Efficiency analysis of the meta-heuristic algorithms in portfolio optimization

Subject Areas : Financial Knowledge of Securities Analysis

Sina  Shirtavani ¹ (PhD Candidate, Department of Industrial Management, Bandar Anzali International Branch, Islamic Azad University, Bandar Anzali, Iran.)
Mehdi  Homayonfar ² (Assistant professor, Industrial management group, Rasht unit, Islamic Azad University, Rasht, Iran)
Keyhan  Azadi ³ (Department of Accounting, Faculty of Management and Accounting, Islamic Azad University, Rasht unit, Rasht, Iran)
amir  daneshvar ⁴ (Department of Entrepreneurship and Business Management, Faculty of Management, Electronic Branch, Islamic Azad University, Tehran,)

Received: 2022-09-21 Accepted : 2023-01-11 Published : 2024-03-11

Keywords: Optimization, Stock Portfolio, Meta-heuristic Algorithms, Genetic Algorithm, Ant colony Algorithm,

Abstract :

The most important goal of every investor in the stock market is to increase returns and reduce investment risk. Therefore, the purpose of this research is to analyze the effectiveness of meta-heuristic algorithms in stock portfolio optimization. Considering that in this research, the past performance of Tehran Stock Exchange companies is examined in past studies from 1390-1399, therefore, in terms of the research design, this research was post-event using Delphi and meta-analysis techniques. The statistical community of this research Academic researchers in the field of finance and active in the Tehran Stock Exchange, and the sampling method in this research was targeted with a volume of 30 people. The data collection tool was a researcher-made questionnaire. The method of collecting information was structured interview of researchers and review of the results of various studies in the field of determining the optimal stock portfolio in Tehran Stock Exchange. In order to analyze the data, Spss software version 23 and Laserl version 5.7 were used. The results showed that among meta-heuristic algorithms of genetic algorithm, ant colony and bee colony are the most suitable tools with the aim of not stopping at local optimal points and not premature convergence. Finally, after evaluating the appropriate algorithms, a comparison of the average risk and returns of the stock portfolio in genetic algorithms, ant colony and bee colony was done in the study unit, they showed that in terms of the criteria of reducing the risk of genetic and bee algorithms and in terms of increasing the return of the optimal portfolio Stock bee algorithm has worked more efficiently.

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