The study uses mean–variance modelling to examine market anomalies and test the existence of the market in the Indian stock market. The analysis includes leading firms by market capitalization across sectors, focusing on daily log returns and excess returns over short-, medium- and long-term horizons. A novel integrated model, combining mean and variance properties through both mean modelling and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) estimation, is applied to capture risk-adjusted market anomaly effects and their implications for investors. The findings highlight persistent and statistically significant anomalies that question the validity of market efficiency. These anomalies are most pronounced during periods of heightened market volatility caused by global events, such as the COVID-19 pandemic, the Russia–Ukraine conflict and other geopolitical crises, challenging the validity of the market availability. Negative ‘Monday’ and positive ‘Tuesday’ effects are consistently observed across all stocks, with variations in their magnitude and direction after applying risk adjustments. Compared to mean modelling, the integrated approach reveals volatility patterns and yields higher or lower risk-adjusted returns. The study demonstrates how to capitalize on positive returns from the market anomalies while managing risks.
Sharma et al. (Fri,) studied this question.
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