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

Yoo, PD; Kim, MH; Jan, T

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

Yoo, PD Kim, MH Jan, T

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet, 2005, 2 pp. 835 - 841
Issue Date:: 2005-12-01

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Field	Value	Language
dc.contributor.author	Yoo, PD	en_US
dc.contributor.author	Kim, MH	en_US
dc.contributor.author	Jan, T	en_US
dc.date.issued	2005-12-01	en_US
dc.identifier.citation	Proceedings - International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet, 2005, 2 pp. 835 - 841	en_US
dc.identifier.isbn	0769525040	en_US
dc.identifier.isbn	9780769525044	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10919
dc.description.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. © 2005 IEEE.	en_US
dc.relation.ispartof	Proceedings - International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet	en_US
dc.title	Machine learning techniques and use of event information for stock market prediction: A survey and evaluation	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
utslib.for	080104 Computer Vision	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	080105 Expert Systems	en_US
dc.location.activity	Vienna, AUSTRIA	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	2	en_US

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. © 2005 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/10919