This paper examines the impact of the Monetary Policy Uncertainty (MPU) of the United States on Asian developed, emerging, and frontier stock markets using a Quantile-on-Quantile (QQR) approach by using monthly data from January 2006 to December 2022 of 14 Asian countries. The study finds that US monetary policy significantly negatively influences Asian stock markets. This is primarily due to the widespread use of the US dollar as a universal currency, resulting in substantial ripple effects on other nations through trade relationships. In Asian developed markets, MPU is negatively related to Australia and New Zealand. At the same time, it has a positive relationship with Hong Kong and Japan at the upper quantiles. Among Asian emerging markets, MPU negatively impacts Taiwan\'s, India\'s, and China\'s returns, increasing this negative relationship at higher MPU quantiles. Additionally, MPU has a significant negative relationship with Thailand, Indonesia, Korea, and Malaysia returns. In contrast, higher quantiles of MPU have no discernible impact on the Philippines stock returns. In Asian frontier markets, MPU negatively impacts Pakistan\'s and Sri Lanka\'s returns. The implications of these findings are twofold: for investors, this study provides valuable insights for hedging activities, allowing for more informed decisions based on the MPU of other countries to identify profitable stocks. For policymakers, this research aids in formulating effective monetary policy strategies. Furthermore, future studies can build upon these results by exploring other markets and comparing their outcomes with the findings presented in this study.

We construct a pandemic-induced fear (PIF) index to measure fear of the COVID-19 pandemic using Internet search volumes of the Chinese local search engine and empirically investigate the impact of fear of the pandemic on Chinese stock market returns. A reduced-bias estimation approach for multivariate regression is employed to address the issue of small-sample bias. We find that the PIF index has a negative and significant impact on cumulative stock market returns. The impact of PIF is persistent, which can be explained by mispricing from investors\' excessive pessimism. We further reveal that the PIF index directly predicts stock market returns through noise trading. Investors\' Internet search behaviors enhance the fear of the pandemic, and pandemic-induced fear determines future stock market returns, rather than the number of cases and deaths caused by the COVID-19 pandemic.

The primary source of investor interest that disrupts the financial markets is news that reflects the macroeconomy. This study intends to track changes in investors\' positive and negative market attention and their effects on stock market returns by examining the print media portrayal of the China-Pakistan economic corridor (CPEC). We access the daily and weekly coverage of the CPEC by national and international newspapers from the Bloomberg database over the period from January 2015 to December 2019. Using the Harvard psychological dictionary, we categorize the news headlines into positive and negative news sentiments. We then relate the news sentiment to the stock market returns, using quintile analysis, ordinary least squares (OLS), and vector autoregressive (VAR) models. The results show that investors react quickly and significantly to positive news. They pay more for the same stock if the positive news stream increases; hence, the stock market return also increases. In contrast, investors do not react with the same passion to an increase in negative news. These findings are in line with the theoretical rationale of the disposition effect. These outcomes may be useful for active investors and practitioners to devise investment strategies in the presence of the hype surrounding the CPEC in the print media.

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.

In this paper, we utilise daily stock returns for the Ghanaian equity market (GSE) to examine the influence of the COVID-19 pandemic on market volatility. We take return volatility from 2nd January, 2018, to 31st December, 2021, and split it into two periods-the pre-COVID-19 period and the COVID-19 period. Utilising the exponential GARCH (EGARCH) model, we discovered leverage effects in all observed periods. Additionally, the research indicates that the COVID-19 period experienced high volatility with a transient volatility persistence. Furthermore, during the COVID-19 pandemic, positive shocks had a more significant impact on the volatility of the GSE\'s returns than negative news of comparable magnitude.

COVID-19 unexpectedly ensnared the entire world and wreaked havoc on global economic and financial systems. The stock market is sensitive to black swan events, and the COVID-19 disaster was no exception. Against this backdrop, this study explores the impact of COVID-19 and economic policy uncertainty (EPU) on Chinese stock markets\' returns for the period spanning January 23, 2020 to August 04, 2021. The outcomes of the novel quantile-on-quantile regression analysis revealed that both COVID-19 and EPU had a significant negative impact on both Shanghai and Shenzhen stock market returns, while COVID-19 aggravated the level of economic uncertainty in both financial markets. The quantile causality approach of Troster et al. (2018) validates our main estimations. We conclude that COVID-19 and a high level of EPU enervated the returns of China\'s leading stock markets. Our study provides key insights to policymakers and market participants to determine the behavior of China\'s stock market returns vis-à-vis COVID-19 during the peak of the pandemic and beyond. Specifically, our findings apprise portfolio investors to augment their portfolio diversification fronts.

This study examines the relationship between positive and negative investor sentiments and stock market returns and volatility in Group of 20 countries using various methods, including panel regression with fixed effects, panel quantile regressions, a panel vector autoregression (PVAR) model, and country-specific regressions. We proxy for negative and positive investor sentiments using the Google Search Volume Index for terms related to the coronavirus disease (COVID-19) and COVID-19 vaccine, respectively. Using weekly data from March 2020 to May 2021, we document significant relationships between positive and negative investor sentiments and stock market returns and volatility. Specifically, an increase in positive investor sentiment leads to an increase in stock returns while negative investor sentiment decreases stock returns at lower quantiles. The effect of investor sentiment on volatility is consistent across the distribution: negative sentiment increases volatility, whereas positive sentiment reduces volatility. These results are robust as they are corroborated by Granger causality tests and a PVAR model. The findings may have portfolio implications as they indicate that proxies for positive and negative investor sentiments seem to be good predictors of stock returns and volatility during the pandemic.

This paper investigates the Chinese stock market reactions to the announcements of Covid-19 vaccine approvals. These announcements generally impacted stock prices, but the impacts appeared to be heterogeneous across sectors. Particularly, firms in the manufacturing, wholesale, retail, and information technology sectors were persistently benefited. We also find that firms with poorer performance, smaller sizes, and greater ages reacted more positively compared to others.

The effect of COVID-induced public anxiety on stock markets, particularly in European stock market returns, is examined in this research. The search volumes for the notion of COVID-19 gathered by Google Trends and Wikipedia were used as proxies for COVID-induced public anxiety. COVID-induced public anxiety was shown to be linked with negative returns in European stock markets when a panel data method was used to a sample of data from 14 European stock markets from January 2, 2020 to September 17, 2020. Using an automated trading system, we used this finding to suggest investment methods based on COVID-induced anxiety. The findings of back-testing indicate that these techniques have the potential to generate exceptional profits. These results have significant consequences for government officials, the media, and investors.

The current research paper identifies the current dynamics in the oil price-stock market nexus to provide a research overview and suggest further research directions. We used bibliometrix R package to examine 684 studies to identify research trends in oil price shocks, stock market returns, and volatility spillover effects. We recognize the most influential authors, publications, and research institutions and their significance within the current scientific literature. We further analyzed research themes to observe impediments in the existing literature and suggest new research directions to summarize that disaggregated sectoral analysis and meta-analysis approach by including moderator analysis will broaden the research contribution in the future. Lastly, we conclude our investigation by identifying new research avenues.