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A hybrid optimization approach to index tracking

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  • Rubén Ruiz-Torrubiano
  • Alberto Suárez

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. Copyright Springer Science+Business Media, LLC 2009

Suggested Citation

  • 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.
    • Handle: RePEc:spr:annopr:v:166:y:2009:i:1:p:57-71:10.1007/s10479-008-0404-4
    DOI: 10.1007/s10479-008-0404-4
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    References listed on IDEAS

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    1. Beasley, J. E. & Meade, N. & Chang, T. -J., 2003. "An evolutionary heuristic for the index tracking problem," European Journal of Operational Research, Elsevier, vol. 148(3), pages 621-643, August.
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    5. Rudolf, Markus & Wolter, Hans-Jurgen & Zimmermann, Heinz, 1999. "A linear model for tracking error minimization," Journal of Banking & Finance, Elsevier, vol. 23(1), pages 85-103, January.
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    Cited by:

    1. Chen, Qi-an & Hu, Qingyu & Yang, Hu & Qi, Kai, 2022. "A kind of new time-weighted nonnegative lasso index-tracking model and its application," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    2. Andrea Scozzari & Fabio Tardella & Sandra Paterlini & Thiemo Krink, 2013. "Exact and heuristic approaches for the index tracking problem with UCITS constraints," Annals of Operations Research, Springer, vol. 205(1), pages 235-250, May.
    3. Seyoung Park & Eun Ryung Lee & Sungchul Lee & Geonwoo Kim, 2019. "Dantzig Type Optimization Method with Applications to Portfolio Selection," Sustainability, MDPI, vol. 11(11), pages 1-32, June.
    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. Fengmin Xu & Meihua Wang & Yu-Hong Dai & Dachuan Xu, 2018. "A sparse enhanced indexation model with chance and cardinality constraints," Journal of Global Optimization, Springer, vol. 70(1), pages 5-25, January.
    6. 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).
    7. Meihua Wang & Chengxian Xu & Fengmin Xu & Hongang Xue, 2012. "A mixed 0–1 LP for index tracking problem with CVaR risk constraints," Annals of Operations Research, Springer, vol. 196(1), pages 591-609, July.
    8. Anubha Goel & Damir Filipovi'c & Puneet Pasricha, 2024. "Sparse Portfolio Selection via Topological Data Analysis based Clustering," Papers 2401.16920, arXiv.org, revised Dec 2024.
    9. Leonardo Riegel Sant’Anna & Tiago Pascoal Filomena & Pablo Cristini Guedes & Denis Borenstein, 2017. "Index tracking with controlled number of assets using a hybrid heuristic combining genetic algorithm and non-linear programming," Annals of Operations Research, Springer, vol. 258(2), pages 849-867, November.
    10. Rubio-García, Álvaro & Fernández-Lorenzo, Samuel & García-Ripoll, Juan José & Porras, Diego, 2024. "Accurate solution of the Index Tracking problem with a hybrid simulated annealing algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 639(C).
    11. Samuel Fern'andez-Lorenzo & Diego Porras & Juan Jos'e Garc'ia-Ripoll, 2020. "Hybrid quantum-classical optimization for financial index tracking," Papers 2008.12050, arXiv.org, revised Oct 2021.
    12. 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.
    13. H Mezali & J E Beasley, 2013. "Quantile regression for index tracking and enhanced indexation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(11), pages 1676-1692, November.
    14. Sant’Anna, Leonardo Riegel & Righi, Marcelo Brutti & Müller, Fernanda Maria & Guedes, Pablo Cristini, 2022. "Risk measure index tracking model," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 361-383.

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