Title

An Analysis Of A Hybrid Neural Network And Pattern Recognition Technique For Predicting Short-Term Increases In The Nyse Composite Index

Keywords

Efficient markets hypothesis; Financial decision support; Heuristics; Neural networks; Pattern recognition; Stock market forecasting; Technical analysis

Abstract

We introduce a method for combining template matching, from pattern recognition, and the feed-forward neural network, from artificial intelligence, to forecast stock market activity. We evaluate the effectiveness of the method for forecasting increases in the New York Stock Exchange Composite Index at a 5 trading day horizon. Results indicate that the technique is capable of returning results that are superior to those attained by random choice. © 2002 Elsevier Science Ltd. All rights reserved.

Publication Date

2-27-2002

Publication Title

Omega

Volume

30

Issue

2

Number of Pages

69-76

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/S0305-0483(01)00057-3

Socpus ID

0036167958 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0036167958

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