Optimizing financial modeling with machine learning: integrating particle swarm optimization for enhanced predictive analytics

This study aims to improve the precision of financial modeling in dynamic stock and foreign exchange markets by incorporating Particle Swarm Optimization (PSO) with essential Machine Learning (ML) models. The goal is to achieve a balanced and precise relationship between the complexity of the model and the quality of its fit, highlighting the advantageous effects of advanced optimization techniques in this specific situation. The empirical testing of pivotal models, namely Support Vector Machine (SVM), Decision Tree, and Random Forest, is conducted within a comprehensive framework incorporating PSO. The evaluation process aims to measure the impact of PSO on accuracy metrics, specifically by analyzing the observed changes in Akaike Information Criterion (AIC) values before and after the implementation of PSO. This approach demonstrates the effectiveness of optimization in achieving a balance between the model’s complexity and the fit’s accuracy. Combining PSO with ML models improves forecast accuracy for the overall monthly volume of stocks and foreign exchange markets. The significant reduction in AIC values after optimization highlights the concrete advantages of optimization techniques, confirming their effectiveness in attaining an optimized balance. This research uniquely contributes by creatively combining advanced optimization techniques with ML to develop financial models.