A Multi-objective Genetic Model for Stock Selection

Abstract

Stock selection has long been recognized as a challenging and important task in finance. Recent advances in machine learning and data mining are leading to significant opportunities to solve these problems more effectively. In this study, we enrich our previous work for stock selection using single-objective genetic algorithms (SOGA) by extending it to the multi- objective GA (MOGA). In our previous work, we devised a stock scoring mechanism to rank and select stocks to form a portfolio, and we employed the SOGA for optimization of model parameters and feature selection for input variables to the model. In this work, we show how our MOGA models outperform the benchmark and improve upon our previous SOGA- based methods. Based on the promising results, we expect this MOGA methodology to advance the current state of research in soft computing for the real-world stock selection applications.