Clustering Stock Exchange data by Using Evolutionary Algorithms for Portfolio Management

Malek Khojasteh Nejad

European Research Studies Journal, 2014, vol. XVII, issue 4, 55-66

Abstract: In present paper, imperialist competitive algorithm and ant colony algorithm and particle swarm optimization algorithm have been used to cluster stocks of Tehran stock exchange. Also results of the three algorithms have been compared with three famous clustering models so called k-means, Fcm and Som. After clustering, a portfolio has been made by choosing some stocks from each cluster and using NSGA-II algorithm. Results show superiority of ant colony algorithms and particle swarm optimization algorithm and imperialist competitive to other three methods for clustering stocks. Due to diversification of the portfolio, portfolio risk will be reduced while using data chosen from the clusters. The more efficient the clustering, the lower the risk is. Also, using clustering for portfolio management reduces time of portfolio selection.

Keywords: Portfolio Management; Data mining; Imperialist Competitive Algorithm; Ant Colony Algorithm; Particle Swarm Optimization Algorithm (search for similar items in EconPapers)
JEL-codes: F21 G11 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:ers:journl:v:xvii:y:2014:i:4:p:55-66

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