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Optimizing sparse mean reverting portfolios

Author

Listed:
  • Sipos, I. Róbert

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

  • Levendovszky, János

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

Abstract

In this paper we investigate trading with optimal mean reverting portfolios subject to cardinality constraints. First, we identify the parameters of the underlying VAR(1) model of asset prices and then the quantities of the corresponding Ornstein-Uhlenbeck (OU) process are estimated by pattern matching techniques. Portfolio optimization is performed according to two approaches: (i) maximizing the predictability by solving the generalized eigenvalue problem or (ii) maximizing the mean return. The optimization itself is carried out by stochastic search algorithms and Feed Forward Neural Networks (FFNNs). The presented solutions satisfy the cardinality constraint thus providing sparse portfolios to minimize the transaction costs and to maximize interpretability of the results. The performance has been tested on historical data (SWAP rates, SP 500, and FOREX). The proposed trading algorithms have achieved 29.57% yearly return on average, on the examined data sets. The algorithms prove to be suitable for high frequency, intraday trading as they can handle financial data up to the arrival rate of every second

Suggested Citation

  • Sipos, I. Róbert & Levendovszky, János, 2013. "Optimizing sparse mean reverting portfolios," Algorithmic Finance, IOS Press, vol. 2(2), pages 127-139.
    • Handle: RePEc:ris:iosalg:0019
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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.

    More about this item

    Keywords

    mean reversion; convergence trading; parameter estimation; VAR(1) model; financial time series.;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • E00 - Macroeconomics and Monetary Economics - - General - - - General
    • F00 - International Economics - - General - - - General

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