Forecasting the nyse composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: a case study in romantic decision support

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Decision Support Systems 32 (2002) 361 – 377

Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: a case study in romantic decision support
William Leigh a,*, Russell Purvis b, James M. Ragusa a

Department of Management Information Systems, College of Business, University of Central Florida,Orlando, FL 32816 1400, USA b Clemson University, Clemson, SC, USA Received in revised form 1 February 2001; accepted 1 August 2001

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 (orromantic) 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 economicfundamentals 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 machinelearning 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. D 2002 Elsevier Science B.V. All rights reserved.Keywords: Technical analysis; Neural networks; Forecasting; Genetic algorithms; Pattern recognition; Heuristics; Financial decision support; Market efficiency

1. Introduction This new century opens on an unprecedented availability and selection of development tools for building decision support systems [4]. These tools


Corresponding author. Fax: +1-407-823-5741. E-mail (W. Leigh).

have reduced the complexity and long development time inherent in building systems that offer valuable insights into the complex problems offered in today’s business world. But will these technological enhancements manifest into systems that exploit the vast opportunities that are now available? This century also promises to be a time of discontinuous and increasinglyrapid change, with new risks taking the place of ones we understand. Time pressures and the rush of events will require that

0167-9236/02/$ - see front matter D 2002 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 7 - 9 2 3 6 ( 0 1 ) 0 0 1 2 1 - X


W. Leigh et al. / Decision Support Systems 32 (2002) 361–377

decision support tools be used in an efficient, unified andadaptive manner. This will require satisficing with good results, often without understanding ‘‘scientifically’’ the underlying decision contexts that we analyze. Decision support systems, however, cannot meet these opportunities without changing the way the systems are approached and built. Indeed, a bolder, more eclectic style will be necessary, which we term romantic. Classical connotes beauty ofform, good taste, restraint, and clarity. Romantic is extravagant, wild, free, imaginative, and fantastic, and is in revolt from that which is classical. We contend that in the early 21st century, the classical style in decision support practice will be supplanted by a more romantic style. A romantic style for decision support combines seemingly disparate sets of theories, data, and techniques,...
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