Title

Forecasting The New York Stock Exchange Composite Index With Past Price And Interest Rate On Condition Of Volume Spike

Keywords

Data mining; Financial expert system; Market efficiency; Neural networks; Security market forecasting

Abstract

We identify trading volume spikes through use of the template matching technique from statistical pattern recognition. For those trading days meeting the condition signifying volume spike recognition, application of linear regression models the future change in price using historical price and prime interest rate values. Also, we train a nonlinear neural network model and use it as a basis for simulated trading, which includes consideration of transaction costs and cash dividends. We illustrate and test with New York Stock Exchange Composite Index data for the period from 1981 to 1999. Results are positive, robust, systematic, economically significant, and informative as to the role of trading volume in the stock market mechanism. © 2004 Elsevier Ltd. All rights reserved.

Publication Date

1-1-2005

Publication Title

Expert Systems with Applications

Volume

28

Issue

1

Number of Pages

1-8

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.eswa.2004.08.001

Socpus ID

9244248635 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/9244248635

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