Leveraging an Integrated First and Second Moments Modeling Approach for Optimal Trading Strategies: Evidence From the Indian Pharma Sector in the Pre‐ and Post‐COVID‐19 Era

ABSTRACT The current research presents a novel approach that integrates the first‐moment (mean) and second‐moment (variance) components of stock price dynamics to forecast future price trends. Employing a combination of statistical and deep learning models, the study aims to predict both the mean and variance of stock price movements for select pharmaceutical companies in India based on their market capitalization. The forecasts are then utilized to assess the effectiveness of the Bollinger Band (BB) trading strategy in terms of hit ratio and average returns per trade. The study covers both pre‐ and post‐COVID periods. The results indicate that the integrated mean and volatility model employed in this study outperforms the stand‐alone mean and volatility models when back‐tested with BB trading strategies, leading to higher returns. Moreover, when combined with a volatility model, the integrated deep learning model consistently demonstrates superior performance compared with the standalone mean or volatility model. The integrated model has yielded significantly higher annualized average returns (> 200%) than the returns generated based on technical indicators, as suggested by existing studies. These findings have significant practical implications, providing investors and traders with an advanced alternative to conventional trading methods.