The Recognition of Investor’s Sentiment and the Trading Strategy Based on HMM
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Juan Cheng, Chenghu Ma, Zhibai Wang
It is widely believed that the investor’s sentiment in securities market is unobservable. All participants can’t ignore investor’s sentiment since it has great influence on the market. The paper used Hidden Markov Model (HMM) to recognize investor’s sentiment in China’s A-share market. The investor’s sentiment is considered as hidden state in HMM. Since HMM doesn’t give the number of the hidden states, the paper sets the number according to Bayesian Information Criterion (BIC). The opening price, the closing price, the highest price, the lowest price and the volume of Shanghai-Shenzhen 300 index (CSI 300) are the observable sequences to figure out the optimal number of the hidden states. The result shows that the number of the hidden states varies from ten to twenty in different periods.In order to evaluate the model, we developed a strategy and did back testing using the historical data of ETF from January 2014 to December 2017.The result shows that the accumulative return of the strategy is greater than that of ETF in most of time except extreme market situation.
Hidden Markov Model, investor’s sentiment, transition probability matrix, hidden sequence, observation sequence, timing strategy, back testing