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Forecasting S&P 500 index using artificial neural networks and design of experiments

Forecasting S&P 500 index using artificial neural networks and design of experiments The main objective of this research is to forecast the daily direction of Standard & Poor's 500 (S&P 500) index using an artificial neural network (ANN). In order to select the most influential features (factors) of the proposed ANN that affect the daily direction of S&P 500 (the response), design of experiments are conducted to determine the statistically significant factors among 27 potential financial and economical variables along with a feature defined as the number of nodes of the ANN. The results of employing the proposed methodology show that the ANN that uses the most influential features is able to forecast the daily direction of S&P 500 significantly better than the traditional logit model. Furthermore, experimental results of employing the proposed ANN on the trades in a test period indicate that ANN could significantly improve the trading profit as compared with the buy-and-hold strategy. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Industrial Engineering International Springer Journals

Forecasting S&P 500 index using artificial neural networks and design of experiments

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References (42)

Publisher
Springer Journals
Copyright
Copyright © 2013 by Niaki and Hoseinzade; licensee Springer.
Subject
Engineering; Industrial and Production Engineering; Quality Control, Reliability, Safety and Risk; Facility Management; Engineering Economics, Organization, Logistics, Marketing; Appl.Mathematics/Computational Methods of Engineering
ISSN
1735-5702
eISSN
2251-712X
DOI
10.1186/2251-712X-9-1
Publisher site
See Article on Publisher Site

Abstract

The main objective of this research is to forecast the daily direction of Standard & Poor's 500 (S&P 500) index using an artificial neural network (ANN). In order to select the most influential features (factors) of the proposed ANN that affect the daily direction of S&P 500 (the response), design of experiments are conducted to determine the statistically significant factors among 27 potential financial and economical variables along with a feature defined as the number of nodes of the ANN. The results of employing the proposed methodology show that the ANN that uses the most influential features is able to forecast the daily direction of S&P 500 significantly better than the traditional logit model. Furthermore, experimental results of employing the proposed ANN on the trades in a test period indicate that ANN could significantly improve the trading profit as compared with the buy-and-hold strategy.

Journal

Journal of Industrial Engineering InternationalSpringer Journals

Published: Feb 28, 2013

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