Portfolio Optimization in Iran Stock Market: Reinforcement Learning Approach

Subject Areas : Labor and Demographic Economics

mahdi  esfandiar ¹ (PhD student, Department of Industrial Management, Qom Branch, Islamic Azad University, Qom, Iran)
mohammadali  keramati ² (Associate Professor, Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran)
Reza  Gholami Jamkarani ³ (Assistant Professor, Department of Accounting, Qom Branch, Islamic Azad University, Qom, Iran)
Kashefy  Neishabouri ⁴ (Assistant Professor, Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran)

Received: 2022-08-16 Accepted : 2022-10-19 Published : 2022-09-21

Keywords: Portfolio optimization, Tehran Stock Exchange, reinforcement learning, Algorithmic Trading, Q Learning,

Abstract :

The concepts of portfolio optimization and diversification have become a tool for developing and understanding financial markets and financial decision making. The purpose of this paper is to use algorithmic trading with a focus on reinforcement learning approach in order to optimize the portfolio of selected stocks. This research is applied in terms of purpose and in terms of data type, quantitative and in terms of method, descriptive and exploratory and from the perspective of research plan, it is a post-event. The statistical population of this study was 672 stock exchange companies in March 1400, of which five companies (statistical sample) were selected. The sampling method was selected by one-step cluster and then purposeful selection of a share from inside each cluster and the study period was from 2017 to 2021. The findings of the research in the upward and downward periods of the market have shown that the reinforcement learning approach in bullish and bearish markets is significantly superior to the buy and maintain approach and has provided better performance, and the results are in line with the performance of algorithms in the stock markets.

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