Machine learning techniques and use of event information for stock market prediction: A survey and evaluation

Authors

Paul D. Yoo, University of Technology, Sydney
Maria H. Kim, University of WollongongFollow
Tony Jan, University of Technology, Sydney

RIS ID

38833

Publication Details

Yoo, P. D., Kim, M. H. & Jan, T. (2005). Machine learning techniques and use of event information for stock market prediction: A survey and evaluation. International Conference on Computational Intelligence for Modeling, Control and Automation (CIMCA 2005) (pp. 835-841). Piscataway, NJ: IEEE.

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.