Machine Learning Techniques and Use of Event Information for Stock Market Prediction: A Survey and Evaluation

Authors:
Paul D. Yoo;Maria H. Kim;Tony Jan
Affiliations:
University of Technology, Sydney, Australia;University of Technology, Sydney, Australia;University of Technology, Sydney, Australia
Venue:
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-2 (CIMCA-IAWTIC'06) - Volume 02
Year:
2005

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Abstract

This paper surveys machine learning techniques for stock market prediction. The prediction of stock markets is regarded as a challenging task of financial time series prediction. In this paper, we present recent developments in stock market prediction models, and discuss their advantages and disadvantages. In addition, we investigate various global events and their issues on predicting stock markets. From this survey, we found that incorporating event information with prediction model plays very important roles for more accurate prediction. Hence, an accurate event weighting method and a stable automated event extraction system are required to provide better performance in financial time series prediction.