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

被引：191
|
作者：
Hassan, Md. Rafiul [1 ]
Nath, Baikunth [1 ]
Kirley, Michael [1 ]
机构：
[1] Univ Melbourne, Dept Comp Sci & Software Engn, Carlton, Vic 3010, Australia
来源：
EXPERT SYSTEMS WITH APPLICATIONS | 2007年 / 33卷 / 01期
关键词：
forecasting; stock market; Hidden Markov Model; Artificial Neural Network; Genetic Algorithm; ARTIFICIAL NEURAL-NETWORKS; OPTIMIZATION;
D O I：
10.1016/j.eswa.2006.04.007
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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. (c) 2006 Elsevier Ltd. All rights reserved.
引用
收藏
页码：171 / 180
页数：10
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