A comprehensive model to support investment decisions based on deep learning and evolutionary algorithms

Abstract

This paper presents and assesses a comprehensive approach to stock price forecasting and portfolio selection that integrates advanced computational intelligence  techniques with fundamental and technical analyses. Several outstanding forecasting methods are compared to identify the most accurate model. Subsequently, differential evolution is used to optimize a stock portfolio, leveraging the results of the selected forecasting method along with key technical and fundamental indicators. Experiments show that the proposed method consistently yields higher returns and better risk management than several benchmarks. Statistical validation confirms the model’s superior performance, highlighting its potential as a robust tool for optimizing investment portfolios.