An integrated Procedure for Resolving Portfolio Optimization Problems, Using Data Envelopment Analysis, Ant Colony Optimization, and Gene Expression Programming

Chih-Ming Hsu

Abstract


The portfolio optimization problem is an important issue in the field of investment/financial decision-making and is currently receiving considerable attention from both researchers and practitioners. The problem becomes much more difficult if the number of assets is increased or if additional constraints, such as cardinality constraints, bounding constraints or other real-world requirements, are considered. Therefore, various heuristic approaches have been proposed to deal with the portfolio optimization problem, which is difficult to resolve using the traditional mathematical programming technique. In this study, an integrated procedure using data envelopment analysis (DEA), ant colony optimization (ACO) for continuous domains and gene expression programming (GEP) is proposed. The procedure is evaluated through a case study on investing in stocks in the semiconductor sub-section of the Taiwan stock market. By providing a potential average return of 13.12% on six-month investments from November 1, 2007 to July 8, 2011, the experimental results show that the proposed procedure can be considered a feasible and effective tool for making outstanding investment plans. Moreover, it is a strategy that can help investors make profits even though the overall stock market suffers a loss.

Keywords


Portfolio optimization; Data envelopment analysis; Ant colony optimization; Gene expression programming

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References


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