M. Versace, R. Bhatt, O. Hinds, and M. Shiffer (USA)
: genetic algorithms, neural networks, financial time series forecasting, mixture of experts,
: In this paper, we propose a biologically inspired methodology to tackle the problem of financial time series using a multi-faceted solution. The paper analyzes how combining ontogenetic and phylogenetic learning, two concepts borrowed from biology, can help optimize financial applications trough biologically inspired methods. 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.
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