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The use of artificial neural networks in the analysis and prediction of stock prices

Fagner Andrade de Oliveira · Luis Enrique Zárate · Marcos de Azevedo Reis · Cristiane Neri Nobre

85 citations0/0 files storedConference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics

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Stock Market Forecasting Methods Neural Networks and Applications Forecasting Techniques and Applications

Abstract

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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References

1998Forecasting with artificial neural networks:2008Surveying stock market forecasting techniques – Part II: Soft computing methods 1988Economic prediction using neural networks: the case of IBM daily stock returns 1998How effective are neural networks at forecasting and prediction? A review and evaluation 1961Investments: Analysis and Management.1994Time series forecasting using neural networks 1997Neural networks in financial engineering: a study in methodology 2006Machine Learning Techniques and Use of Event Information for Stock Market Prediction: A Survey and Evaluation

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