High-order Hidden Markov Model for trend prediction in financial time series

Abstract

Financial price series trend prediction is an essential problem which has been discussed extensively using tools and techniques of economic physics and machine learning. Time dependence and volatility issues in this problem have made Hidden Markov Model (HMM) a useful tool in predicting the states of stock market. In this paper, we present an approach to predict the stock market price trend based on high-order HMM. Different from the commonly used first-order HMM, short and long-term time dependence are both considered in the high order HMM. By introducing a dimension reduction method which could transform the high-dimensional state vector of high-order HMM into a single one, we present a dynamic high-order HMM trading strategy to predict and trade CSI 300 and S&P 500 stock index for the next day given historical data. In our approach, we make a statistic of the daily returns in the history to demonstrate the relationship between hidden states and the price change trend. Experiments on CSI 300 and S&P 500 index illustrate that high-order HMM has preferable ability to identify market price trend than first-order one. Thus, the high-order HMM has higher accuracy and lower risk than the first-order model in predicting the index price trend.