Title
Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: a case study in romantic decision support
Abbreviated Journal Title
Decis. Support Syst.
Keywords
technical analysis; neural networks; forecasting; genetic algorithms; pattern recognition; heuristics; financial decision support; market; efficiency; MARKETS; CLASSIFICATION; PREDICTIONS; SELECTION; DOMAINS; TIME; Computer Science, Artificial Intelligence; Computer Science, Information; Systems; Operations Research & Management Science
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. (C) 2002 Elsevier Science B.V. All rights reserved.
Journal Title
Decision Support Systems
Volume
32
Issue/Number
4
Publication Date
1-1-2002
Document Type
Article
Language
English
First Page
361
Last Page
377
WOS Identifier
ISSN
0167-9236
Recommended Citation
"Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: a case study in romantic decision support" (2002). Faculty Bibliography 2000s. 3312.
https://stars.library.ucf.edu/facultybib2000/3312
Comments
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