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Multi-objective stock market portfolio selection using multi-stage stochastic programming with a harmony search algorithm

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Abstract

The problem of stock portfolio selection is one of the most critical problems in financial markets. The portfolio selection problem is to find an optimal solution to allocate a fixed amount of capital to a set of available stocks with the objective function of having the maximum expected rate of return and, at the same time, the least possible risk. Among the shortcomings that most stock portfolio models have is not paying attention to future changes and focusing too much on past information. This study aims to provide a framework that addresses some shortcomings and provides a practical tool. In this regard, this study suggests a multi-objective and multi-stage stochastic model for portfolio selection in the financial market. The stage refers to the periods in which the stock portfolio will be reviewed. By combining the scenario generation model with multi-stage stochastic programming, the investor is expected to achieve a suitable solution based on past information and various future scenarios. To solve the proposed model, a meta-heuristic algorithm whose main idea is derived from the harmony search algorithm is proposed. Finally, numerical instances were utilized by the use of real stock information from the Iranian stocks market. The algorithm proposed in this research was compared with a genetic algorithm in terms of the quality of the generated solutions and the runtime of the algorithms, and the superiority of the proposed algorithm has been proven.

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Authors and Affiliations

  1. Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran

    H. Asgari & J. Behnamian

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  1. H. Asgari
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Asgari, H., Behnamian, J. Multi-objective stock market portfolio selection using multi-stage stochastic programming with a harmony search algorithm. Neural Comput & Applic 34, 22257–22274 (2022). https://doi.org/10.1007/s00521-022-07686-4

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