Portfolio optimization and risk measurement based on non-dominated sorting genetic algorithm

Ping-Chen Lin

doi:10.3934/jimo.2012.8.549

Article Contents

2012, Volume 8, Issue 3: 549-564. Doi: 10.3934/jimo.2012.8.549

This issue Previous Article Optimal investment with a value-at-risk constraint Next Article A Max-Min clustering method for $k$-means algorithm of data clustering

Portfolio optimization and risk measurement based on non-dominated sorting genetic algorithm

Ping-Chen Lin^1,

1.
National Kaohsiung University of Applied Sciences, Institute of Finance and Information, No. 415, Jiangung Rd., Sanmin Chiu, Kaohsiung, 807

Received: April 30, 2011

Revised: October 31, 2011

Published: June 2012

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Abstract

This paper introduces a multi-objective genetic algorithm (MOGA) in regard to the portfolio optimization issue in different risk measures, such as mean-variance, semi-variance, mean-variance-skewness, mean-absolute-deviation and lower-partial-moment to optimize risk of portfolio. This study introduces a PONSGA model by appling the non-dominated sorting genetic algorithm (NSGA-II) to maximize both the expected return and the skewness of portfolio as well as to simultaneously minimize different portfolio risks. The experimental results demonstrated that the PONSGA approach is significantly superior to the GA in all performances, examined such as the coefficient of variation, Sharpe index, Sortino index and portfolio performance index. The statistical significance tests also showed that the NSGA-II models outperformed the GA models in different risk measures.

Keywords:

49 and 90.

Mathematics Subject Classification: Portfolio, risk measurement, multi-objective genetic algorithm, NSGA-II, GA.

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