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A hybrid optimization approach to index tracking

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Abstract

Index tracking consists in reproducing the performance of a stock-market index by investing in a subset of the stocks included in the index. A hybrid strategy that combines an evolutionary algorithm with quadratic programming is designed to solve this NP-hard problem: Given a subset of assets, quadratic programming yields the optimal tracking portfolio that invests only in the selected assets. The combinatorial problem of identifying the appropriate assets is solved by a genetic algorithm that uses the output of the quadratic optimization as fitness function. This hybrid approach allows the identification of quasi-optimal tracking portfolios at a reduced computational cost.

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Authors and Affiliations

  1. Computer Science Department, Universidad Autónoma de Madrid, Calle Francisco Tomás y Valiente, 11, 28049, Madrid, Spain

    Rubén Ruiz-Torrubiano & Alberto Suárez

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  1. Rubén Ruiz-Torrubiano
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  2. Alberto Suárez
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Correspondence to Rubén Ruiz-Torrubiano.

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Ruiz-Torrubiano, R., Suárez, A. A hybrid optimization approach to index tracking. Ann Oper Res 166, 57–71 (2009). https://doi.org/10.1007/s10479-008-0404-4

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  • DOI: https://doi.org/10.1007/s10479-008-0404-4

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