NDLI: Stock market prediction using different neural network classification architectures

Content Provider	IEEE Xplore Digital Library
Author	Schierholt, K. Dagli, C.H.
Copyright Year	1996
Description	Author affiliation: Dept. of Eng. Manage., Missouri Univ., Rolla, MO, USA (Schierholt, K.; Dagli, C.H.)
Abstract	In recent years, many attempts have been made to predict the behavior of bonds, currencies, stocks, or stock markets. The Standard and Poors 500 Index is modeled using different neural network classification architectures. Most previous experiments used multilayer perceptrons for stock market forecasting. A multilayer perceptron architecture and a probabilistic neural network are used to predict the incline, decline, or steadiness of the index. The results of trading with the advice given by the network is then compared with the maximum possible performance and the performance of the index. Results show that both networks can be trained to perform better than the index, with the probabilistic neural network performing slightly better than the multi layer perceptron.
Starting Page	72
Ending Page	78
File Size	462363
Page Count	7
File Format	PDF
ISBN	0780332369
DOI	10.1109/CIFER.1996.501826
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-03-24
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stock markets Neural networks Portfolios Multilayer perceptrons Economic forecasting Research and development management Multi-layer neural network Chaos Predictive models Exchange rates
Content Type	Text
Resource Type	Article

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