COVID-19, stock market and sectoral contagion in US: a time-frequency analysis

We assess the conditional relationship in the time-frequency domain between the return on S&P 500 and confirmed cases and deaths by COVID-19 in Hubei, China, countries with record deaths and the world, for the period from January 29 to June 30, 2020. Methodologically, we follow Aguiar-Conraria et al. (2018), by using partial coherencies, phase-difference diagrams, and gains. We also perform a parametric test for Granger-causality in quantiles developed by Troster (2018). We find that short-term cycles of deaths in Italy in the first days of March, and soon afterwards, cycles of deaths in the world are able to lead out-of-phase US stock market. We find that low frequency cycles of the US market index in the first half of April are useful to anticipate in an anti-phasic way the cycles of deaths in the US. We also explore sectoral contagion, based on dissimilarities, Granger causality and partial coherencies between S&P sector indices. Our findings, such as the strategic role of the energy sector, which first reacted to the pandemic, or the evidence about predictability of the Telecom cycles, are useful to tell the history of the pass-through of this recent health crises across the sectors of the US economy.     

Impact of COVID-19 on stock market efficiency: Evidence from developed countries

This study investigates the impact of the novel coronavirus (COVID-19) pandemic on stock market efficiency for six hard-hit developed countries, namely, the United States (US), Spain, the United Kingdom (UK), Italy, France, and Germany. Applying the wild bootstrap automatic variance ratio test on daily stock market data from July 29, 2019 to January 25, 2021, it is found that all stock markets used in this study deviate from market efficiency during some periods of the pandemic. Deviations from market efficiency are seen more in the stock markets of the US and UK during the COVID-19 outbreak than in other stock markets. These results are strengthened when a different econometric method, the automatic portmanteau test, is used. The findings of this study indicate an increasing chance for stock price predictions and abnormal returns during the COVID-19 pandemic.     

Crude oil market and stock markets during the COVID-19 pandemic: Evidence from the US,Japan, and Germany

We analyzed the return and volatility spillover between the COVID-19 pandemic in 2020, the crude oil market, and the stock market by employing two empirical methods for connectedness: the time-domain approach developed by Diebold and Yilmaz (2012) and the method based on frequency dynamics developed by Barunik and Krehlik (2018). We find that the return spillover mainly occurs in the short term; however, the volatility spillover mainly occurs in the long term. From the moving window analysis results, the impact of COVID-19 created an unprecedented level of risk, such as plummeting oil prices and triggering the US stock market circuit breaker four times, which caused investors to suffer heavy losses in a short period. Furthermore, the impact of COVID-19 on the volatility of the oil and stock markets exceeds that caused by the 2008 global financial crisis, and continues to have an effect. The impact of the COVID-19 pandemic on financial markets is uncertain in both the short and long terms. Our research provides some urgent and prominent insights to help investors and policymakers avoid the risks in the crude oil and stock markets because of the COVID-19 pandemic and reestablish economic development policy strategies.     

The impact of COVID-19 on the stock market crash risk in China

This study investigates the impact of the COVID-19 pandemic on the stock market crash risk in China. For this purpose, we first estimated the conditional skewness of the return distribution from a GARCH with skewness (GARCH-S) model as the proxy for the equity market crash risk of the Shanghai Stock Exchange. We then constructed a fear index for COVID-19 using data from the Baidu Index. Based on the findings, conditional skewness reacts negatively to daily growth in total confirmed cases, indicating that the pandemic increases stock market crash risk. Moreover, the fear sentiment exacerbates such risk, especially with regard to the impact of COVID-19. In other words, when the fear sentiment is high, the stock market crash risk is more strongly affected by the pandemic. Our evidence is robust for the number of daily deaths and global cases.     

Market sentiment to COVID-19 and the Chinese stock market

We classify the market sentiment to COVID-19 into expected and unexpected components and then examine their particular impacts on the stock market. We find that unexpected sentiment causes fluctuations in the stock market more than expected sentiment does. However, unexpected sentiment cannot affect stock market informativeness despite the remarkable informational effect of expected sentiment. Moreover, the relation between expected sentiment and stock market fluctuation or informativeness is one-way, whereas there exists a two-way interaction between unexpected sentiment and stock market fluctuation. This further confirms that expected sentiment is informational, whereas unexpected sentiment is quite noisy and informationally harmful.     

In search of COVID-19 and stock market behavior

The aim of this paper is two-fold. First, we investigate the nexus between investor attention to COVID-19 and daily returns in 59 countries. We use Google Search Volume Index to account for investor attention. Our empirical findings suggest that the search volume of the pandemic is negatively associated with daily returns. The effect was strong in the week that the World Health Organization declared it as pandemic and among advanced countries. Second, we explore the relationship between search volume and market volatility. The findings suggest that COVID-19 sentiment generated excess volatility in the market. Our findings remain robust with alternative specifications.     

Machine learning sentiment analysis,COVID-19 news and stock market reactions

The recent COVID-19 pandemic represents an unprecedented worldwide event to study the influence of related news on the financial markets, especially during the early stage of the pandemic when information on the new threat came rapidly and was complex for investors to process. In this paper, we investigate whether the flow of news on COVID-19 had an impact on forming market expectations. We analyze 203,886 online articles dealing with COVID-19 and published on three news platforms (MarketWatch.com, NYTimes.com, and Reuters.com) in the period from January to June 2020. Using machine learning techniques, we extract the news sentiment through a financial market-adapted BERT model that enables recognizing the context of each word in a given item. Our results show that there is a statistically significant and positive relationship between sentiment scores and S&P 500 market. Furthermore, we provide evidence that sentiment components and news categories on NYTimes.com were differently related to market returns.     

When ESG talks: ESG tone of 10-K reports and its significance to stock markets

Since the ESG topic consistently gains on importance in the investment universe, companies provide investors with information regarding recent and future ESG activities through different reporting channels. The most recent research finds relevance of ESG-related corporate activities for formation of investors' opinion regarding companies' valuations and growth prospects. Based on a sample of more than seventeen thousand unique 10-K reports of US companies filed with SEC in period 2013 to 2019 and the word-power methodology proposed by Jegadeesh and Wu (2013), this study also shows evidence for significant relation of ESG textual tone of 10-K reports to stock market returns of filing companies around the report filing dates. Using the ESG linguistic dictionary recently proposed by Baier, Berninger, and Kiesel (2020), this study shows evidence for significant relation of social and governance-related topics disclosure to stock returns, while environmental narratives being ignored by the markets. When looking at individual words from the ESG lexicon, such words as “community”, “health”, “control” imply positive reaction of markets, while “discrimination”, “embezzlement”, and “crime” are related to negative returns. The robustness analysis based on the inverse document frequency word weightings and actual ESG performance scores confirms the significance of ESG information disclosure of 10-K reports for investors. Thus, this study sheds light on the mechanics of ESG information perception and its influence on capital markets.     

COVID-19, government policy responses,and stock market liquidity around the world: A note

Unprecedented non-pharmaceutical interventions targeted to curb the spread of COVID-19 exerted a dramatic impact on the global economy and financial markets. This study is the first attempt to investigate the influence of these government policy responses on global stock market liquidity. To this end, we examine daily data from 49 countries for the period January-April 2020. We demonstrate that the impact of the interventions is limited in scale and scope. Workplace and school closures deteriorate liquidity in emerging markets, while information campaigns on the novel coronavirus facilitate trading activity.     

COVID-19 and A-share banks' stock price volatility: From the perspective of the epidemic evolution in China and the US

With a financial market dominated by indirect financing, China's banking system played a critical role in the government's response to COVID-19, which piqued our interest in the short-term impact of COVID-19 on the risk of China's banks. Examining the stock price of A-share listed banks and the number of confirmed cases in China and the US during the short time window surrounding the COVID-19 pandemic's outbreak, this study reveals that COVID-19 increased the A-share banking price volatility in both China and the US, reflecting a strong spillover effect of the US economic and financial system. Furthermore, COVID-19 in China has a smaller impact on the stock price volatility of China's state-owned banks (SOBs) than that of medium- and small-sized (M&S) banks, reflecting the higher risk resistance capability of large SOBs. Further analysis confirms that the impact primarily reflected systematic risk rather than idiosyncratic risk, as small and micro enterprises and M&S banks received more targeted financial support from the government. In contrast, large banks took on more responsibilities in the emergency financial stimulus, narrowing the idiosyncratic risk gap between the two types of banks and allowing the banking industry to better play its core role in the recovery of real economy in China. These findings will assist us in better understanding the effectiveness of financial assistance policies during the epidemic and will provide insights for future policymaking during similar crises.     

COVID-19 pandemic,oil prices,stock market,geopolitical risk and policy uncertainty nexus in the US economy: Fresh evidence from the wavelet-based approach

In this paper, we analyze the connectedness between the recent spread of COVID-19, oil price volatility shock, the stock market, geopolitical risk and economic policy uncertainty in the US within a time-frequency framework. The coherence wavelet method and the wavelet-based Granger causality tests applied to US recent daily data unveil the unprecedented impact of COVID-19 and oil price shocks on the geopolitical risk levels, economic policy uncertainty and stock market volatility over the low frequency bands. The effect of the COVID-19 on the geopolitical risk substantially higher than on the US economic uncertainty. The COVID-19 risk is perceived differently over the short and the long-run and may be firstly viewed as an economic crisis. Our study offers several urgent prominent implications and endorsements for policymakers and asset managers.     

Heterogeneously informed trading and the stock market efficiency during the COVID-19 pandemic

This study investigates the U.S. stock market efficiency from the symmetric and asymmetric perspectives during the COVID-19 pandemic. We explore that the pandemic boosts (hurts) the information role of symmetrically (asymmetrically) informed trading. Specifically, we find that the epidemic outbreak and infection scale strengthen (weaken) the stock return reaction to symmetrically (asymmetrically) informed trading. Evidence also indicates that the effect of symmetrically (asymmetrically) informed trading on stocks' permanent price shocks and price informational efficiency is enhanced (impaired) during the pandemic. Moreover, all these effects are consistently more intensive to informed buys.     

The role of stock ownership by US members of Congress on the market for political favors

I examine whether stock ownership by politicians helps to enforce noncontractible quid pro quo relations with firms. The ownership by US Congress members in firms contributing to their election campaigns is higher than in noncontributors. This bias toward contributors depends on the financial incentives of politicians and the relation's value. Firms with a stronger ownership–contribution association receive more government contracts. The financial gains from these contracts are economically large. When politicians divest stocks, firms discontinue contributions to the politicians, lose future contracts, and perform poorly. Politicians divest the stocks in contributors, but not in noncontributors, in anticipation of retirement.     

Securities regulation,household equity ownership,and trust in the stock market

Review of Accounting Studies - Using aggregate data from national accounts, we study whether strengthening and harmonizing securities regulation across the European Union increases household equity...     

Cross-sector comovements and policy impact in the COVID-19 stock market: A dynamic factor approach

This study investigates the remarkable comovements in U.S. equity returns during the COVID-19 pandemic. It constructs a dynamic factor model (DFM) to illuminate the sources of the comovements and their implications. Using the Markov Chain Monte Carlo (MCMC) estimation method, the study finds that the comovements had a weak daily oscillation pattern during the pandemic. With that pattern, the study also finds significant monetary policy effects on the equity returns of several key sectors. In addition, it estimates the impact of news shocks, including monetary policy news, fiscal stimulus news, and unemployment news, on cross-sector equity returns. For any given sector, the conventional and unconventional monetary policy news shocked the sector in opposite directions. Among the positive monetary news shocks, the strongest were from interest rate policy surprises. Conversely, fiscal stimulus news had the most substantial positive impact and triggered all sectors to rebound from the bear market at the end of March 2020. Furthermore, by applying Natural Language Processing (NLP) sentiment analysis, this study sheds light on the positive correlation between comovements and news sentiment.     

Foreign institutional ownership and stock market liquidity: Evidence from Indonesia

From January 2002 to August 2007, foreign institutions held almost 70% of the free-float value of the Indonesian equity market, or 41% of the total market capitalization. Over the same period, liquidity on the Jakarta Stock Exchange improved substantially with the average bid–ask spread more than halved and the average depth more than doubled. In this study we examine the Granger causality between foreign institutional ownership and liquidity, while controlling for persistence in foreign ownership and liquidity measures. We find that foreign holdings have a negative impact on future liquidity: a 10% increase in foreign institutional ownership in the current month is associated with approximately 2% increase in the bid–ask spread, 3% decrease in depth, and 4% rise in price sensitivity in the next month, challenging the view that foreign institutions enhance liquidity in small emerging markets. Our findings are consistent with the negative liquidity impact of institutional investor ownership in developed markets.     

The Japanese stock market and the macroeconomy: An empirical investigation

We investigate the relationship between macroeconomic variables, such as the industrial production index, interest rate and inflation rate, and the stock market, using Toda and Yamamoto (1995)'s vector autoregressions (VAR) specification. The major findings are: (1) macroeconomic variables do Granger cause the stock market variable, while reverse is not so clear. (2) The lagged stock market variable affects its current value but its impact tend to diminish in the long-run. Policy implication we draw is that the price keeping operation by the Japanese government would not work, but appropriate macroeconomic policies would benefit not only the real market but also the stock market.     

Idiosyncratic volatility,fundamentals, and institutional herding: Evidence from the Japanese stock market

Using Japanese data from 1975 to 2003, we show that both institutional herding and firm earnings are positively related to idiosyncratic volatility. We reject the hypothesis that institutional investors herd toward stocks with high idiosyncratic volatility and systematic risk. Our results suggest that a behavior story may explain the negative premium earned by high idiosyncratic volatility stocks found by Ang et al. [Ang, Andrew, Hodrick, Robert J., Yuhang Xing, Xiaoyan Zhang, 2004. The cross-section of volatility and expected returns, Forthcoming Journal of Finance]. We also find that the dispersions of change in institutional ownership and return-on-asset move together with the market aggregate idiosyncratic volatility over time. Our results suggest that investor behavior and stock fundamentals may both help explain the time-series pattern of market aggregate idiosyncratic volatility.     

The medium is the message: Learning channels,financial literacy,and stock market participation

This paper investigates the effects of learning channels on stock market participation. More specifically, we investigate the direct effects of learning about financial matters from one's private network, financial advisors, and the media, as well as the moderating effects of financial literacy on the relationship between learning from these channels and stock market participation. Analyzing a unique cross-section data that combine survey data and bank register data on individual retail investors, we find that media is the only learning channel that increases the likelihood of owning stocks and the portfolio share invested in stocks. We also find that financial literacy has a significant moderating effect: Interactions point to the joint importance of learning from media and financial literacy for individuals' stock market participation. Our findings suggest implications to policymakers when designing financial education programs.