A fusion model of HMM, ANN and GA for stock market forecasting

Authors:
Md. Rafiul Hassan;Baikunth Nath;Michael Kirley
Affiliations:
Computer Science and Software Engineering, The University of Melbourne, Carlton 3010, Australia;Computer Science and Software Engineering, The University of Melbourne, Carlton 3010, Australia;Computer Science and Software Engineering, The University of Melbourne, Carlton 3010, Australia
Venue:
Expert Systems with Applications: An International Journal
Year:
2007

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Abstract

In this paper we propose and implement a fusion model by combining the Hidden Markov Model (HMM), Artificial Neural Networks (ANN) and Genetic Algorithms (GA) to forecast financial market behaviour. The developed tool can be used for in depth analysis of the stock market. Using ANN, the daily stock prices are transformed to independent sets of values that become input to HMM. We draw on GA 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 in the IT sector and are compared with a conventional forecast method.