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

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  • Wu, Dexiang
  • Kwon, Roy H.
  • Costa, Giorgio

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.

Suggested Citation

  • Wu, Dexiang & Kwon, Roy H. & Costa, Giorgio, 2017. "A constrained cluster-based approach for tracking the S&P 500 index," International Journal of Production Economics, Elsevier, vol. 193(C), pages 222-243.
    • Handle: RePEc:eee:proeco:v:193:y:2017:i:c:p:222-243
    DOI: 10.1016/j.ijpe.2017.07.018
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    Cited by:

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    2. Almohri, Haidar & Chinnam, Ratna Babu & Colosimo, Mark, 2019. "Data-driven analytics for benchmarking and optimizing the performance of automotive dealerships," International Journal of Production Economics, Elsevier, vol. 213(C), pages 69-80.
    3. Frieder Meyer-Bullerdiek, 2022. "Selected Methods of optimized Sampling for Index Tracking – Evidence from German Stocks," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 12(6), pages 1-8.
    4. Mahdi Moeini, 2022. "Solving the index tracking problem: a continuous optimization approach," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(2), pages 807-835, June.
    5. Seo Woo Hong & Pierre Miasnikof & Roy Kwon & Yuri Lawryshyn, 2021. "Market Graph Clustering via QUBO and Digital Annealing," JRFM, MDPI, vol. 14(1), pages 1-13, January.
    6. Julio Cezar Soares Silva & Adiel Teixeira de Almeida Filho, 2023. "A systematic literature review on solution approaches for the index tracking problem in the last decade," Papers 2306.01660, arXiv.org, revised Jun 2023.

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