TY - JOUR AU - Sipos, I. Róbert AU - Levendovszky, János PY - 2015/01/01 Y2 - 2024/03/29 TI - Optimizing Sparse Mean Reverting Portfolios with AR-HMMs in the Presence of Secondary Effects JF - Periodica Polytechnica Electrical Engineering and Computer Science JA - Period. Polytech. Elec. Eng. Comp. Sci. VL - 59 IS - 1 SE - DO - 10.3311/PPee.7352 UR - https://pp.bme.hu/eecs/article/view/7352 SP - 1-8 AB - 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.<br />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. ER -