Fusion model of hidden Markov model for stock market forecasting

A fusion model APHMM is proposed by combining the hidden Markov model(HMM),artificial neural networks(ANN) and particle swarm optimization(PSO) to forecast financial market behavior.In APHMM,use ANN to transform the daily stock price into independent sets of values and become input to HMM.Then draw on PSO to optimize the initial parameters of HMM.The trained HMM is used to identify and locate similar patterns in the historical data.The price differences between the matched days and the respective next day are calculated.Finally,a weighted average of the price differences of similar patterns is obtained to prepare a forecast for the required next day.Forecasts are obtained for a number of securities that show APHMM is feasible.