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Fuzzy Expert System for Stock Portfolio Selection: An Application to Bombay Stock Exchange

Author

Listed:
  • Gour Sundar Mitra Thakur

    (Mondal)

  • Rupak Bhattacharyya

    (Mondal)

  • Seema Sarkar

    (Mondal)

Abstract

Selection of proper stocks, before allocating investment ratios, is always a crucial task for the investors. Presence of many influencing factors in stock performance have motivated researchers to adopt various Artificial Intelligence (AI) techniques to make this challenging task easier. In this paper a novel fuzzy expert system model is proposed to evaluate and rank the stocks under Bombay Stock Exchange (BSE). Dempster-Shafer (DS) evidence theory is used for the first time to automatically generate the consequents of the fuzzy rule base to reduce the effort in knowledge base development of the expert system. Later a portfolio optimization model is constructed where the objective function is considered as the ratio of the difference of fuzzy portfolio return and the risk free return to the weighted mean semi-variance of the assets that has been used. The model is solved by applying Ant Colony Optimization (ACO) algorithm by giving preference to the top ranked stocks. The performance of the model proved to be satisfactory for short-term investment period when compared with the recent performance of the stocks.

Suggested Citation

  • Gour Sundar Mitra Thakur & Rupak Bhattacharyya & Seema Sarkar, 2022. "Fuzzy Expert System for Stock Portfolio Selection: An Application to Bombay Stock Exchange," Papers 2204.13385, arXiv.org, revised May 2022.
    • Handle: RePEc:arx:papers:2204.13385
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    References listed on IDEAS

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    3. Grossman, Sanford J & Stiglitz, Joseph E, 1980. "On the Impossibility of Informationally Efficient Markets," American Economic Review, American Economic Association, vol. 70(3), pages 393-408, June.
    4. Włodzimierz Ogryczak, 2000. "Multiple criteria linear programming model for portfolio selection," Annals of Operations Research, Springer, vol. 97(1), pages 143-162, December.
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