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This study investigates how the Global Financial Crisis has affected the weak-form Efficient Market Hypothesis (EMH) on the stock prices of sixteen nations throughout the globe based on a suite of Fourier unit root tests. Considering the smooth structural breaks, we employed the Fourier-based unit root tests to assess the weak-form efficient market hypothesis. We used multiple frequency datasets of global financial stock market indexes that span over 20 years to have comprehensive analysis and robustness in the results. The study is performed from distinct sub-sample periods of the global financial crisis, including the pre-crisis period (2000–2007), the crisis and post-crisis period (2008–2020), and the overall sample period (2000–2020). We observed seven stock markets in the total sample period and twelve in the pre-crisis period, which were weak-form efficient across different frequency data sets. During the crisis and post-crisis period, just four out of sixteen stock market indexes were found to be weak in efficiency based on Fourier unit root tests. Given the superior properties of the Fourier unit root tests, this study reiterates that investors may receive a stream of arbitrage benefits in all markets due to the inefficiency of these countries. We offer investment implications that enable forecasting future stock price changes based on past performance and creating trading methods that produce anomalous profits.

The article extends the empirical literature on overconfidence bias in G7 stock markets during pre- and post-COVID-19 and provides additional evidence. Using vector autoregression and impulse response functions (IRFs), we analyze the overconfidence bias for the daily data from January 2015 to December 2021. Because the pertinent coefficients are positive and highly significant for only a few lags, there is a strong contemporaneity between market volume and market return in the pre-COVID-19 period of the Canadian and Italian stock markets. The study shows compelling evidence of overconfident behavior in the Italian market during the COVID-19 crisis. Along with trading volume, market liquidity influences overconfidence bias, which tracks market return but not vice versa. For investors, decision-makers, and market regulators, the study has significant ramifications in the current market turbulence caused by the COVID-19 pandemic. Furthermore, overconfidence contributes to the reported extra unpredictability due to the high level of sensitive data.

AbstractThe study uses wavelet power spectrum and wavelet coherence transformation methodologies to examine how geopolitical risk affected the returns on stocks, oil, and gold during the GFC, COVID-19, and Russia-Ukraine war-three disruptive events that affected the world’s financial markets. For better diversification benefits during the turbulent times, we further investigate the degree of co-movement in frequency and time domains. We observe that GPR has high variations during Russia-Ukraine war period compared to COVID-19 period and is shown to have least variation during the GFC period. WTI crude oil and DJGI indexes are observed to have high variations during GFC, and COVID-19 periods followed by Russia-Ukraine war. We further observe that GOLD offers better diversification opportunity as well as leading movement against WTI and DJGI during disruptive events in financial markets. The results provide new understanding of how geopolitical risk affects financial assets for international investors, fund managers, and regulators, which would further aid to find risky and safer haven possibilities during the turmoil periods.

This study examines the influence of Oil Price Volatility on Banks efficiency within the BRICS countries (Brazil, Russia, India, China, and South Africa) noting the importance of the banking sector efficiency as a tool to ensure financial stability in the region. Being able to measure efficiency levels in banks determines how successful a bank is in managing its operations and achieving its goals. A sample data of 112 banks was selected using the Bank Scope database over the time interval 2003 to 2018 to inspect banking sector relative efficiency following a non-parametric methodology known as Data Envelopment Analysis (DEA). The paper applies a two-stage model to process the empirical results consisting in using the Data Envelopment Analysis (DEA) to identify the scores of banks efficiency at a first stage (Stage 1) and determining how volatility in Oil price has impact on these scores of efficiencies on a second stage (Stage 2). Findings of the study indicate that the Chinese banking system shows the highest efficiency (90%), followed by the South Africa (87%), followed by the Brazilian and Indian banking system with efficiency level of (77%), the Russian bank industry revealed the lowest efficient banking system with level of efficiency (50%).

The study examines the vital connection between stock returns and oil price changes for oil exporting/importing countries separately. We present evidence employing granger causality, impulse response and error variance decomposition based on panel vector autoregression. The results of panel granger causality suggested that after oil price crash owing to covid-19 pandemic, the interdependence between oil and stock price changes increased. Similar results were revealed by impulse response graphs and forecast error variance decomposition. Specifically, in the period marked by the rapid outbreak of the covid-19 pandemic, causality from oil to stocks increased. Although we found that both oil exporting and oil importing countries were affected in a similar way, oil price changes had a larger impact on oil exporting countries. The findings of the present study have implications for investors and fund managers. By incorporating crude oil price in the prediction models, the accuracy of stock returns forecast can be improved.