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An integrated portfolio optimisation procedure based on data envelopment analysis, artificial bee colony algorithm and genetic programming

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  • Chih-Ming Hsu

Abstract

Portfolio optimisation is an important issue in the field of investment/financial decision-making and has received considerable attention from both researchers and practitioners. However, besides portfolio optimisation, a complete investment procedure should also include the selection of profitable investment targets and determine the optimal timing for buying/selling the investment targets. In this study, an integrated procedure using data envelopment analysis (DEA), artificial bee colony (ABC) and genetic programming (GP) is proposed to resolve a portfolio optimisation problem. The proposed procedure is evaluated through a case study on investing in stocks in the semiconductor sub-section of the Taiwan stock market for 4 years. The potential average 6-month return on investment of 9.31% from 1 November 2007 to 31 October 2011 indicates that the proposed procedure can be considered a feasible and effective tool for making outstanding investment plans, and thus making profits in the Taiwan stock market. Moreover, it is a strategy that can help investors to make profits even when the overall stock market suffers a loss.

Suggested Citation

  • Chih-Ming Hsu, 2014. "An integrated portfolio optimisation procedure based on data envelopment analysis, artificial bee colony algorithm and genetic programming," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(12), pages 2645-2664, December.
    • Handle: RePEc:taf:tsysxx:v:45:y:2014:i:12:p:2645-2664
    DOI: 10.1080/00207721.2013.775388
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    1. Michaud, Richard O. & Michaud, Robert O., 2008. "Efficient Asset Management: A Practical Guide to Stock Portfolio Optimization and Asset Allocation," OUP Catalogue, Oxford University Press, edition 2, number 9780195331912.
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    Cited by:

    1. Chen, Wei, 2015. "Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 125-139.
    2. Luís Lobato Macedo & Pedro Godinho & Maria João Alves, 2020. "A Comparative Study of Technical Trading Strategies Using a Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 349-381, January.
    3. Alireza Moradi & Saber Saati & Mehrzad Navabakhsh, 2023. "Genetic algorithms for optimizing two-stage DEA by considering unequal intermediate weights," OPSEARCH, Springer;Operational Research Society of India, vol. 60(3), pages 1202-1217, September.
    4. Carla Oliveira Henriques & Maria Elisabete Neves & Licínio Castelão & Duc Khuong Nguyen, 2022. "Assessing the performance of exchange traded funds in the energy sector: a hybrid DEA multiobjective linear programming approach," Annals of Operations Research, Springer, vol. 313(1), pages 341-366, June.

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