Application and analysis of forecasting stock price index based on combination of ARIMA model and BP neural network

Yulin Du

63 citations0/0 files storedChinese Control and Decision Conference (CCDC)

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

Abstract

Stock price index is a barometer of the national economy, which often shows strong nonlinearity because of various factors. It is necessary to use nonlinear models to improve the accuracy of prediction. We predict Shanghai Securities Composition stock index with ARIMA-BP neural network method, and compare the accuracy with the result of single ARIMA model and BP neural network method. We find that the prediction accuracy of ARIMA-BP neural network is better than the BP neural network, BP neural network is better than linear model ARIMA, which confirms the change of stock price index is nonlinear.

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References

2011Using artificial neural network models in stock market index prediction 2014Recurrent neural network and a hybrid model for prediction of stock returns 2016Application of artificial neural network for the prediction of stock market returns: The case of the Japanese stock market 1992Using Neural Networks for Credit Scoring 2008An empirical methodology for developing stockmarket trading systems using artificial neural networks

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