Enhancing investment strategies through machine learning: A comprehensive analysis across market sectors

Abstract

Abstract. This study presents a detailed exploration of the application of machine learning (ML) models to optimize investment strategies across various market sectors, including equity markets, fixed income and derivatives, and the volatile cryptocurrency markets. We evaluate three primary ML models: linear regression, decision trees, and neural networks, based on their predictive accuracy, computational efficiency, and robustness to market volatility. A rigorous process involving backtesting and cross-validation assesses each model's performance. Our framework encompasses data preprocessing, feature engineering, model implementation, and a nuanced approach to risk assessment integrating Value at Risk (VaR) and the Sharpe Ratio. We demonstrate the models' effectiveness in predicting stock prices, interest rates, and cryptocurrency price movements. The application of our ML framework led to the development of dynamic portfolio optimization strategies that significantly outperform traditional methods. This study contributes to the understanding of ML's potential to revolutionize investment strategies, providing a foundation for future research and practical applications in financial markets.