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

• DOI: 10.3311/ppee.7352
• Publication Date: 2015-04-08T10:36:19Z
• Authors: I. Róbert Sipos; János Levendovszky
• Keywords: 0502 economics and business; 05 social sciences

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.