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Transactions of the Institute of Measurement and Control
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Genetic Programming with Wavelet-Based Indicators for Financial Forecasting

Jin Li

The Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA), School of Computer Science, The University of Birmingham, Edgbaston, Birmingham B15 2TT, UK, J.li{at}cs.bham.ac.uk

Zhu Shi

School of Software Engineering, The University of Science and Technology of China,P.R. China

Xiaoli Li

The Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA), School of Computer Science, The University of Birmingham, Edgbaston, Birmingham B15 2TT, UK

Wavelet analysis, as a promising technique, has been used to approach numerous problems in science and engineering. Recent years have witnessed its novel application in economic and finance. This paper is to investigate whether features (or indicators) extracted using the wavelet analysis technique could improve financial forecasting by means of Financial Genetic Programming (FGP), a genetic programming-based forecasting tool. More specifically, to predict whether the Dow Jones Industrial Average (DJIA) Index will rise by 2.2% or more within the next 21 trading days, we first extract some indicators based on wavelet coefficients of the DJIA time series using a discrete wavelet transform; we then feed FGP with those wavelet-based indicators to generate decision trees and make predictions. By comparison with the prediction performance of our previous study, it is suggested that wavelet analysis be capable of bringing in promising indicators, and improving the forecasting performance of FGP.

Key Words: financial forecasting • genetic programming • stock data • wavelet analysis

Transactions of the Institute of Measurement and Control, Vol. 28, No. 3, 285-297 (2006)
DOI: 10.1191/0142331206tim177oa
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