Does non-fundamental news related to COVID-19 matter for stock returns? Evidence from Shanghai stock market

Role of proliferation COVID-19 media chatter in predicting Indian stock market: Integrated framework of nonlinear feature transformation and advanced AI

The outbreak of the COVID-19 pandemic has transpired the global media to gallop with reports and news on the novel Coronavirus. The intensity of the news chatter on various aspects of the pandemic, in conjunction with the sentiment of the same, accounts for the uncertainty of investors linked to financial markets. In this research, Artificial Intelligence (AI) driven frameworks have been propounded to gauge the proliferation of COVID-19 news towards Indian stock markets through the lens of predictive modelling. Two hybrid predictive frameworks, UMAP-LSTM and ISOMAP-GBR, have been constructed to accurately forecast the daily stock prices of 10 Indian companies of different industry verticals using several systematic media chatter indices related to the COVID-19 pandemic alongside several orthodox technical indicators and macroeconomic variables. The outcome of the rigorous predictive exercise rationalizes the utility of monitoring relevant media news worldwide and in India. Additional model interpretation using Explainable AI (XAI) methodologies indicates that a high quantum of overall media hype, media coverage, fake news, etc., leads to bearish market regimes.

Corporate vulnerability in the US and China during COVID-19: A machine learning approach

The impact of COVID-19 on stock market dynamics and other macroeconomic indicators has been extensively researched. However, the question of how it affects corporate vulnerability has received less attention. This article aims to fill this gap by examining the implications of COVID-19 on corporate vulnerability in the United States (US) and China, using daily data from January 2020 to December 2021. The empirical results of cointegration analysis demonstrate that COVID-19 considerably worsen corporate vulnerabilities in the long-term in the US and in the short-term in China. Additionally, non-linear results demonstrate long-run asymmetries in the US and short-run asymmetries in China, confirming the accuracy of error prediction and suggesting that US corporations are more exposed to COVID-19-induced risks. The channels through which COVID-19 may affect corporate vulnerability include changes in consumer behavior and demand, disruptions in supply chains, financial stress, government policies and regulations, and changes in the competitive landscape. This study sheds light on the effects of the COVID-19 pandemic on corporate vulnerability in the US and China, revealing regulatory implications that may necessitate greater government involvement, managerial implications that emphasize risk management and contingency planning, and social implications that highlight the importance of prioritizing stakeholder welfare and embracing digital transformation.

Time-varying effects of fuel prices on stock market returns during COVID-19 outbreak

This article explores the impact of fuel price movements on the stock market return of 2020 during the COVID-19 disruptions. In doing so, a monthly data of seven selected stock market indices representing developed and emerging economies globally was used for analysis. The study used a time-varying parameter VAR model to examine a time-varying causal association between oil prices and stock market returns and a novel quantile-causality approach to capture the fluctuations of these markets under COVID-19's varying market conditions. The study further utilises the entropy transfer approach to capture the Granger-causal relationship in the presence of nonlinearities of the data series. The results indicate a high information flow from fuel prices to the FTSE-100, Pacific, and European stock indicies, but not the other way round. The results show that, for the FTSE-100 and the European region, there is a two-way information flow between equities and natural gas, and vice-versa. However, a one-way information flow was established from the stock market to the Pacific and emerging economies.

Probability of informed trading during the COVID-19 pandemic: the case of the Romanian stock market

Using data from the Bucharest Stock Exchange, we examine the factors influencing the probability of informed trading (PIN) during February-October 2020, a COVID-19 pandemic period. Based on an unconditional quantile regression approach, we show that PIN exhibit asymmetric dependency with liquidity and trading costs. Furthermore, building a customized database that contains all insider transactions on the Bucharest Stock Exchange, we reveal that these types of orders monotonically increase the information asymmetry from the 50th to the 90th quantile throughout the PIN distribution. Finally, we bring strong empirical evidence associating the level of information asymmetry to the level of fake news related to the COVID-19 pandemic. This novel result suggests that during episodes when the level of PIN is medium to high (between 15 and 50%), any COVID-19 related news classified as misinformation released during the lockdown period, is discouraging informed traders to place buy or sell orders conditioned by their private information.

COVID-19 and stock market performance: Evidence from the RCEP countries☆

As the world's largest trading bloc, the agreement of RCEP, which was formalized in September 2020, is believed to play a non-neglectable role in the post-pandemic recovery. Real economies and the capital markets of the participating countries will have greater interactions due to tariff reduction and negative lists. By looking into the shocks in early 2020 that affect the stock markets of RCEP participating countries, we measure the stock market reaction to common risks just before the RCEP agreement was formalized. Following return-based, volume-based and liquidity-based event-study approaches, we use daily data from 11 Asia-Pacific countries to examine the stock market reactions. We find that RCEP economies for which the agreement took effect on January 1st, 2022 showed better risk resistance in response to COVID-19 shocks. In the long run, trading benefits brought by the RCEP agreement are expected to form and strengthen a system of circular flow of international trading activities among the participating countries, which will in turn increase the risk resistance ability of their stock markets.

Comparing asymmetric price efficiency in regional ESG markets before and during COVID-19

The ever-emerging environmental, social, and governance (ESG) concerns have received significant attention of policymakers, governments, regulation bodies, and investors. Considering the markets volatilities due to economic and financial uncertainties that can drive the informational price inefficiencies across the markets, this study compares the asymmetric price efficiency of regional ESG markets by using an asymmetric multifractal detrended fluctuation analysis before and during COVID-19 crisis. We then examine whether global factors influence the asymmetric efficiency of regional ESG markets. Our findings reveal that COVID-19 outbreak reduced the efficiency of regional ESG markets, except for Europe, which sustained its efficiency even during the pandemic. Moreover, global factors drive the efficiency of regional ESG markets significantly before and during COVID-19. A major implication of our findings stems from the fact that a contagion reduces the efficiency of the markets while stable economic conditions make those markets informationally efficient.

THE IMPACT OF COVID-19 ON STOCK MARKET PERFORMANCE: EVIDENCE FROM TEN ASIAN COUNTRIES

The purpose of this study is to examine the effect of COVID-19 on stock market performance from the aspects of stock price volatility and stock market risk in the top GDP10 Asian countries. The dependent variable has been used in this study, which is the stock price volatility and stock market risk, while the independent variable that has been involved in this research is confirmed cases and death cases from COVID-19. In addition, the control variables that will be taken into this study are gross domestic product (GDP) and exchange rate. The study is examined between January 1, 2020 and June 30, 2020. Data were all collected from the source Investing.com, Trading Economies, Worldometer and World Bank. The findings show a significant positive relationship between the impacts of Covid-19 confirmed cases on stock price volatility among the top 10 Asian Country. However, there is an insignificant relationship between the impacts of Covid-19 death cases on stock price volatility among the top 10 GDP Asian Country. Covid-19 confirmed cases and death cases are insignificant on stock market risks among the top 10 GDP Asian Country.

COVID-19 and Indigenous health in the Brazilian Amazon

We test whether the COVID-19 pandemic has an ethnicity-differentiated (Indigenous vs non-Indigenous) effect on infant health in the Brazilian Amazon. Using vital statistics data we find that Indigenous infants born during the pandemic are 0.5% more likely to have very low birth weights. Access to health care contributes to health gaps. Thirteen percent of mothers travel to deliver their babies. For traveling mothers, having an Indigenous baby during the pandemic increases the probability of very low birth weight by 3%. Indigenous mothers are 7.5% less likely to receive adequate prenatal care. Mothers that travel long distances to deliver their babies and give birth during the pandemic are 35% less likely to receive proper prenatal care. We also find evidence that the pandemic shifts medical resources from rural to urban areas, which disproportionately benefits non-Indigenous mothers. These results highlight the need for policies to reduce health inequalities in the Amazon.

Changes in social behavior and impacts of the COVID-19 pandemic on regional housing markets: Independence and risk

This paper explores changes in social behavior since the start of the COVID-19 pandemic, which are characterized by reduction in relocation, mobility, and community engagement, and how the correlations between regional housing markets are affected by these changes. Because changes in mobility and engagement are the most apparent in large cities, the present study calculates the independence indicator of regional housing markets in the 50 largest metropolitan statistical areas (MSAs) in the United States and determines their relationship with Mobility and Engagement Index values. The empirical results show that as mobility and community engagement decline in a certain area, housing market fluctuations become more independent, indicating correlations between regional housing markets in the US might decrease after the COVID-19 outbreak. This paper also finds that there are more MSAs having significantly decreased in volatility since the outbreak of the pandemic. This paper provides evidence indicating that housing markets may be impacted differently by the COVID-19 pandemic than other asset markets, particularly stock markets. Changes in mobility and engagement can be used as an indicator to assess whether the correlation between regional housing markets would decline, which means that, compared with financial instruments, more factors from real aspects need to be considered when determining the changes in real estate affected by the epidemic.

What drives US stock markets during the COVID-19 pandemic? A global sensitivity analysis

This paper identifies robust determinants of US stock price movements in the economic shadow of the COVID-19 crisis and in the presence of model uncertainty, using several influential factors highlighted in relevant research. Our investigation performs an extreme bounds analysis (EBA), a global sensitivity framework capable of handling the problem of model uncertainty. We document that excess market returns, term spread, implied volatility, oil, Twitter-based economic uncertainty, and European and Chinese stock returns are the only variables that are robust to all possible variations in the condition set of information. The results also reveal the irrelevance of newly reported COVID-19 cases and deaths as novel drivers that contribute to the formation stock prices, thus lending support to the “psychophysical numbing” phenomenon.

Unemployment claims during COVID-19 and economic support measures in the U.S

Governments want to know how effective COVID-19 anti-contagion policies and implemented economic stimulus measures have been to plan their short-run interventions. We condition on the state of the pandemic to assess the impact of non-pharmaceutical interventions and economic stimulus policies on the excess unemployment insurance claims in the United States. We focus on weekly data between February 2020 and January 2021 and motivate our analysis by the theoretical framework of the second-wave SIR-macro type models to build a panel Vector AutoRegressive (VAR) specification. Non-pharmaceutical interventions become effective immediately and impact the labor market negatively. Economic stimulus takes about a month to turn effective and only partially eases the economic welfare losses. Health-related restrictive measures are primarily driven by the state of the pandemic. Economic support policies depend predominantly on the reaction of the labor market rather than the severity of the pandemic itself.

Firm-level short selling and the local COVID-19 pandemic: Evidence from China

Short seller trading behavior attracts much attention, especially when negative shocks occur. Recent literature has focused on the impact of the COVID-19 pandemic, an unprecedented shock, but evidence on short sellers' reactions is quite scarce. This paper investigates how short sellers responded to the local COVID-19 pandemic in China. Empirical results show that greater numbers of newly confirmed COVID-19 cases in listed firms' headquarters locations are associated with more subsequent short selling of those firms. The results hold after addressing other potential concerns. In addition, the impact of the local COVID-19 pandemic on short selling is stronger for firms with weaker financial conditions, in more vulnerable industries, and with higher risks of a stock price crash. The impact is alleviated after lifting the lockdown restrictions in Wuhan and becomes insignificant in later outbreaks. Overall, our findings support the informational role of short sellers within the context of the COVID-19 pandemic.

Market Quality and Short-Selling Ban during the COVID-19 Pandemic: A High-Frequency Data Approach

The recent emergence of COVID-19 and the subsequent short-selling restriction (SSR) imposed on some equity markets provide us with a unique framework to analyze the effects of this kind of measure on market quality in the context of increasingly automated equity markets. We contribute to the literature by analyzing the microstructure and quality parameters of the Spanish equity market during COVID-19 and SSR. We study four subperiods, namely pre-crisis, turmoil, SSR, and first de-escalation periods, by means of a tick-by-tick dataset and the complete limit order book (LOB). We observe the following impact of the SSR on the constituents of IBEX 35: (1) the SSR did comply partially with its aim at an intraday level regarding volatility, but liquidity was reduced; (2) liquidity deterioration affected more the sell than the buy side of the LOB; (3) high-frequency activity (HFT) diminished during SSR, reinforcing volatility; (4) negative effects on liquidity and HFT diminished and disappeared as the ban was lifted; (5) HFT unidirectionally Granger causes 1 min realized volatility while the natural logarithm of the slope of the LOB bidirectionally Granger causes 1 min realized volatility.

How does China's stock market react to supply chain disruptions from COVID-19?

As a once-in-a-century global pandemic, COVID-19 severely hit the global economy and disrupted the worldwide supply chain. Based on 505 Chinese firms, we use the event study method to explore the effect of COVID-19 on the financial performance of firms. The findings show that COVID-19 has an immediate impact on Chinese firms. Hubei firms experience stronger effects than non-Hubei firms. Supply chain disruptions effects from COVID-19 are observed. Transportation industry is hit more severely than retail industry. Insurance companies experience a strong adverse effect. On the other hand, both medical and competitor firms experience significantly positive spillover effects.

Economic recovery forecasts under impacts of COVID-19

This paper proposes a joint model by combining the time-varying coefficient susceptible-infected-removal model with the hierarchical Bayesian vector autoregression model. This model establishes the relationship between several critical macroeconomic variables and pandemic transmission states and performs economic predictions under two predefined pandemic scenarios. The empirical part of the model predicts the economic recovery of several countries severely affected by COVID-19 (e.g., the United States and India, among others). Under the proposed pandemic scenarios, economies tend to recover rather than fall into prolonged recessions. The economy recovers faster in the scenario where the COVID-19 pandemic is controlled.

The COVID-19 pandemic, consumption and sovereign credit risk: Cross-country evidence

Many recent studies investigate the economic effect of the COVID-19 pandemic in multiple aspects, while whether and how the sovereign credit risk reacts to the shock is still underexplored. Using a sample of forty developed and developing countries and employing staggered difference-in-differences models, we find that the sovereign credit risk measured by sovereign credit default swap spreads significantly increases after the COVID-19 pandemic outbreak, and the adverse effect is more pronounced for short-term credit risk. The reason is that the pandemic causes severe concerns about aggregate consumption contraction in addition to the fiscal capacity and the volatility of exports. We also find that fiscal stimuli stabilizing consumer spending alleviate the adverse effect of the COVID-19 pandemic, while debt relief does not matter. Overall, practitioners and policy makers should attach more importance to consumption and its recovery during the pandemic when making decisions.

Effects of COVID-19 Pandemic on the Bulgarian Stock Market Returns

The purpose of this paper is to provide the first empirical research analysing the effects of the COVID-19 pandemic on the Bulgarian stock market before its onset and in the four pandemic waves. For this purpose, we used a fixed effect panel data regression model for the stock returns of 23 companies listed on the Bulgarian Stock Exchange from 2 January 2020 to 16 November 2021. The study showed that the growth rate of COVID-19 deaths per day in Bulgaria had a negative effect on the stock returns and had the strongest influence on them in the fourth pandemic wave. In addition, our results showed that stock returns in healthcare, IT, utilities, and real estate sectors were negatively affected before the COVID-19 pandemic while the first COVID-19 pandemic wave had a positive effect on healthcare and consumer staples sectors. During the second COVID-19 wave, the stock returns of the IT sector had a positive effect, while Utilities sector had a negative effect. The third COVID-19 wave had a positive effect on industrials and consumer staples sectors, while healthcare, real estate, and IT sectors showed a negative effect. During the fourth COVID-19 wave, the stock returns of the IT sector had a positive effect and consumer staples sector had a negative effect.

Dynamic nonlinear connectedness between the green bonds, clean energy, and stock price: the impact of the COVID-19 pandemic

This paper uses weekly data from July 01, 2011 to July 09, 2021 to examine the dynamic nonlinear connectedness between the green bonds, clean energy, and stock price around the COVID-19 outbreak in the global markets. By building a time-varying parameter vector autoregression model (TVP-VAR), the comparison analyses of pre- and during the COVID-19 sample groups verify the existence of nonlinear and dynamic correlation among the three variables. First, prior to the COVID-19 pandemic, the simultaneous impacts of clean energy on stock price increased over time. Second, the results of impulse responses at different horizons indicate that green bonds lead to a short-term increase of clean energy, and it exerts an increasingly positive impacts after the COVID-19 outbreak. The COVID-19 has weakened the negative impacts of green bonds on stock price in the medium term. Finally, through the analysis of impulse responses at different points, we find that stock prices will rise when clean energy is subjected to a positive shock, and this positive effect is stronger during economic recovery period than in the other two periods.

COVID-19 Pandemic and Romanian Stock Market Volatility: A GARCH Approach

This paper investigates the volatility of daily returns on the Romanian stock market between January 2020 and April 2021. Volatility is analyzed by means of the representative index for Bucharest Stock Exchange (BSE), namely, the Bucharest Exchange Trading (BET) index, along with twelve companies traded on BSE. The quantitative investigation was performed using GARCH approach. In the survey, the GARCH model (1,1) was applied to explore the volatility of the BET and BSE traded shares. Conditional volatility for the daily return series showed noticeable evidence of volatility that shifts over the explored period. In the first quarter of 2020, the Romanian equity market volatility increased to a level very close to that recorded during the global financial crisis of 2007–2009. Over the next two quarters, volatility had a downward trend. Besides, after VAR estimation, no causal connection was found among the COVID-19 variables and the BET index.

Cryptocurrency volatility forecasting: What can we learn from the first wave of the COVID-19 outbreak?

This study aims to examine the issue of cryptocurrency volatility modelling and forecasting based on high-frequency data. More specifically, this study assesses whether crisis periods, particularly the coronavirus disease pandemic, influence the dynamic of cryptocurrency volatility. We investigate the four main cryptocurrency markets (Bitcoin, Ethereum Classic, Ethereum, and Ripple) from April 2018 to June 2020. The realized volatility measure is computed and decomposed to various components (continuous versus discontinuous, positive and negative semi-variances, and signed jumps). A variety of heterogeneous autoregressive (HAR) models are developed including these components, thereby enabling assessment of different assumptions (including persistence and asymmetric dynamic) of modelling and volatility forecasting based on in-sample and out-of-sample forecasting strategies, respectively. Our results reveal three main findings. First, the extended HAR model that includes the positive and negative jumps appears to be the best model for predicting future volatility for both crisis and non-crisis periods. Second, during the crisis period, only the negative jump component is statistically significant. Third, in terms of volatility forecasting, the results show that the extended HAR model that includes positive and negative semi-variances outperform the other models.