Optimizing Sparse Mean Reverting Portfolios with AR-HMMs in the Presence of Secondary Effects

  • I. Róbert Sipos
    Affiliation

    Department of Networked Systems and Services Budapest University of Technology and Economics Budapest, Hungary

  • János Levendovszky
    Affiliation

    Department of Networked Systems and Services Budapest University of Technology and Economics Budapest, Hungary

Abstract

In this paper we optimize mean reverting portfolios subject to cardinality constraints. First, the parameters of the corresponding Ornstein-Uhlenbeck (OU) process are estimated by auto-regressive Hidden Markov Models (AR-HMM) in order to capture the underlying characteristics of the financial time series. Portfolio optimization is then performed according to maximizing the mean return by the means of the introduced AR-HMM prediction algorithm. The optimization itself is carried out by stochastic search algorithms. The presented solutions satisfy the cardinality constraint thus providing a sparse portfolios which minimizes the transaction costs and maximizes the interpretability of the results.
The performance has been tested on historical data obtained from S&P 500 and FOREX. The results demonstrate that a good average return can be achieved by the proposed AR-HMM based trading algorithms in realistic scenarios. Furthermore, profitability can also be accomplished in the presence of secondary effects.
Keywords: mean reversion, Markov models, parameter estimation, financial time series, algorithmic trading
Published online
2015-04-08
How to Cite
Sipos, I. R., Levendovszky, J. “Optimizing Sparse Mean Reverting Portfolios with AR-HMMs in the Presence of Secondary Effects”, Periodica Polytechnica Electrical Engineering and Computer Science, 59(1), pp. 1-8, 2015. https://doi.org/10.3311/PPee.7352
Section
Articles