Recurrent Neural Networks in Forecasting S&P 500 Index

8 Pages Posted: 18 Jul 2017

See all articles by Samuel Edet

Samuel Edet

IMT School for Advanced Studies

Date Written: July 12, 2017

Abstract

The objective of this research is to predict the movements of the S&P 500 index using variations of the recurrent neural network. The variations considered are the simple recurrent neural network, the long short term memory and the gated recurrent unit. In addition to these networks, we discuss the error correction neural network which takes into account shocks typical of the financial market. In predicting the S&P 500 index, we considered 14 economic variables, 4 levels of hidden neurons of the networks and 5 levels of epoch. From these features, relevant features were selected using experimental design. The selection of an experiment with the right features is chosen based on its accuracy score and its Graphical Processing Unit (GPU) time. The chosen experiments (for each neural network) are used to predict the upward and downward movements of the S&P 500 index. Using the prediction of the S&P 500 index and a proposed strategy, we trade the S&P 500 index for selected periods. The profit generated is compared with the buy and hold strategy.

Keywords: Recurrent neural networks, long short term memory, error correction neural network,S&P 500 index

JEL Classification: C90

Suggested Citation

Edet, Samuel, Recurrent Neural Networks in Forecasting S&P 500 Index (July 12, 2017). Available at SSRN: https://ssrn.com/abstract=3001046 or http://dx.doi.org/10.2139/ssrn.3001046

Samuel Edet (Contact Author)

IMT School for Advanced Studies ( email )

Piazza S. Francesco 19
Lucca, IT-55100
Italy
55100 (Fax)

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