NDLI: Neural networks for financial market prediction

Content Provider	IEEE Xplore Digital Library
Author	Chen, C.H.
Copyright Year	1994
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Massachusetts Univ., North Dartmouth, MA, USA (Chen, C.H.)
Abstract	The use of neural networks in financial market prediction presents a major challenge to the design of effective neural network predictors and classifiers. In this paper, the author examines several neural networks to evaluate their capability in prediction and in trend estimation which is treated as a classification problem. The networks considered are the backpropagation trained network (BPN), the general regression neural network (GRNN), the class-sensitive neural network (CSNN), and the conjugate gradient trained network (CGNN). It is concluded that CSNN is among the best performing networks in both prediction and trend estimation. All major indicators are evaluated by the neural networks. It is found that the use of good indicators like the rate of change, momentum, moving average, etc. can lead to about 5% improvement over the case that no indicator is used. Momentum computed from the previous 14 day data is the best single indicator. The complexity of the financial market probably explains why the large number of indicators cannot provide any significant improvement in network classification.
Starting Page	1199
Ending Page	1202
File Size	326725
Page Count	4
File Format	PDF
ISBN	078031901X
DOI	10.1109/ICNN.1994.374354
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-06-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Artificial neural networks Stock markets Backpropagation Humans Artificial intelligence Pattern analysis Time series analysis Pattern recognition Training data
Content Type	Text
Resource Type	Article

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