A Supervised Classification System of Financial Data Based on Wavelet Packet and Neural Networks

Salim Lahmiri, Mounir Boukadoum and Sylvain Chartier
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Salim Lahmiri: Department of Computer Science, University of Quebec at Montreal, Montreal, QC, Canada, & ESCA School of Management, Casablanca, Morocco
Mounir Boukadoum: Department of Computer Science, University of Quebec at Montreal, Montreal, Canada
Sylvain Chartier: School of Psychology, University of Ottawa, Ottawa, Canada

International Journal of Strategic Decision Sciences (IJSDS), 2013, vol. 4, issue 4, 72-84

Abstract: The purpose of this paper is to present an automated system to classify financial data patterns as indicators of stock market future upward or downward moves. The classification system uses wavelet packet transform (WPT) for data decomposition and backpropagation neural networks (BPNN) for classification task. Its results are compared to those of a common classification system found in the literature which is based on ordinary wavelet transform (WT) and BPNN. In particular, the WPT is applied to the stock market data to obtain two categories of patterns: (i) approximation coefficients that represent major trend of the original data, and (ii) the residuals of the original data that capture its short-time variations. Therefore, those patterns are both complementary information used as inputs to classify stock market future shifts. For comparison purpose, BPNN and support vector machine (SVM) are separately used to classify patterns. Using S&P500 price index data, simulation results showed that both BPNN and SVM perform better with WPT extracted patterns (residuals and approximation coefficients) than standard approach based on WT approximation coefficients. In addition, BPNN outperform SVM. The WPT-NN based approach for financial data classification is more effective and promising than the standard approach adopted in the literature. The finding supports the adoption of the proposed classification system as an appropriate decision-making system in financial industry to classify financial data for forecasting purpose.

Date: 2013
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