A Genetic Algorithm for UPM/LPM Portfolios

David Moreno (), David Nawrocki and Ignacio Olmeda
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David Nawrocki: Villanova University, College of Commence and Finance, (Philadelphia, USA)
Ignacio Olmeda: Universidad de Alcala, Computer Science Department (Madrid, Spain)

No 357, Computing in Economics and Finance 2006 from Society for Computational Economics

Abstract: Some researchers and many practitioners have move from the classic mean-variance (Markowitz, 1959) portfolio theory to a new portfolio optimization framework based on downside-risk measures that are more appropriate to the investor’s preferences. Moreover, several studies (Friedman and Savage, 1952; Kahneman and Tversky, 1979) have point out the existence of S-shape utility functions in investors, which mean, investors are risk-averse and risk-seeking. In this paper we propose a new portfolio optimization framework based on minimizing the Lower-Partial-Moment (LPM) and maximizing the upper-partial-moment (UPM) returns that is more in accordance to the investor’s behavior and the S-shape utility function found in real world. Given the complexity of the optimization problem, and the high nonlinearities and discontinuities, we use a metaheuristic (genetic algorithm) to achieve our goal. We find that, in general, the UPM-LPM portfolio optimization beats the classical mean-variance optimization and the mean-downside risk portfolios. Also, we find that the bigger differences happen close to the portfolio of minimum downside-risk and the smallest differences are in the area of the efficient frontier where the potential upside return is maximize.

Keywords: Downside-risk; Upper-Partial-Moment; Genetic Algorithm; Optimization (search for similar items in EconPapers)
JEL-codes: C63 G11 (search for similar items in EconPapers)
Date: 2006-07-04
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Persistent link: https://EconPapers.repec.org/RePEc:sce:scecfa:357

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