An analysis of a hybrid neural network and pattern recognition technique for predicting short-term increases in the NYSE composite index

W. Leigh, M. Paz and R. Purvis

Omega, 2002, vol. 30, issue 2, 69-76

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.

Keywords: Stock; market; forecasting; Neural; networks; Pattern; recognition; Heuristics; Financial; decision; support; Efficient; markets; hypothesis; Technical; analysis (search for similar items in EconPapers)
Date: 2002
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Citations: View citations in EconPapers (8)

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