Evaluating the Effectiveness of Machine Learning Models in Predicting Stock Returns: A Comparative Analysis of Traditional and Advanced Techniques

This study examines the effectiveness of machine learning (ML) models in predicting stock returns, comparing them to traditional approaches like Linear Regression. Using data from Dow Jones Industrial Average stocks over a recent five-year period, the research implements Linear Regression, Random Forest, and Neural Network models. The study finds that ML models, especially Neural Networks, consistently outperform traditional methods across various metrics including predictive accuracy, directional accuracy, and risk-adjusted returns. The analysis reveals the importance of recent price movements and technical indicators in predictions. While ML models show significant potential for enhancing stock return forecasting, the research also highlights challenges such as higher computational requirements and the persistent unpredictability in financial markets, as even the best model explained only a fraction of return variance. The study suggests further research into advanced architectures and alternative data sources to improve predictive performance. Overall, the findings indicate promising avenues for integrating ML techniques with traditional financial theory to enhance investment strategies, while acknowledging the ongoing challenges in this complex field. Keywords: Machine Learning, Stock Return Prediction, Neural Networks, Random Forest, Linear Regression.