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In recent years there has been a significant growth of interest in the incorporation of historical series of variables related to stock prediction into mathematical models or computational algorithms in order to generate predictions or indications about expected price movements. The objective of this study was to utilize artificial neural networks to predict the closing price of the stock PETR4 which is traded on BM&FBOVESPA. Three stages were used to generate the prediction: obtainment of the samples, pre-processing, and prediction. 32 different configurations were created by varying the window size and prediction horizon. The best performance was obtained with 5 days of quotes and a prediction horizon of 1 day where the mean squared error was 0.0129.

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