DocumentCode :
2387423
Title :
Use of Neural Networks in Forecasting Financial Market
Author :
Marzi, Hosein ; Turnbull, Mark
Author_Institution :
St. Francis Xavier Univ., Antigonish
fYear :
2007
fDate :
2-4 Nov. 2007
Firstpage :
516
Lastpage :
516
Abstract :
In today´s volatile financial market the demand for an accurate option price forecaster has been a focal point for researchers. The purpose of this study is to forecast option prices using neural networks. Initially simple neural network was implemented using twenty year period data from S&P 500 index call option prices. The prediction result was better than that of traditional Black-Scholes model. A hybrid neural network was developed that utilized aspects of Black- Scholes model into the neural network and tested against the traditional approach and simple neural network. The hybrid neural network outperformed performance of the tradition forecasting model and improved prediction results of simple neural networks.
Keywords :
backpropagation; economic forecasting; neural nets; share prices; stock markets; Black-Scholes model; Levenberg-Marquardt backpropagation algorithm; financial market forecasting; hybrid neural network training; option prices; Artificial neural networks; Computer networks; Demand forecasting; Economic forecasting; Economic indicators; Information systems; Neural networks; Predictive models; Pricing; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Granular Computing, 2007. GRC 2007. IEEE International Conference on
Conference_Location :
Fremont, CA
Print_ISBN :
978-0-7695-3032-1
Type :
conf
DOI :
10.1109/GrC.2007.78
Filename :
4403153
Link To Document :
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