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

Yoo, Paul and Kim, M.H. and Jan, T. (2005) Machine learning techniques and use of event information for stock market prediction: a survey and evaluation. In: UNSPECIFIED (ed.) CIMCA 2005: International Conference on Computational Intelligence for Modelling Control and Automation. IEEE Computer Society, pp. 835-841. ISBN 9780769525040.

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Official URL: https://doi.org/10.1109/CIMCA.2005.1631572

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

Metadata

Item Type:	Book Section
School:	Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
Depositing User:	Sarah Hall
Date Deposited:	25 Oct 2021 15:57
Last Modified:	09 Aug 2023 12:52
URI:	https://eprints.bbk.ac.uk/id/eprint/46474

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