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Herding behaviour has been shown to significantly influence market performance over the years, often leading to mispricing of assets due to asset bubbles. This study aimed to analyse herding behaviour on the Johannesburg Stock Exchange (JSE) before, during and after COVID-19. A cross-sectional absolute deviation (CSAD) model was used to determine the presence of herding during the specified periods, from the 1st of January 2018 to the 31st of January 2024, while causes and implications were explored further to offer a better understanding of herding behaviour. Herding behaviour was detected in all observed periods, which was not anticipated, which raised the need to discuss the policies in place to curb irrational investor behaviour. The evidence suggests that portfolio managers should understand that herding behaviour can cause market movements that are not necessarily rational or based on fundamentals. Instead, the results indicate that prices can be influenced by collective sentiment. As such, portfolio managers can assess the risks associated with blindly following the crowd and formulate strategies to avoid it. Portfolio managers can also educate their clients on the risks associated with herding behaviour to set realistic expectations and prioritise long-term stability over short-term trends.

This study provides a contemporary analysis of the dynamic interplay between persistent trends and short-term stochastic movements in international financial markets, challenging the traditional view of an efficient market. Through a quantitative examination of the S&P 500, FTSE 100, Nikkei 225, DAX, and CAC 40 using daily closing prices from 2000 to 2023, the research employs a 50-day SMA to identify trends and a 14-day stochastic oscillator to gauge whether some financial markets are overbought or oversold. The findings reveal distinct, regime-dependent market behaviors across different economies, with strong uptrends in the United States and Germany contrasting with weaker trends in the United Kingdom and France and a downtrend in Japan. The results empirically demonstrate that a trend following strategy, which goes long during uptrends and remains neutral during downtrends, consistently enhances risk adjusted returns and preserves capital compared to a static buy and hold approach. Furthermore, correlation analysis emphasis that effective global diversification requires an understanding of synchronous market regimes, not just holding different assets. The findings of this study suggest that disciplined, regime aware framework, integrating both trend and momentum signals, is essential for robust global asset allocation in today's complex financial landscape making it a noteworthy contribution.

The increasing inter relationship between financial markets have led to the discovery of new risk dynamics particularly when considering downside risk. Recent advances in econometric modelling have prompted the search for new insights into the intertemporal dynamics between downside risks and expected stock returns. Therefore, the aim of this study was to investigate this relationship in the S&P 500, the FTSE 100, the DAX, the Nikkei 225, and the TSX Composite, covering the period from January 2000 to December 2023. Using a time-varying transition probability model, the findings revealed consistent negative correlations between downside risk measures and expected returns across all markets, with CVaR exhibiting stronger inverse relationships compared to VaR. These findings challenge the classical risk-return trade-off but align with behavioural explanations where heightened risk aversion during downturns suppresses returns. The regime-switching analysis further uncovers asymmetry in transition probabilities where high-risk regimes persist with a 74% probability, while low-risk regimes show greater stability. By implication, this study provides empirical support for regulatory frameworks prioritizing tail risk mitigation, particularly in volatile markets.

Market anomaly is an occurring phenomenon in the market. Supposedly, an anomaly does not exist in markets that are considered efficient. An anomaly is an aberration in the efficient market theory where existing information does not reflect stock prices; therefore, investors can earn abnormal returns. This study examines how The Day Of The Week Effect and The Month Of The Year Effect affect stock returns on the Indonesian Stock Price Index and the Global Stock Price Index. Samples in this study are daily stock return data and return data on stocks of IHSG, DJIA, SSEC, and N225. The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model is used to analyze data. The results show that in IHSG and SSEC, there was no The Day Of The Week Effect. The DJIA and N225 were found in The Day of the Week Effect. The Month of the Year Effect was found in IHSG, DJIA, SSEC, and N225.