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Stock portfolio selection with full-scale optimization and differential evolution

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  • Bjorn Hagstromer
  • Jane Binner

Abstract

Full-Scale Optimization (FSO) is a utility maximization approach to portfolio choice problems that has theoretical appeal but that suffers from computational burden in large scale problems. We apply the heuristic technique differential evolution to solve FSO-type asset selection problems of 97 assets under complex utility functions rendering rough utility search surfaces. We show that this problem is computationally feasible and that solutions retrieved with random starting values are converging to one optimum. Furthermore, the study constitutes the first FSO application to stock portfolio optimization. The results indicate that when investors are loss averse, FSO improves stock portfolio performance compared to Mean Variance (MV) portfolios. This finding widens the scope of applicability of FSO, but it is also stressed that out-of-sample success will always be dependent on the forecasting ability of the input return distributions.

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  • Bjorn Hagstromer & Jane Binner, 2009. "Stock portfolio selection with full-scale optimization and differential evolution," Applied Financial Economics, Taylor & Francis Journals, vol. 19(19), pages 1559-1571.
    • Handle: RePEc:taf:apfiec:v:19:y:2009:i:19:p:1559-1571
    DOI: 10.1080/09603100903018778
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    Cited by:

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    3. Rand Kwong Yew Low, 2018. "Vine copulas: modelling systemic risk and enhancing higher‐moment portfolio optimisation," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(S1), pages 423-463, November.
    4. Liang-chuan Wu & I-chan Tsai, 2014. "Three fuzzy goal programming models for index portfolios," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(8), pages 1155-1169, August.
    5. Aydin Aslan & Peter N. Posch, 2022. "How Do Investors Value Sustainability? A Utility-Based Preference Optimization," Sustainability, MDPI, vol. 14(23), pages 1-15, November.
    6. Jules Clement Mba & Edson Pindza & Ur Koumba, 2018. "A differential evolution copula-based approach for a multi-period cryptocurrency portfolio optimization," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 32(4), pages 399-418, November.
    7. Madalina Gabriela ANGHEL & Gyorgy BODO & Okwiet BARTEK, 2016. "Model of Static Portfolio Choices," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 64(1), pages 49-53, January.

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