Can AI models Outperform the Traditional Buy-and-Hold Strategy?

Abstract

The efficient market theory suggests that the 'buy and hold' (B&H) strategy is optimal due to its simplicity and lower costs. Artificial intelligence (AI) models, including machine learning (ML) is now a common research tool in financial applications. However, advancements in AI and machine learning offer the potential to outperform B&H. This paper investigates AI models applied to the Nifty 50 index, demonstrating that an AI-based strategy can surpass the performance of the traditional B&H approach. We compare various machine learning classifiers, such as LightGBM, KNN, XGB, SVC, and Random Forest, to evaluate their effectiveness. Additionally, we dive into the feature engineering process, converting a core set of financial indicators into comprehensive datasets for model training. A detailed Profit and Loss (PnL) comparison is conducted, revealing that our AI-based strategy not only outperforms the B&H strategy in general but also shows resilience during volatile market conditions. The findings highlights the significant potential of AI in developing superior trading strategies, offering insights into its practical applications in financial markets.