Modelling and Trading the DJIA Financial Index Using Neural Networks Optimized with Adaptive Evolutionary Algorithms

Abstract

In the current paper we study an evolutionary framework for the optimization of various types Neural Networks’ structure and parameters. Two different adaptive evolutionary algorithms, named as adaptive Genetic Algorithms (aGA) and adaptive Differential Evolution (aDE), were developed to optimize the structure and the parameters of two different types of Neural Networks: Multilayer Perceptron (MLPs) and Wavelet Neural Networks (WNN). Wavelet neural networks have been introduced as an alternative to MLPs to overcome their shortcomings presenting more compact architecture and higher learning speed. Furthermore, the evolutionary algorithms, which were implemented, are both adaptive in terms that their most important parameters (Mutation and Crossover probabilities) are assigned using a self adaptive scheme. The motivation of this paper is to uncover novel hybrid methodologies for the task of forecasting and trading DJIE financial index. This is done by benchmarking the forecasting performance the four proposed hybrid methodologies (aGA-MLP, aGA-WNN, aDE-MLP and aDE-WNN) with some traditional techniques, either statistical such as a an autoregressive moving average model (ARMA), or technical such as a moving average covcergence/divergence model (MACD). The trading performance of all models is investigated in a forecast and trading simulation on our time series over the period 1997-2012. As it turns out, the aDE-WNN hybrid methodology does remarkably well and outperforms all other models in simple trading simulation exercises. (This paper is submitted for the ACIFF workshop).