Portfolio optimization in capital market bubble space, application of bee colony algorithm

مهندسی مالی و مدیریت اوراق بهادار

Article 9, Volume 13, Issue 53, December 2022, Pages 153-172   

Document Type: Original Article

Authors

Iman Mohammadi¹; Hamzeh Mohammadi Khashoei^{* 2}; arezoo aghaei chadegani³

¹Department of Management, Najafabad Branch, Islamic Azad University, Najafabad, Iran.

²Management Department, Najaf Abad Branch, Islamic Azad University, Najaf Abad, Iran (Accounting Department, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran)

³Department of Accounting, Najafabad Branch, Islamic Azad University, Najafabad, Iran.

Abstract

The existence of bubbles in the market,especially the capital market,can be a factor in preventing the participation of investors in the capital market process and the correct allocation of financial resources for the economic development of the country.On the other hand,due to the goal of investors in achieving a high return portfolio with the least amount of risk,it is necessary to pay more attention to these markets In this study,in order to maximize returns and minimize investment risk,an attempt was made to create an optimal portfolio in conditions where the capital market has a price bubble.According to the purpose,the research is of applied type,and in terms of data,quantitative and post-event,and in terms of analysis,is descriptive-correlation.In order to identify bubble months in the period from2015to2019in Tehran Stock Exchange,sequence tests and skewness and kurtosis tests were used and after identifying bubble periods,artificial bee colony algorithm was used to optimize the portfolio.The results indicate the identification of 10 periods with a price bubble in the study period.Also,in portfolio optimization, selected stock portfolios are formed with maximum returns and minimum risk.This research will be a guide for investors in identifying bubble courses and how to form an optimal portfolio in these conditions.

Keywords

Price bubble; Portfolio optimization; Meta-heuristic algorithm; Returns; Risk

References