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

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.