Predicting the exchange traded fund DIA with a combination of genetic algorithms and neural networks

Abstract

Abstract We evaluate the performance of a heterogeneous mixture of neural network algorithms for predicting the exchange-traded fund DIA. A genetic algorithm is utilized to find the best mixture of neural networks, the topology of individual networks in the ensemble, and to determine the features set. The genetic algorithm also determines the window size of the input time-series supplied to the individual classifiers in the mixture of experts. The mixtures of neural network experts consist of recurrent back-propagation networks, and radial basis function networks. The application of genetic algorithm on the heterogeneous mixture of powerful neural network architectures shows promise for prediction of stock market time series. These highly non-linear, stochastic and highly non-stationary time series have been found to be notoriously difficult to predict using conventional linear statistical methods. In this paper, we propose a biologically inspired methodology to tackle such hard problem using a multi-faceted solution.