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Sparse, mean reverting portfolio selection using simulated annealing

Author

Listed:
  • Fogarasi, Norbert

    (Department of Networked Systems and Services, Budapest University of Technology and Economics)

  • Levendovszky, Janos

    (Department of Networked Systems and Services, Budapest University of Technology and Economics)

Abstract

We study the problem of finding sparse, mean reverting portfolios based on multivariate historical time series. After mapping the optimal portfolio selection problem into a generalized eigenvalue problem, we propose a new optimization approach based on the use of simulated annealing. This new method ensures that the cardinality constraint is automatically satisfied in each step of the optimization by embedding the constraint into the iterative neighbor selection function. We empirically demonstrate that the method produces better mean reversion coefficients than other heuristic methods, but also show that this does not necessarily result in higher profits during convergence trading. This implies that more complex objective functions should be developed for the problem, which can also be optimized under cardinality constraints using the proposed approach.

Suggested Citation

  • Fogarasi, Norbert & Levendovszky, Janos, 2013. "Sparse, mean reverting portfolio selection using simulated annealing," Algorithmic Finance, IOS Press, vol. 2(3-4), pages 197-211.
    • Handle: RePEc:ris:iosalg:0013
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    Citations

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    Cited by:

    1. Attila Ceffer & Janos Levendovszky & Norbert Fogarasi, 2019. "Applying Independent Component Analysis and Predictive Systems for Algorithmic Trading," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 281-303, June.
    2. I. Róbert Sipos & Attila Ceffer & János Levendovszky, 2017. "Parallel Optimization of Sparse Portfolios with AR-HMMs," Computational Economics, Springer;Society for Computational Economics, vol. 49(4), pages 563-578, April.
    3. Philippe Goulet Coulombe & Maximilian Goebel, 2023. "Maximally Machine-Learnable Portfolios," Papers 2306.05568, arXiv.org, revised Apr 2024.
    4. Philippe Goulet Coulombe & Maximilian Gobel, 2023. "Maximally Machine-Learnable Portfolios," Working Papers 23-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Apr 2023.

    More about this item

    Keywords

    mean reversion; convergence trading; parameter estimation; stochastic optimization; simulated annealing;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • D40 - Microeconomics - - Market Structure, Pricing, and Design - - - General

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