Portfolio selection model using teaching learning-based optimization approach

Kumar A.; Shahid M.

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Portfolio selection model using teaching learning-based optimization approach

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Portfolio selection is among the most challenging processes that have recently increased the interest of professionals in the area. The goal of mean-variance portfolio selection is to maximize expected return with minimizing risk. The Markowitz model was employed to solve the linear portfolio selection problem (PSP). However, due to numerous constraints and complexities, the problem is so critical that traditional models are insufficient to provide efficient solutions. Teaching learning-based optimization (TLBO) is a powerful population-based nature-inspired approach to solve optimization problems. This article presents a portfolio selection model using the TLBO approach to maximize the portfolio's Sharpe ratio. The Sharpe ratio combines both expected return and risk. This algorithm models the natural teaching process of the classroom with two main phases, viz., teaching and learning. Performance analysis has been undertaken to investigate the suitability of TLBO based solution approach by comparing it with genetic algorithm (GA) and particle swarm optimization (PSO) on the real datasets, Deutscher Aktienindex (DAX) 100, Hang Seng 31, standard poor’s (SP) 100, financial times stock exchange (FTSE) 100, and Nikkei 225. The empirical results verify the superiority of the TLBO over GA and PSO. © 2023, Institute of Advanced Engineering and Science. All rights reserved.

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《IAES International Journal of Artificial Intelligence》 |2023年第3期|1083-1090|共8页
作者
Kumar A.; Shahid M.;
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作者单位

Department of Commerce, Faculty of Commerce, Aligarh Muslim University;

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正文语种英语
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Nature inspired algorithm; optimization algorithm; Portfolio optimization; Portfolio selection; Sharpe ratio; Teaching learning-based;

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Portfolio selection model using teaching learning-based optimization approach

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