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Computer and Computing Technologies in Agriculture IX 9th IFIP WG 5.14 International Conference, CCTA 2015 Beijing, China, September 27–30, 2015, Part I |
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Conference papers
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.
Cited literature [11 references]
https://hal.inria.fr/hal-01557817
Contributor : Hal Ifip <>
Submitted on : Thursday, July 6, 2017 - 3:50:02 PM
Last modification on : Thursday, July 6, 2017 - 3:54:12 PM
Long-term archiving on: : Wednesday, January 24, 2018 - 1:45:28 AM
Contributor : Hal Ifip <>
Submitted on : Thursday, July 6, 2017 - 3:50:02 PM
Last modification on : Thursday, July 6, 2017 - 3:54:12 PM
Long-term archiving on: : Wednesday, January 24, 2018 - 1:45:28 AM
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