Title

Forecasting The Nyse Composite Index With Technical Analysis, Pattern Recognizer, Neural Network, And Genetic Algorithm: A Case Study In Romantic Decision Support

Keywords

Financial decision support; Forecasting; Genetic algorithms; Heuristics; Market efficiency; Neural networks; Pattern recognition; Technical analysis

Abstract

The 21st century is seeing technological advances that make it possible to build more robust and sophisticated decision support systems than ever before. But the effectiveness of these systems may be limited if we do not consider more eclectic (or romantic) options. This paper exemplifies the potential that lies in the novel application and combination of methods, in this case to evaluating stock market purchasing opportunities using the "technical analysis" school of stock market prediction. Members of the technical analysis school predict market prices and movements based on the dynamics of market price and volume, rather than on economic fundamentals such as earnings and market share. The results of this paper support the effectiveness of the technical analysis approach through use of the "bull flag" price and volume pattern heuristic. The romantic approach to decision support exemplified in this paper is made possible by the recent development of: (1) high-performance desktop computing, (2) the methods and techniques of machine learning and soft computing, including neural networks and genetic algorithms, and (3) approaches recently developed that combine diverse classification and forecasting systems. The contribution of this paper lies in the novel application and combination of the decision-making methods and in the nature and superior quality of the results achieved. © 2002 Elsevier Science B.V. All rights reserved.

Publication Date

3-1-2002

Publication Title

Decision Support Systems

Volume

32

Issue

4

Number of Pages

361-377

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/S0167-9236(01)00121-X

Socpus ID

0036498492 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0036498492

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