A constrained cluster-based approach for tracking the S&P 500 index

Dexiang Wu, Roy H. Kwon and Giorgio Costa

International Journal of Production Economics, 2017, vol. 193, issue C, 222-243

Abstract: We consider the problem of tracking a benchmark target portfolio of financial securities in particular the S&P 500. Linear integer programming models are developed that seeks to track a target portfolio using a strict subset of securities from the benchmark portfolio. The models represent a clustering approach to select securities and also include additional constraints that aim to control risk and transactions costs. Lagrangian and semi-Lagrangian methods are developed to compute solutions to the tracking models. The computational results show the effectiveness of the linear tracking models and the computational methods in tracking the S&P 500. Overall, the models and methods presented can serve as the basis of the optimization module in an optimization-based decision support for creating tracking portfolios.

Keywords: Index tracking; Portfolio optimization; Linear mixed integer programming; Lagrangian relaxation (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:193:y:2017:i:c:p:222-243

DOI: 10.1016/j.ijpe.2017.07.018

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