Performance Comparison of Non-Dominated Sorting Genetic Algorithm with strength Pareto evolutionary algorithm in Selecting Optimal Portfolios in Tehran Stock Exchange

Document Type : Research Paper


1 Assistant Prof., Department of Business Management, Faculty of Economic and Management, Semnan University, Semnan, Iran.

2 MSc., Department of Business Management, Faculty of Economic and Management, Semnan University, Semnan, Iran.



Objective: One of the most important issues for all investors, including individual and institutional investors in the stock market, is finding the optimal portfolio. Identifying the optimal portfolio in the stock market can be considered a two-objective optimization problem. This problem maximizes and minimizes the return and risk of the portfolio, respectively. Like other multi-objective optimization problems, the portfolio optimization problem can be solved by multi-objective evolutionary algorithms (MOEAs). Accordingly, the Non-dominated Sorting Genetic Algorithm ( ) and Strength Pareto Evolutionary Algorithm ( ), as the two advanced algorithms of multi-objective evolution algorithms, can be used to solve this problem. These algorithms identify the optimal solution by ranking and archiving solutions located on the Pareto frontier. The purpose of this research is to compare the performance of  and  in mean-variance and mean-semi-variance approaches to identify the optimal stock portfolio.
Methods: This research investigated 241 stocks enlisted on the Tehran Stock Exchange (TSE). It was conducted within 174 months from September 2006 to March 2019. The researchers first identified the optimal portfolio using NSGAII and SPEA2 algorithms through two approaches including mean-variance and mean-semi-variance. Then, by conducting a statistical hypothesis test on the average Sharp ratio of extracted portfolios, the performance of NSGAII and SPEA2 algorithms were compared. To confirm the research findings, a robustness test was done by comparing the performance of the SPEA2 algorithm with the traditional Markowitz model. Also, to ensure the stability of research findings, the performance of two algorithms in the mean-variance and mean-semi variance approaches were compared with quarterly data ending March 2022.
Results: According to the obtained results, the SPEA2 algorithm has better performance than the NSGAII algorithm in both approaches. Backtesting the real data for the quarter ending inMarch 2022 confirmed the findings of the present study. Also by doing robustness tests, the researchers found the SPEA2 algorithm as the superior algorithm in this research with better performance than Markowitz's basic model.
Conclusion: The results indicated that the 2 algorithm has better performance in selecting the optimal portfolio than the  algorithm in both the mean-variance and mean-semi variance approaches. Regardless of how the stock returns are distributed, this study recommends that individual and institutional investors use the SPEA2 algorithm to determine the optimal portfolio arrangement.


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