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

Authors

  • 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
https://doi.org/10.3311/PPee.7352

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

Citation data from Crossref and Scopus

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

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Articles