A new two-phase approach to the portfolio optimization problem based on the prediction of stock price trends

الموضوعات :

Hamid Reza Yousefzade ¹ , Amin Karrabi ² , Aghileh Heydari ³

1 - Department of Mathematics, Payame Noor University (PNU), Tehran, Iran
2 - Department of Mathematics, Payam noor University, Mashhad, Iran
3 - Department of Mathematics, Payame Noor University (PNU), P.O. BOX 19395-4697, Tehran, Iran.

تاريخ الإرسال : 23 الإثنين , ذو الحجة, 1442 تاريخ التأكيد : 10 الثلاثاء , جمادى الأولى, 1443 تاريخ الإصدار : 05 السبت , ربيع الأول, 1444

الکلمات المفتاحية: ‎Multi-objective particle swarm optimization (MOPSO), Efficient Frontier, Support vector regression (SVR), Multi-objective optimziation,

ملخص المقالة :

Forming a portfolio of different stocks instead of buying a particular type of stock can reduce the potential loss of investing in the stock market. Although forming a portfolio based solely on past data is the main theme of various researches in this field, considering a portfolio of different stocks regardless of their future return can reduce the profits of investment. The aim of this paper is to introduce a new two-phase approach to forming an optimal portfolio using the predicted stock trend pat-tern. In the first phase, we use the Hurst exponent as a filter to identify stable stocks and then, we use a meta-heuristic algorithm such as the support vector regression algorithm to predict stable stock price trends. In the next phase, according to the predicted price trend of each stock having a positive return, we start arranging the portfolio based on the type of stock and the percentage of allocated capacity of the total portfolio to that stock. To this end, we use the multi-objective particle swarm optimization algorithm to determine the optimal portfolios as well as the optimal weights corresponding to each stock. The sample, which was selected using the systematic removal method, consists of active firms listed on the Tehran Stock Ex-change from 2018 to 2020. Experimental results, obtained from a portfolio based on the prediction of stock price trends, indicate that our suggested approach outperforms the retrospective approaches in approximating the actual efficient frontier of the problem, in terms of both diversity and convergence.

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