COVID–19’s Impact on Stock Prices Across Different Sectors—An Event Study Based on the Chinese Stock Market

In this article, we use an event study approach to empirically study the market performance and response trends of Chinese industries to the COVID-19 pandemic. The study found that transportation, mining, electricity & heating, and environment industries have been adversely impacted by the pandemic. However, manufacturing, information technology, education and health-care industries have been resilient to the pandemic.

Whether the long-memory property is inherent in the movement of the stock market returns and volatility (risk) time series is known as a very important issue practically or theoretically regard to the efficiency of the stock market. The efficient market hypothesis describes that the information obtained from past statistics can not be used to predict the future stock price. This is because when generating the information that may affect the price of the stock reflects this value of the information on the price quickly and enough. Since the future information is unknown, the future stock price will not be predictable if the stock market is efficient. However, if the long-memory property exists in the stock market returns and volatility time series, it could predict a certain portion of the future returns and risks by using past data. This predictability means the assumption of classical investment theory, that the stock market is efficient, may not be proper. Thus, the existence of long-memory property has been addressed as an important research topic by the financial investment researchers and stock market investors. By using the stock prices of 50 stocks representing the Chinese stock market and their weighted average statistical index - SSE 50 Index, this study analyzes whether the long-memory property is inherent in Chinese stock market price movement as well as explains whether the existence of long-memory property is spurious result of the contemporaneous aggregation. The Chinese stock market is extremely proper market to perform the research related to the long-memory property because it is large and highly dynamic market. Using the returns and volatility of daily closing price (i.e. the absolute value of returns and its squared value) from January 2, 2004 to December 10, 2014 to conduct the Lo’s modified (R/S) analysis and the Geweke-Porter-Hudak (GPH) test. The main results of the empirical analysis from this study are as follows. First, although SSE 50 Index return series has long-memory property, there are not many evidences for its 50 constituent company stock prices. This means that predicting the return series for SSE 50 Index is relatively easier than individual stock prices. Second, in the case of volatility, both of the SSE 50 Index and its 50 individual stock prices have the presence of a long-memory property. Third, most of the 50 individual stock prices in Chinese market have the long-memory property. These are the unique properties inherent in the stock market time series instead of causing by the spurious consequence of a contemporaneous aggregation bias. Fourth, volatility has the stronger presence of a long-memory property than returns. This means that predicting the risk is relatively easier than returns due to volatility clustering. Based on the overall statistical test results, volatility has the stronger presence of a long-memory property than returns. The long-memory property exists in the Chinese stock market and this is the unique property inherent in the stock market time series instead of causing by the spurious consequence of a contemporaneous aggregation bias. These analytical findings indicate that the Chinese stock market is not fully efficient due to the existence of the long-memory property. The reasons that Chinese stock market is not efficient enough are that many Chinese investors have the speculative purposes and market information is not delivered transparently and quickly. Because of these characteristics, global investors will have room to reduce the risk and increase profits by leveraging long-memory properties in the Chinese stock market.

In this article, we analyze the characteristics of the implicit cycle of volatility in Chinese stock market by the theory of frequency spectrum. Through searching literature, we know the fact that the study of volatility in Chinese stock market always concentrate their attention on existence of volatility and there is lack of research on the implicit cycle characteristic in the market volatility. In recent years, some scholars also study the volatility in Chinese stock market and hold that there is implicit cycle of volatility in Chinese stock market, but don't provide the statistical test about peak value. The essence of implicit cycle in volatility is the performance of the low efficient market. Therefore, in this article, we establish the periodgram analysis model, and apply the window spectrum estimate of the power spectrum to analyze the volatility of Shanghai's stock price index and Shenzhen's. We also study the existence on implicit cycle of volatility in Chinese stock market in order to determine the improvement degree about Chinese stock market's efficiency. In this article, we study the implicit cycle of volatility in Chinese stock market by the stock index. The volatility of stock market is referring to the volatility that corresponded to the stock index. The author selects the day closing quotation index of Shanghai stock exchange composite index and of Shenzhen stock exchange component index as data sample and the data sector is from January 4, 1999 to December 13, 2005, amount to 1668 trading day. We each establish the two index's day return rate's percentage sequence. The data sequence doesn't have the tendency and seasonal characteristic. We apply the SPECTRA process of the SAS software (spectral analysis process) to determine the sequence's implicit cycle and provide the statistical test about peak value. So we obtain some researches output. We hold that there does not exist the implicit cycle of volatility in Chinese stock market. From this research we know that the Chinese stock market efficiency obtains the enhancement and the volatility structure have a greater change than several year ago. We also believe that the higher volatility in Chinese stock market is may caused by the centralized and fierce new message and by the worse market absorbency in the shock of message. Both lead to the stock price's volatility

This study empirically analyzes the influence of the trade disputes between China and the United States on the stock markets of the host countries. From 2000 to 2019, using 20 years of daily return series of S&P 500 and SSE composite stock price index data, after separating in the pre- and the post-trade dispute, we dynamically analyze the volatility clustering, asymmetric volatility, etc. by using time-variable volatility models such as EGARCH and TGARCH models. The main results are as follows. Firstly, in each of the U.S. and Chinese stock markets, volatility clustering was observed during the pre and the post-trade dispute, but its intensity increased in the U.S. market and conversely decreased in the Chinese market in the post-trade dispute, suggesting that the impact of the U.S. market to Chinese market is more sensitive. Secondly, after the trade dispute, the variability and persistence of the U.S market has decreased, while it has increased in the Chinese market. Thirdly, in the post-trade dispute, asymmetric volatility in the Chinese stock market is not noted, and the degree of asymmetric volatility that existed in the pre-trade dispute is weaker than the U.S. market. It confirms that in the Chinese market, investors are not sensitive to the increase in debt ratios and the increase in risk premium caused by the fall in stock prices during the crisis. Fourthly, during the trade dispute period, the possibility of adverse asymmetric volatility (ADV) in the Chinese stock market is still present, suggesting that Chinese investors respond more sensitively to good news than bad news. Thus, the speculative tendency, which is more sensitive to positive factors than negative ones, is found to likely increase in the Chinese market in the trade dispute period. To summarize the above results, in the case of market turbulence such as trade disputes, active intervention from government is able to effectively stabilize the market. Unlike developed stock markets, Chinese investors find the possibility that the contrarian strategies are more effective than “momentum” strategies.

By considering two time windows of crises, first one is the time period of Asian financial crisis (1997-1999) and the other one is prevailing global economic crisis (2007-2009), the pattern of underpricing and aftermarket performance are studied. A sample of 626 companies and Market adjusted return model are used. Result indicates that in the recent global economic crisis IPO activity is on shrinking trend and there is 10% increase in average underpricing as compared to last Asian financial crisis. There is a fluctuating trend in aftermarket performance of IPO returns. A minimum return of 62% in 2009 is observed. This study also endeavors to examine the efficiency of Chinese stock market and how the Asian and global financial crisis influences the efficiency of Chinese stock market. In order to determine the efficiency of Chinese stock market we apply efficient market hypothesis of random walk. Here we apply ADF, DF-GLS, PP and KPSS tests on stock market returns in order to check the unit root in data series for both Shenzhen and Shanghai stock exchanges separately. The results of the study shows that Chinese stock market is weak form efficient and past data of stock market movements may not be very useable in order to make excess returns. In both periods of crises Chinese stock market is observed weak form efficient.

This study aims to investigate bidirectional risk spillovers between the Chinese and other Asian stock markets. To achieve this, we construct a dynamic Copula-EVT-CoVaR model based on 11 Asian stock indexes from 1 January 2007 to 31 December 2021. The findings show that, firstly, synchronicity exists between the Chinese stock market and other Asian stock markets, creating conditions for risk contagion. Secondly, the Chinese stock market exhibits a strong risk spillover to other Asian stock markets with time-varying and heterogeneous characteristics. Additionally, the risk spillover displays an asymmetry, indicating that the intensity of risk spillover from other Asian stock markets to the Chinese is weaker than that from the Chinese to other Asian stock markets. Finally, the Chinese stock market generated significant extreme risk spillovers to other Asian stock markets during the 2007–2009 global financial crisis, the European debt crisis, the 2015–2016 Chinese stock market crash, and the China–US trade war. However, during the COVID-19 pandemic, the risk spillover intensity of the Chinese stock market was weaker, and it acted as the recipient of risk from other Asian stock markets. The originality of this study is reflected in proposing a novel dynamic copula-EVT-CoVaR model and incorporating multiple crises into an analytical framework to examine bidirectional risk spillover effects. These findings can help Asian countries (regions) adopt effective supervision to deal with cross-border risk spillovers and assist Asian stock market investors in optimizing portfolio strategies.

In the context of COVID-19, this paper compares the stock market performance and financial policies between China and the United States, as well as the similarities and differences of SARS in 2002, the U.S. subprime mortgage crisis in 2008, the U.S.-China trade frictions in 2018 and the COVID-19 in terms of these events impact on stock markets. By reviewing literature and event studies, this paper conducts a cross-sectional comparison and a longitudinal comparison of the Chinese and U.S. stock markets respectively. The study shows that the impact of COVID-19 on China is far less than that of the U.S. China adopted a more accommodative and longer-lasting financial policy which increased the stock markets independence significantly through sudden crisis events. The findings will help scholars to understand the performance of the U.S. and Chinese stock markets under several crisis events and the changes in the Chinese stock market over time.

To compare and analyze the difference between the United States and Chinese stock market, the relative variance, correlation, beta, and volatility of different stock markets are required. These parameters could help people to make better decisions and risk predictions while investing in China or the United States. The analysis also shows cases and the unique characteristics of both the Chinese and United States stock market, leading to the conclusion that the United States stock market is more mature and stable than the Chinese stock market. Through the comparison between the two stock markets, the Chinese and American economies could be better understood.

The purpose of the study had been aimed to further the understanding in exploring the relevance performance of the monopoly stock of Tenaga Nasional Berhad (TNB) to assess the stock performance return in comparison against the Malaysian stock market with reference towards the measurement of the Kuala Lumpur Stock Exchange (KLSE) market index performance as the benchmark. With reference to the previous studies, there is relevance support to identify the tendency of the findings to suggest the higher performance for the major stocks like TNB stock where the business model of TNB being monopolizing the industry creating the upper hand for the stability in driving the revenue and profit leading to higher value in the stock price. The methodology of the research had further the quantitative analysis study using the historical data of 10years from 2014 to 2023 to identify the potential pattern and trend to assess the comparison for the performance and trading trend for both the TNB stock and market index of KLSE. The outcome of the research had suggested the sufficient evidence to identify the higher average return for the TNB stock over the negative return average being achieved by the KLSE market index putting clear picture on the higher performance of the monopoly TNB stock. In addition, the growth of the trading volume trend had suggested that the investors are being higher confidence towards the growth of the TNB stock where the growth of the trading volume for TNB stock is higher than the trading volume for KLSE market index and even exceeding the average return for the TNB stock. 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PurposeThis study aims to examine the long memory as well as the effect of structural breaks in the US and the Chinese stock markets. More importantly, it further explores possible causes of the differences in long memory between these two stock markets.Design/methodology/approachThe authors employ various methods to estimate the memory parameters, including the modified R/S, averaged periodogram, Lagrange multiplier, local Whittle and exact local Whittle estimations.FindingsChina's two stock markets exhibit long memory, whereas the two US markets do not. Furthermore, long memory is robust in Chinese markets even when we test break-adjusted data. The Chinese stock market does not meet the efficient market hypothesis (EMHs), including the efficiency of information disclosure, regulations and supervision, investors' behavior, and trading mechanisms. Therefore, its stock prices' sluggish response to information leads to momentum effects and long memory.Originality/valueThe authors elaborately illustrate how long memory develops by analyzing not only stock market indices but also typical individual stocks in both the emerging China and the developed US, which diversifies the EMH with wider international stylized facts and findings when compared with previous literature. A couple of tests conducted to analyze structural break effects and spurious long memory demonstrate the reliability of the results. The authors’ findings have significant implications for investors and policymakers worldwide.

Purpose “Metaverse” has become a buzzword in the Chinese stock market. However, it remains unclear whether a firm's metaverse-related announcements will elicit positive stock market reactions. Whether and how stakeholder reactions are influenced by a firm's metaverse-related readiness also needs to be further explored. This study aims to discuss the aforementioned objective. Design/methodology/approach The authors derived a set of factors based on readiness theory and business ecosystem literature and extend them into the context of the metaverse. The authors used a sample of 642 Chinese listed firms in 2021 to investigate the hypotheses through the event study. Findings The study’s findings show that metaverse coverage induces a positive stock market reaction, but it is subject to three moderating effects. The authors introduce the novel concepts of IT readiness, ecosystem readiness and digital infrastructure readiness as the moderators. Stakeholders perceive metaverse announcements as overhyped, and stock prices do not fluctuate significantly after a metaverse announcement when the listed firms are not ready to embrace the metaverse. Originality/value This study is one of the first that introduces the event study method into the metaverse research, and it reveals that different levels of readiness influence stakeholders' evaluations and reactions to corporate metaverse coverage. This provides empirical evidence on metaverse development in China from the stock market's perspective.

Purpose – The purpose of this paper is to examine whether the framework of Prospect Theory and Mental Accounting proposed by Grinblatt and Han (2005) can be applied to analyzing the relationship between the disposition effect and momentum in the Chinese stock market. Design/methodology/approach – The paper applies the methodology proposed by Grinblatt and Han (2005). Findings – Using firm-level data, with a sample period from January 1998 to June 2013, the authors find evidence that the momentum effect in the Chinese stock market is not driven by the disposition effect, contradicting the findings of Grinblatt and Han (2005) concerning the US stock market. The discrepancies in the findings between the Chinese and US stock markets are robust and independent of sample periods. Research limitations/implications – The findings suggest that Grinblatt and Han’s model may not be applicable to the Chinese stock market. This is possibly because of the regulatory differences between the two stock markets and cross-national variation in investor behavior; in particular, the short-selling prohibition in the Chinese stock market and greater reference point adaptation to unrealized gains/losses among Chinese compared to Americans. Originality/value – This study provides evidence of the inapplicability of Grinblatt and Han’s model for the Chinese stock market, and shows the differences in the relationship between disposition effect and momentum between the Chinese and US stock markets.

Internet attention and information asymmetry: Evidence from Qihoo 360 search data on the Chinese stock market

Initial value sensitivity of the Chinese stock market and its relationship with the investment psychology

Over time, investor behavior has become more complicated due to the unpredictability of the Chinese stock market. Major research has found that investor sentiment swings have a huge impact on the market. Therefore, investors need to observe and understand how investor sentiment affects the market in the right way. Much of the past literature has used textual data mining and event analysis to measure the impact of investor sentiment and its impact on stock market returns, pricing, and volatility. Several articles have concluded that investor sentiment is highly responsive to the Chinese stock market. Another phenomenon that has emerged on various forums and social media is that people's fear of the stock market is spreading. The results of event analysis can prove that the impact of events such as the lifting of the ban on restricted shares and the listing of companies will be very obvious and have an impact on investor sentiment. An important implication of this study is that in addition to the dominant role of Granger causality in the causality that produces negative returns, negative investor sentiment is the main factor determining the overall behavior of the stock market. When bad news breaks, investors release their pent-up emotions for a short period, resulting in a violent reaction. In addition, market sentiment can also affect trading volumes and stock prices. This has put pressure on the market. In the end, this paper expands the future research direction from the two aspects of communication mechanism and quantitative analysis.