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Threshold Accepting for Index Tracking

Author

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  • Manfred Gilli and Evis Kellezi

Abstract

In this paper we investigate the performance of the threshold accepting heuristic for the index tracking problem. The index tracking problem consists in minimizing the tracking error between a portfolio and a benchmark. The objective is to replicate the performance of a given index upon the condition that the number of stocks allowed in the portfolio is smaller than the number of stocks in the benchmarking index. The quantities of stocks in the portfolio are integers. Transaction costs have to be faced each time that the portfolio is rebalanced. We find the composition of a portfolio that best tracks the performance of the benchmark during a given period in the past and then look at the performance of the portfolio in the subsequent period. We report computational results in the cases where the benchmarks are market indices tracked by a small number of assets. We find that the threshold accepting is a very suitable and efficient optimization technique for this problem.

Suggested Citation

  • Manfred Gilli and Evis Kellezi, 2001. "Threshold Accepting for Index Tracking," Computing in Economics and Finance 2001 72, Society for Computational Economics.
    • Handle: RePEc:sce:scecf1:72
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    Cited by:

    1. Nikos S. Thomaidis & Timotheos Angelidis & Vassilios Vassiliadis & Georgios Dounias, 2009. "Active Portfolio Management With Cardinality Constraints: An Application Of Particle Swarm Optimization," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 5(03), pages 535-555.
    2. Rubén Ruiz-Torrubiano & Alberto Suárez, 2009. "A hybrid optimization approach to index tracking," Annals of Operations Research, Springer, vol. 166(1), pages 57-71, February.

    More about this item

    Keywords

    Index tracking; threshold accepting; heuristic optimization;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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