Forecasting the U.S. stock market via Levenberg-Marquardt and Haken artificial neural networks using ICA&PCA pre-processing techniques

Authors:
Sergey Golovachev
Affiliations:
National Research University - Higher School of Economics, Moscow, Department of World Economics and International Affairs
Venue:
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Year:
2011

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Abstract

Artificial neural networks (ANN) is an approach to solving different tasks. In this paper we forecast U.S. stock market movements using two types of artificial neural networks: a network based on the Levenberg-Marquardt learning mechanism and a synergetic network which was described by German scientist Herman Haken. The Levenberg-Marquardt ANN is widely used for forecasting financial markets, while the Haken ANN is mostly known for the tasks of image recognition. In this paper we apply the Haken ANN for the prediction of the stock market movements. Furthermore, we introduce a novation concerning preprocessing of the input data in order to enhance the predicting power of the abovementioned networks. For this purpose we use Independent Component Analysis (ICA) and Principal Component Analysis (PCA). We also suggest using ANNs to reveal the "mean reversion" phenomenon in the stock returns. The results of the forecasting are compared with the forecasts of the simple auto-regression model and market index dynamics.