Comparative analysis between genetic programming and machine learning algorithms in forecasting financial trends

Abstract

This study presents a comparative analysis of Genetic Programming (GP) and five machine learning (ML) algorithms, namely Support Vector Machines (SVM), AdaBoost, XGBoost, Long Short-Term Memory (LSTM), and Deep Neural Networks (DNN), in the task of financial trend forecasting. We use historical daily data from the NASDAQ, S&P 500, and Nikkei 225 indices, covering the period from January 2015 to January 2025. Model performance is evaluated using Sharpe and Sortino Ratios, capturing both accuracy and risk-adjusted return. Results show that GP exhibits greater stability in Asian markets, while LSTM and XGBoost achieve better performance in North American markets.