Agent-Based Modelling with Wavelets and an Evolutionary Artificial Neural Network: Applications to CAC 40 Forecasting

Abstract

Analysis of separate scales of a complex signal provides a valuable source of information, considering that different financial decisions occur at different scales. Wavelet transform decomposition of a complex time series into separate scales and their economic representation is a focus of this study. An evolutionary / artificial neural network (E/ANN) is used to learn the information at separate scales and combine it into meaningfully weighted structures. Potential applications of the proposed approach are in financial forecasting and trading strategies development based on individual preferences and trading styles.