Optimizing Sparse Mean Reverting Portfolios with AR-HMMs in the Presence of Secondary Effects
AbstractIn 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
How to Cite
Sipos, I. R. and Levendovszky, J. (2015) “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. https://doi.org/10.3311/PPee.7352.