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


Salim Lahmiri


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.


DOI: j.msl.2011.10.006

Keywords: Stock market ,Neural networks ,Loss limit ,Forecasting

How to cite this paper:

Lahmiri, S. (2012). An entropy-LVQ system for S&P500 downward shifts forecasting.Management Science Letters, 2(1), 21-28.


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