10.1016/j.frl.2020.101701
IF 7.4SSCIJCR Q1经济学2区TopJCI 2.73
Google Scholar

[4] Baker et al., 2020
S.R. Baker, N. Bloom, S.J. Davis, K.J. Kost, M.C. Sammon, T. Viratyosin
The Unprecedented Stock Market Impact of COVID-19
National Bureau of Economic Research (2020)
Google Scholar

[5] Donadelli et al., 2017
M. Donadelli, R. Kizys, M. Riedel
Dangerous infectious diseases: bad news for Main Street, good news for Wall Street?
J. Financ. Mark., 35 (2017), pp. 84-103
IF 2.1SSCIJCR Q2经济学2区JCI 0.68
View PDF View articleView in Scopus Google Scholar

[6] Fallahgoul, 2020
H. Fallahgoul
Inside the mind of investors during the COVID-19 Pandemic: evidence from the stocktwits data
arXiv Prepr. arXiv, 2004 (2020), p. 11686
Google Scholar

[7] Fama, 1965
E.F. Fama
Portfolio analysis in a stable Paretian market
Manage. Sci., 11 (1965), pp. 404-419
IF 4.6SCIE/SSCIJCR Q1管理学2区TopJCI 1.11EI
CrossrefGoogle Scholar

[8] Ichev and Marinč, 2018
R. Ichev, M. Marinč
Stock prices and geographic proximity of information: evidence from the Ebola outbreak
Int. Rev. Financ. Anal., 56 (2018), pp. 153-166
IF 7.5SSCIJCR Q1经济学1区TopJCI 2.32
View PDF View articleView in Scopus Google Scholar

[9] Liu et al., 2020
H. Liu, A. Manzoor, C. Wang, L. Zhang, Z. Manzoor
The COVID-19 outbreak and affected countries stock markets response
Int. J. Environ. Res. Public Health, 17 (2020), p. 2800
CrossrefView in Scopus Google Scholar

[10] Mazur et al., 2020
M. Mazur, M. Dang, M. Vega
COVID-19 and the march 2020 stock market crash. Evidence from S&P1500
Financ. Res. Lett. (2020), Article 101690, 10.1016/j.frl.2020.101690
IF 7.4SSCIJCR Q1经济学2区TopJCI 2.73
Google Scholar

[11] Nadeem Ashraf, 2020
B. Nadeem Ashraf
Stock markets’ reaction to COVID-19: cases or fatalities?
Res. Int. Bus. Financ (2020), Article 101249
IF 6.3SSCIJCR Q1经济学2区TopJCI 1.92
Google Scholar

[12] Shan and Gong, 2012
L. Shan, S.X. Gong
Investor sentiment and stock returns: wenchuan earthquake
Financ. Res. Lett., 9 (2012), pp. 36-47
IF 7.4SSCIJCR Q1经济学2区TopJCI 2.73
View PDF View articleView in Scopus Google Scholar

[13] Shehzad et al., 2020
K. Shehzad, X. Liu, H. Kazouz
COVID-19’s disasters are perilous than global financial crisis: a rumor or fact?
Financ. Res. Lett., 36 (2020), Article 101669, 10.1016/j.frl.2020.101669
IF 7.4SSCIJCR Q1经济学2区TopJCI 2.73
View PDF View articleView in Scopus Google Scholar

[14] Sun et al., 2018
Yunchuan Sun, Mengting Fang, Xinyu Wang
A novel stock recommendation system using Guba sentiment analysis, (2018), Volume 22 Issue 3, June 2018, Pages 575-587.
Personal and Ubiquitous Computing, 22 (3) (2018), pp. 575-587, 10.1007/s00779-018-1121-x
EI
View in Scopus Google Scholar

[15] Tao, 2010
B. Tao
Analysis of the impact of unconventional and unexpected disasters on Chinese stock market volatility – an empirical study based on unit root test of exogenous structural abrupt changes
Knowl. Econ. (2010), pp. 42-43
CrossrefGoogle Scholar

[16] Sun et al., 2017
Y. Sun, M. Fang, X. Wang and S. Diao, GubaLex: Guba-Oriented Sentiment Lexicon for Big Texts in Finance, 2017 13th International Conference on Semantics, Knowledge and Grids (SKG), Beijing, 2017, pp. 25-32.
Google Scholar

Outline 大纲

Cited by (93) 被引用 (93)

Figures (1) 人物 (1)

Tables (11)

Finance Research Letters 金融研究快报

Highlights 强调

Abstract 抽象的

Keywords 关键词

1. Introduction 一、简介

2. Data and methodology 2. 数据和方法

2.1. Data 2.1.数据

2.2. Event study 2.2.事件研究

2.3. Panel regression model
2.3.面板回归模型

3. Empirical findings 3. 实证结果

3.1. Event-study reports 3.1.事件研究报告

3.2. Panel regression reports
3.2.面板回归报告

3.3. Characteristic effect
3.3.特色效应

3.4. Industry effect 3.4.产业效应

4. Conclusion 4。结论

CRediT authorship contribution statement
CRediT 作者贡献声明

Acknowledgements 致谢

Appendix A. Nomenclature 附录 A 术语

Appendix B. Cumulative abnormal return in different industries during event windows
附录B 事件窗口期各行业累计超额收益

Appendix C. Panel data regression results of stocks in different industries
附录C. 不同行业股票面板数据回归结果

References

Cited by (93)

How does Chinese stock market react to breaking news about COVID-19? Evidence from event study

Impact of coronavirus pandemic on stock index: A polynomial regression with time delay

Investor sentiment response to COVID-19 outbreak-related news: A sectoral analysis of US firms

How the pandemic-led volatility in the natural resource commodity indices affect U.S and China markets

Sensitivity of Chinese stock markets to individual investor sentiment: An analysis of Sina Weibo mood related to COVID-19

Systematic COVID risk, idiosyncratic COVID risk and stock returns

The impact of COVID-19 on the Chinese stock market: Sentimental or substantial?COVID-19对中国股市的影响：感性还是实质性？

Highlights 强调

Abstract 抽象的

Keywords 关键词

1. Introduction 一、简介

2. Data and methodology 2. 数据和方法

2.1. Data 2.1.数据

2.2. Event study 2.2.事件研究

2.3. Panel regression model2.3.面板回归模型

3. Empirical findings 3. 实证结果

3.1. Event-study reports 3.1.事件研究报告

3.2. Panel regression reports3.2.面板回归报告

3.3. Characteristic effect3.3.特色效应

3.4. Industry effect 3.4.产业效应

4. Conclusion 4。结论

CRediT authorship contribution statementCRediT 作者贡献声明

Acknowledgements 致谢

Appendix A. Nomenclature 附录 A 术语

Appendix B. Cumulative abnormal return in different industries during event windows附录B 事件窗口期各行业累计超额收益

Appendix C. Panel data regression results of stocks in different industries附录C. 不同行业股票面板数据回归结果

References

The impact of COVID-19 on the Chinese stock market: Sentimental or substantial?
COVID-19对中国股市的影响：感性还是实质性？

2.3. Panel regression model
2.3.面板回归模型

3.2. Panel regression reports
3.2.面板回归报告

3.3. Characteristic effect
3.3.特色效应

CRediT authorship contribution statement
CRediT 作者贡献声明

Appendix B. Cumulative abnormal return in different industries during event windows
附录B 事件窗口期各行业累计超额收益

Appendix C. Panel data regression results of stocks in different industries
附录C. 不同行业股票面板数据回归结果