Predicting S&P500 Index Using Artificial Neural Network

Abstract : This paper studies artificial neural network algorithm as a means of modelling and forecasting the financial market data. Such method bypasses traditional statistical method to deal with financial time series data. A recurrent neural network model, Elman network, is implemented to incorporate autocorrelation in time series data. A 3-parameter model is chosen to fit and forecast S&P 500 index. The experimental data is from 2000–2007, to screen out the abnormal market environment after 2008 financial crisis.
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Shanghong Li, Jiayu Zhang, Yan Qi. Predicting S&P500 Index Using Artificial Neural Network. 9th International Conference on Computer and Computing Technologies in Agriculture (CCTA), Sep 2015, Beijing, China. pp.173-189, ⟨1010.1007/978-3-319-48357-3_17⟩. ⟨hal-01557817⟩

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