Pandemic-induced fear and stock market returns: Evidence from China

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

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.

Can climate change attention predict energy stock returns?

We propose a climate change attention (CCA) index based on Google search volume index (GSVI) from 2004 to 2021 and show that it is an economically and statistically significant negative predictor for next month's energy stock returns. The index is extracted using principal component analysis (PCA), but the results are similar by using the equal-weighted average method. Compared with 14 traditional macroeconomic predictors, CCA performs the best and provides complementary information when added into bivariate and multivariate macro predictive models. When further considering the effect of CCA's forecasting power over different periods, strong evidence is shown that this outperformance is especially prominent in economic depressions and down market conditions. From the asset allocation perspective, CCA can provide a mean-variance investor with significant economic gains under alternative risk aversions. Our empirical results prove that investors' attention to climate change contains predictive information for excess returns of global traditional energy stock index.

Disaster response: The COVID-19 pandemic and insider trading around the world

This paper investigates how corporate insiders respond to the initial COVID-19 outbreaks. Using comprehensive insider transaction data from 25 countries, we document a consistent pattern of insider selling during the month after the first COVID-19 case is confirmed in a given country. Insider selling during these disease outbreaks is less pronounced in countries with higher information disclosure requirements, higher public enforcement index, a more efficient judiciary system, and stronger investor protection. Furthermore, cultural differences and the stringency levels of government responses to the COVID-19 outbreaks help moderate insider panic selling when health disasters strike. The findings suggest that a transparent, reliable business system contributes to rebuilding investor trust and corporate resilience during crises.

Impact of the Global Fear Index (COVID-19 Panic) on the S&P Global Indices Associated with Natural Resources, Agribusiness, Energy, Metals, and Mining: Granger Causality and Shannon and Rényi Transfer Entropy

The Global Fear Index (GFI) is a measure of fear/panic based on the number of people infected and deaths due to COVID-19. This paper aims to examine the interconnection or interdependencies between the GFI and a set of global indexes related to the financial and economic activities associated with natural resources, raw materials, agribusiness, energy, metals, and mining, such as: the S&P Global Resource Index, the S&P Global Agribusiness Equity Index, the S&P Global Metals and Mining Index, and the S&P Global 1200 Energy Index. To this end, we first apply several common tests: Wald exponential, Wald mean, Nyblom, and Quandt Likelihood Ratio. Subsequently, we apply Granger causality using a DCC-GARCH model. Data for the global indices are daily from 3 February 2020 to 29 October 2021. The empirical results obtained show that the volatility of the GFI Granger causes the volatility of the other global indices, except for the Global Resource Index. Moreover, by considering heteroskedasticity and idiosyncratic shocks, we show that the GFI can be used to predict the co-movement of the time series of all the global indices. Additionally, we quantify the causal interdependencies between the GFI and each of the S&P global indices using Shannon and Rényi transfer entropy flow, which is comparable to Granger causality, to confirm directionality more robustly The main conclusion of this research is that financial and economic activity related to natural resources, raw materials, agribusiness, energy, metals, and mining were affected by the fear/panic caused by COVID-19 cases and deaths.

Foreign to all but fluent in many: The effect of multinationality on shock resilience

The sudden COVID-19 pandemic sent shockwaves through international markets. This paper studies the relation between multinationality and risk. While IB literature agrees that internationalization, in times of relative stability, increases systematic risk, we argue that internationalization also improves resilience against exogenous shocks. Leveraging the sequential COVID-waves as a unique empirical laboratory, we show that although multinationality causes liability of foreignness that increases systematic risk, it also generates an asset of multinationality that enhances shock resilience. Yet this advantage of internationalized firms gradually erodes as less internationalized firms learn about the shock and investors adapt their valuations to the post-shock reality.

Time-Varying Causalities in Prices and Volatilities between the Cross-Listed Stocks in Chinese Mainland and Hong Kong Stock Markets

Due to the heterogeneity of investor structure between the Chinese mainland stock market (A-share market) and the Hong Kong stock market (H-share market) as well as the limitations on arbitrage activities, most cross-listed stocks in the two markets (AH stocks) have the characteristics of “one asset, two prices”, in which AH stocks with the same vote rights and dividend streams are traded at different prices in different markets. Based on the VAR (LA-VAR as well) model and a four-variable system including AH stock indices (AHXA, AHXH), the China Securities Index 300 (CSI 300), and the Hang Seng Index (HSI), this paper applies a new time-varying causality test to examine the causalities in prices and volatilities for two pairings (AXHA-AHXH pairing and CSI 300-HSI pairing) during the sample period spanning from 4 January 2010 to 21 May 2021. The empirical results exhibit statistically significant time-varying causalities of the two pairings. Specifically, at the price level, AHXH has a significant negative causal effect on AHXA from October 2017 to February 2020 except for several months in 2018, while AHXA merely has a negative impact on AHXA during a short period from March 2017 to May 2017. Of note, the direction of causalities in volatilities between AHXA and AHXH reverses. A positive causality is found from AHXA to AHXH at the 5% significance level during the period of April 2014 through May 2021, while no causality is detected in the opposite direction during the whole sample period. Meanwhile, the volatilities of CSI 300 significantly Granger cause those of HSI over the whole sample period, but not vice versa. Implications of our results are discussed.

Introspecting predictability of market fear in Indian context during COVID-19 pandemic: An integrated approach of applied predictive modelling and explainable AI

Financial markets across the globe have seen rapid volatility and uncertainty owing to scary and disruptive impacts of COVID-19 pandemic. Mayhem wrecked by frequent lockdowns, curfews, emergencies, etc. has stoked the high quantum of chaotic movement in equity markets and resulted in perplexed investor behaviour. It, therefore, is of paramount practical relevance to measure predictability of market fear at such a crucial juncture of time. Market fear can effectively be measured in terms of implied and historic volatility of equity markets. The present study chooses India VIX and 20-day rolling standard deviation of NIFTY returns to account for implied and historic volatility respectively during the ongoing COVID-19 timeline. Pertinent macroeconomic constructs, technical indicators and Google search volume index on meaningful keywords have been chosen as raw explanatory features for inspecting predictability. Boruta feature selection methodology has been used in a supervised manner to select significant features. State-Of-The-Art machine and deep learning algorithms namely Gradient Boosting (GB), Extra Tree Regression (ERT), Deep Neural Network (DNN), Long Short Term Memory Network (LSTM) are then used on processed feature set to scrupulously evaluate the quantum of predictability of said assets. The integrated predictive frameworks have been subjected to a battery of numerical and statistical checks to draw inferences. Additionally, Explainable AI frameworks have been used to analyse the nature of influence of respective features. Findings indeed suggest that despite exhibiting high degree of volatile traits, both India VIX and historic volatility can be predicted utilizing the proposed architectures effectively and serve practical actionable insights.