A Comprehensive Soft Computing-Based Approach to Portfolio Management by Discarding Undesirable Stocks

Abstract

Stock markets play a crucial role in modern society. Many individuals and organizations try to improve their performance in these markets by exploiting approaches that consider different types of factors. However, although there are great complexities involved, practitioners often use simplistic methodologies that neglect relevant features of the problem. That is, they leave out some activities that are essential to obtain better results in the related decision-making problem. Here, we propose to use a soft computing-based approach to comprehensively address the main activities of stock investments. We present a novel method for modeling expert knowledge through fuzzy logic that allows the investor to discard undesirable stocks (i.e., stocks that are not suitable for investment); thus, reducing computational complexity in the search process and likely improving the performance of the final stock portfolio. Extensive experiments allowed us to conclude that discarding undesirable stocks by exploiting the proposed method produces portfolios that outperform the benchmarks. Therefore, the proposal is a promising alternative to complement current approaches.

Keywords

• Stock portfolio
• fuzzy logic
• artificial neural network
• evolutionary algorithm
• computational intelligence