Surveying stock market forecasting techniques - Part II: Soft computing methods

Authors:
George S. Atsalakis;Kimon P. Valavanis
Affiliations:
Department of Production Engineering and Management, Technical University of Crete, University Campus, Kounoupidiana, Chania 73100, Greece;Department of Computer Science and Engineering University of South Florida, Tampa, FL 33620, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

The key to successful stock market forecasting is achieving best results with minimum required input data. Given stock market model uncertainty, soft computing techniques are viable candidates to capture stock market nonlinear relations returning significant forecasting results with not necessarily prior knowledge of input data statistical distributions. This paper surveys more than 100 related published articles that focus on neural and neuro-fuzzy techniques derived and applied to forecast stock markets. Classifications are made in terms of input data, forecasting methodology, performance evaluation and performance measures used. Through the surveyed papers, it is shown that soft computing techniques are widely accepted to studying and evaluating stock market behavior.