Machine Learning Based Sector Rotation in the Indian Equity Market: A Risk-Adjusted Evaluation

This study examines whether a machine learning driven sector rotation strategy is capable of generating statistically significant abnormal returns in the Indian equity market after rigorous risk adjustment. Using daily return data for six major Indian sectoral indices and the NIFTY 500 benchmark over the period February 2011 to December 2025, a Random Forest regression framework is implemented to forecast five-day forward cumulative returns. Performance is assessed using annualized return, volatility, Sharpe ratio, maximum drawdown, and a CAPM regression with Newey-West heteroskedasticity and autocorrelation consistent standard errors. Although the strategy delivers marginally higher raw returns than the benchmark, risk-adjusted analysis reveals no statistically significant alpha. The results indicate that the observed performance is largely attributable to systematic market exposure rather than independent abnormal return generation.