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Article history: Received July 20, 2011 Accepted 7 October 2011 Available online 8 October 2011 The purpose of this paper is to predict the S&P500 down moves with technical analysis indicators using learning vector quantization (LVQ) neural networks and probabilistic neural networks (PNN). In addition, entropy-based input selection technique is employed to improve the prediction accuracies. The out-of-sample simulations show that LVQ outperforms PNN. In addition, the Entropy-LVQ system achieved higher accuracy in comparison with the literature. © 2012 Growing Science Ltd. All rights reserved.

An entropy-LVQ system for S&P500 downward shifts forecasting