Returns,volatility and investor sentiment: Evidence from European stock markets

In this study, we show that patterns in returns behave as if investors, influenced by their level of optimism, selected stocks according to their volatility. Our goal is to confirm the contribution of behavioral finance while showing that investor sentiment can be profitably used by practitioners. We incorporate volatility in the relationship between investor sentiment and future returns, this is the main originality of our approach. Our methodology consists in comparing returns, volatility and higher-order moments of portfolios managed with investor sentiment against those obtained either with passive (buy and hold) portfolio management or with a minimum variance portfolio. Portfolios managed with investor sentiment have better returns and involve less risk under certain conditions.     

Have the predictability of oil changed during the COVID-19 pandemic: Evidence from international stock markets

The COVID-19 has undoubtfully brought fierce shocks to the real economic activities, financial market and public lives. Under this special condition, this study explores whether the predictability of crude oil futures information has changed before and during the COVID-19 pandemic for 19 international stock markets. From an in-sample perspective, we find that the crude oil futures RV can significantly affect future stock volatility for each equity index except SSEC. Moreover, the out-of-sample results from statistic and economic perspective reveal that crude oil futures RV is a more efficient predictor during the COVID-19 pandemic compared with the pre-crisis period. Furthermore, we find that the predictability of crude oil futures information is stronger from March to May 2020, when the epidemic is seriously prevailing. The empirical results from alternative evaluation method, recursive window method, alternative realized measures, controlling VIX and the seasonal effect, asymmetric forecasting window and different testing windows are robust and consistent. Our findings could offer novel and significant policy and practical implications.     

Immunizing markets against the pandemic: COVID-19 vaccinations and stock volatility around the world

The COVID-19 pandemic has exerted a noteworthy impact on stock market volatility around the world. Can vaccination programs revert these adverse effects? To answer this question, we scrutinize daily data from 66 countries from January 1, 2020 to April 30, 2021. We provide convincing evidence that COVID-19 vaccination assists in stabilizing the global equity markets. The drop in volatility is robust to many considerations and does not result solely from either the pandemic itself or the government policy responses—the negative correlation remains significant after controlling for these factors. The impact of vaccinations is relatively stronger within developed markets than in emerging ones.     

Constructing a positive sentiment index for COVID-19: Evidence from G20 stock markets

The present study investigates the degree of market responses through the scope of investors' sentiment during the COVID-19 pandemic across G20 markets by constructing a novel positive search volume index for COVID-19 (COVID19⁺). Our key findings, obtained using a Panel-GARCH model, indicate that an increased COVID19⁺ index suggests that investors decrease their COVID-19 related crisis sentiment by escalating their Google searches for positively associated COVID-19 related keywords. Specifically, we explore the predictive power of the newly constructed index on stock returns and volatility. According to our findings, investor sentiment positively (negatively) predicts the stock return (volatility) during the COVID-19. This is the first study assessing global sentiment by proposing a novel proxy and its impacts on the G20 equity market.     

Modelling return and conditional volatility exposures in global stock markets

This article empirically investigates the exposure of country-level conditional stock return volatilities to conditional global stock return volatility. It provides evidence that conditional stock market return volatilities have a contemporaneous association with global return volatilities. While all the countries included in the study exhibited a significant and positive relationship to global volatility, emerging market volatility exposures were considerably higher than developed market exposures. JEL Classification G12     

Analysis of risk correlations among stock markets during the COVID-19 pandemic

The outbreak of the COVID-19 pandemic significantly negatively impacted the global economy and stock markets. This paper investigates the stock-market tail risks caused by the COVID-19 pandemic and how the pandemic affects the risk correlations among the stock markets worldwide. The conditional autoregressive value at risk (CAViaR) model is used to measure the tail risks of 28 selected stock markets. Furthermore, risk correlation networks are constructed to describe the risk correlations among stock markets during different periods. Through dynamic analysis of the risk correlations, the influence of the COVID-19 pandemic on stock markets worldwide is examined quantitatively. The results show the following: (i) The COVID-19 pandemic has caused significant tail risks in stock markets in most countries, while the stock markets of a few countries have been unaffected by the pandemic. (ii) The topology of risk correlation networks has become denser during the COVID-19 pandemic. The impact of the COVID-19 pandemic makes it easier for risk to transfer among stock markets. (iii) The increase in the closeness of the risk relationship between countries with lower economic correlation has become much higher than that between counties with higher economic correlation during the COVID-19 pandemic. For researchers and policy-makers, these findings reveal practical implications of the risk correlations among stock markets.     

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 asymmetric relationship between returns and implied volatility: Evidence from global stock markets

We investigate the asymmetric relationship between returns and implied volatility for 20 developed and emerging international markets. In particular we examine how the sign and size of return innovations affect the expectations of daily changes in volatility. Our empirical findings indicate that the conditional contemporaneous return-volatility relationship varies not only based on the sign of the expected returns but also upon their magnitude, according to recent results from the behavioral finance literature. We find evidence of an asymmetric and reverse return-volatility relationship in many advanced, Asian, Latin-American, European and South African markets. We show that the US market displays the highest reaction to price falls, Asian markets present the lowest sensitivity to volatility expectations, while the Euro area is characterized by a homogeneous response both in terms of direction and impact. These results may be safely attributed to cultural and societal characteristics. An extensive quantile regression analysis demonstrates that the detected asymmetric pattern varies particularly across the extreme distribution tails i.e., in the highest/lowest quantile ranges. Indeed, the classical feedback and leverage hypotheses appear not plausible, whilst behavioral theories emerge as the new paradigm in real-world applications.     

Effect of twitter investor engagement on cryptocurrencies during the COVID-19 pandemic

This study aims to examine whether the prices and returns of two cryptocurrencies, Dogecoin and Ethereum, are affected by Twitter engagement following the COVID-19 pandemic. We use the autoregressive integrated moving average with explanatory variables model to integrate the effects of investor attention and engagement on Dogecoin and Ethereum returns using data from December 31, 2020, to May 12, 2021. The results provide evidence supporting the hypothesis of a strong effect of Twitter investor engagement on Dogecoin returns; however, no potential impact is identified for Ethereum. These findings add to the growing evidence regarding the effect of social media on the cryptocurrency market and have useful implications for investors and corporate investment managers concerning investment decisions and trading strategies.     

Research on stock volatility risk and investor sentiment contagion from the perspective of multi-layer dynamic network

In the post-epidemic period, the international economic structure has been readjusted, with risks contagious across financial and economic systems. This paper primarily uses the high-frequency TENET network and the Granger-causality network to describe the interconnectedness between the tail risk of stock volatility and investor sentiment, then the two-layer network is constructed by the generalized variance decomposition method to examine the inter-layer connectedness. Based on the two-layer network, the heterogeneity frequency response of network connectedness and dynamic network structure are further analyzed from the perspective of frequency domain. The study found that the tail risk of high-frequency stock volatility displays industry heterogeneity and time-varying property, and investor sentiment contagion network provides information transmission medium for stock risk. The double-layer network study found that stock volatility in consumer goods industry exhibits higher risk spillover to investor sentiment. The diversified financial industry, real estate industry and energy industry in the two-layer network are systemically important industries. In addition, the study of the frequency domain dynamic network found that the connectedness volatility in the short-term risk network of stock volatility was significantly higher than that of the investor sentiment network, and the short-term risk spillover effect of the network played a leading role in the total risk spillover. The research conclusions provide reference for preventing systemic risks from the perspective of systemically important industries and cyclical fluctuations.     

The impact of Covid-19 on G7 stock markets volatility: Evidence from a ST-HAR model

We investigate the impact of Covid-19 on stock markets across G7 countries and their business sectors. We highlight the synchronicity and severity of this unprecedented crisis. We find strong transition evidence to a crisis regime in all countries and sectors, yet crisis intensity and timings vary. The Health Care and Consumer services sectors were the most severely affected; a reflection of the Covid-19 drug-race and international travel restrictions. The Technology sector was hit the latest and least severely, as imposed lockdown measures forced people to explore various web-based entertainment and distraction options. Country-wise the UK and the US were the most affected with the highest heterogeneity in their business sectors' response; a possible reflection of the ambiguity in the initial response and adoption of lockdown measures. Financial markets' response to Covid-19 is akin to response in previous financial crisis rather than previous pandemics. A series of robustness checks confirms our findings.     

Corporate governance and the information environment: Evidence from Chinese stock markets

This article explores the relationship between corporate governance and the information environment in Chinese stock markets. We construct a parsimonious governance measure for public firms using a 2003 through 2011 sample period. We use four indicators to proxy for the information environment: analyst following, analyst forecast accuracy, analyst forecast dispersion, and price timeliness. We find that better governed firms tend to be associated with larger analyst followings and more informative forecasts. We also find that better governed firms tend to improve on the timeliness of bad news relative to good news.     

Pandemic-driven financial contagion and investor behavior: Evidence from the COVID-19

This paper studies the pandemic-driven financial contagion during the COVID-19 period and the impact of investor behavior on it by constructing three types of direct behavior measurements based on Google search volumes. More specifically, using a sample of 26 major stock markets around the world during the COVID-19 pandemic, we construct a non-linear financial contagion network via a dynamic mixture copula-EVT (extreme value theory) model to quantitatively detect and measure the complex nature of pandemic-driven financial contagion. Furthermore, through constructing direct investor behavior measurements including investor attention, sentiment, and fear, we find investor behavior plays an important role in explaining pandemic-driven financial contagion. We also find that the impacts of investor behavior on the pandemic-driven financial contagion are heterogeneous under several different settings, including market conditions, market development levels, regional subsets, and contagion directions.     

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.     

Internal capital markets,corporate investment,and the COVID-19 pandemic: Evidence from Korean business groups

This paper examines whether the investment of Korean business group (“chaebol”) affiliated firms behaved differently from that of non-chaebol firms in response to the COVID-19 outbreak. I show that chaebol firms cut back investment to a lesser degree than similar non-chaebol firms. Chaebol firms with higher-than-industry-median market-to-book ratios invested more and experienced less decline in their stock prices, while I do not find such relationships for non-chaebol firms. This paper provides evidence that chaebol internal capital markets helped mitigate the negative effects of the pandemic on firm investment and value.     

The return volatility of cryptocurrencies during the COVID-19 pandemic: Assessing the news effect

In this paper, we test the role of news in the predictability of return volatility of digital currency market during the COVID-19 pandemic. We use hourly data for cryptocurrencies and daily data for the news indicator, thus, the GARCH MIDAS framework which allows for mixed data frequencies is adopted. We validate the presupposition that fear-induced news triggered by the COVID-19 pandemic increases the return volatilities of the cryptocurrencies compared with the period before the pandemic. We also establish that the predictive model that incorporates the news effects forecasts the return volatility better than the benchmark (historical average)model.     

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.     

Evidence of oil market price clustering during the COVID-19 pandemic

We use hourly data on opening price, closing price, opening ask price, opening bid price, closing ask price and closing bid price to show that while oil prices are characterized by price clustering behavior, prices tend to cluster on numbers closer to zero than to one. Comparing the pre-COVID-19 sample with the COVID-19 sample, we find that evidence of price clustering is 8% more in the COVID-19 sample. We test the determinants of price clustering and find that as much as 30% of the price clustering behavior can be attributed to the COVID-19 pandemic. Finally, using a simple technical trading strategy, we do not find any evidence that the oil market is profitable in the COVID-19 period.     

Empirical relationship between macroeconomic volatility and stock returns: Evidence from Latin American markets

Emerging market stock returns have been characterized as having higher volatility than returns in the more developed markets. But previous studies give little attention to the fundamentals driving the reported levels of volatility. This paper investigates whether dynamics in key macroeconomic indicators like exchange rates, interest rates, industrial production and money supply in four Latin American countries significantly explain market returns. The MSCI world index and the U.S. 3-month T-bill yield are also included to proxy the effects of global variables. Using a six-variable vector autoregressive (VAR) model, the study finds that the global factors are consistently significant in explaining returns in all the markets. The country variables are found to impact the markets at varying significance and magnitudes. These findings may have important implications for decision-making by investors and national policymakers.     

Directional spillover effects and time-frequency nexus between oil,gold and stock markets: Evidence from pre and during COVID-19 outbreak

The Covid-19 crisis has been spread rapidly throughout the world so far. However, how deep and long the turbulence would depend on the success of solutions taken to deter the spread of Covid-19, the impacts of government policies may be prominent to alleviate the current crisis. In this article, we investigate the spillover effects and time-frequency connectedness between S&P 500, crude oil prices, and gold asset using both the spillover index of Diebold and Yilmaz (2012) and the wavelet coherence to evaluate whether the time-varying dynamic return spillover index exhibited the intensity and direction of transmission during the Covid-19 outbreak. Overall, the present results shed light on that in comparison with the pre-Covid-19 period, and the return transmissions are more apparent during the Covid-19 crisis. More importantly, there exist significant dependent patterns about the information spillovers among the crude oil, S&P 500, and gold markets might provide significant implications for portfolio managers, investors, and government agencies.