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A Hybrid Model for Portfolio Optimization Based on Stock Clustering and Different Investment Strategies

Author

Listed:
  • Siamak Goudarzi

    (Department of Information Technology, University of Qom, Qom, Iran,)

  • Mohammad Javad Jafari

    (Department of Information Technology, University of Qom, Qom, Iran,)

  • Amir Afsar

    (Department of Industrial Management, Tarbiat Modares University, Tehran, Iran.)

Abstract

In today's dynamic business environment, in order to compete in the market, financial institutes are trying to find the best portfolio policy that in turn leads to an increase in the return and a decrease in the risk for the investors. The objective of this study is to develop a portfolio considering the behavior of investors in risk taking. This research aims to support investors, experts and intermediate managers in establishing optimized portfolio of stocks according to investment strategy. The proposed model has used the five indexes of risk, return, skewness, liquidity and current ratio of 66 companies that enlisted in Tehran stock exchange market and then clustered different companies using the hybrid method of clustering algorithm. After that, the clusters ranked using Topsis method. Ultimately, using genetic algorithm, the portfolio is established for different classes of investors with respect to their risk-taking level. The results show that the proposed model in comparison to general index, the industry index and the index of 50 more active companies are better in Tehran stock exchange.

Suggested Citation

  • Siamak Goudarzi & Mohammad Javad Jafari & Amir Afsar, 2017. "A Hybrid Model for Portfolio Optimization Based on Stock Clustering and Different Investment Strategies," International Journal of Economics and Financial Issues, Econjournals, vol. 7(3), pages 602-608.
    • Handle: RePEc:eco:journ1:2017-03-80
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    References listed on IDEAS

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    1. Wilford, D. Sykes, 2012. "True Markowitz or assumptions we break and why it matters," Review of Financial Economics, Elsevier, vol. 21(3), pages 93-101.
    2. Li, Xiang & Qin, Zhongfeng & Kar, Samarjit, 2010. "Mean-variance-skewness model for portfolio selection with fuzzy returns," European Journal of Operational Research, Elsevier, vol. 202(1), pages 239-247, April.
    3. Joro, Tarja & Na, Paul, 2006. "Portfolio performance evaluation in a mean-variance-skewness framework," European Journal of Operational Research, Elsevier, vol. 175(1), pages 446-461, November.
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    Cited by:

    1. Han Yang & Ming-hui Wang & Nan-jing Huang, 2021. "The $$\alpha$$ α -Tail Distance with an Application to Portfolio Optimization Under Different Market Conditions," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1195-1224, December.
    2. Hidetoshi Ito & Akane Murakami & Nixon Dutta & Yukari Shirota & Basabi Chakraborty, 2021. "Clustering of ETF Data for Portfolio Selection during Early Period of Corona Virus Outbreak," Gakushuin Economic Papers, Gakushuin University, Faculty of Economics, vol. 58(1), pages 99-114.

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    More about this item

    Keywords

    Portfolio Optimization; Clustering; Neural Network; Genetic Algorithm;
    All these keywords.

    JEL classification:

    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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