Stock Index Forecasting by Hidden Markov Models with Trends Recognition

Stock index forecast is a complicated problem since the financial market is influenced by various underlying factors and some of them are unobservable. Hidden Markov Model (HMM) is an effective probabilistic graphic model which can models the hidden states of the observed sequence. So the underlying patterns of the stock movements can be learnt by HMM models. In this paper, HMM is employed to forecast the Shanghai Stock Exchange Composite Index and Shenzhen Stock Exchange Composite Index, which are the two representative stock indices in China. The proposed method is empirically tested on the target data sets and compared with other models based on HMM using Mean Absolute Percentage Error (MAPE). Results show that the proposed HMM model achieves good forecasting performance, and at the same time reveals meaningful hidden states of stock market.