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Integrated Metaheuristic Optimization Of 130–30 Investment‐Strategy‐Based Long–Short Portfolios

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  • G.A. Vijayalakshmi Pai
  • Thierry Michel

Abstract

A 130–30 strategy is an attractive and viable equity investment strategy for building long–short portfolios and notionally expected to enhance investment exposure and market protection. However, the amalgamation of the strategy in the portfolio optimization problem model poses complex constraints which render the problem model difficult for solving using traditional methods, thus justifying the application of metaheuristic solutions. We discuss a metaheuristic and integrated optimization of long–short portfolios, when the 130–30‐strategy‐based constraint, besides other investor preferential constraints, is incorporated in the problem's formulation. In the absence of reported work and for reasons of performance comparison and analysis, two metaheuristic strategies have been proposed in order to solve the problem: (i) evolution strategy with hall of fame and (ii) differential evolution (rand/1/bin) with hall of fame. The experimental studies were undertaken on the Bombay Stock Exchange (BSE200) and Tokyo Stock Exchange (Nikkei 225) data sets and specifically for the period March 1999–March 2009, which included both upturns and downturns in the global markets. The efficiencies of the portfolios obtained by the two metaheuristic methods were analysed using an efficiency improvement possibility function, a portfolio productivity indicator which is a variation of Luenberger's shortage functions. Copyright © 2012 John Wiley & Sons, Ltd.

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  • G.A. Vijayalakshmi Pai & Thierry Michel, 2012. "Integrated Metaheuristic Optimization Of 130–30 Investment‐Strategy‐Based Long–Short Portfolios," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(1), pages 43-74, January.
  • Handle: RePEc:wly:isacfm:v:19:y:2012:i:1:p:43-74
    DOI: 10.1002/isaf.335
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    1. Doering, Jana & Kizys, Renatas & Juan, Angel A. & Fitó, Àngels & Polat, Onur, 2019. "Metaheuristics for rich portfolio optimisation and risk management: Current state and future trends," Operations Research Perspectives, Elsevier, vol. 6(C).

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