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Can the Content of Public News be used to Forecast Abnormal Stock Market Behaviour?

Robertson, Calum S., Geva, Shlomo, & Wolff, Rodney C. (2007) Can the Content of Public News be used to Forecast Abnormal Stock Market Behaviour? In Ramakrishnan, Naren, Zaïane, Osmar R., Shi, Yong, Clifton, Christopher W., & Wu, Xindong (Eds.) Seventh IEEE International Conference on Data Mining, 28-31 October, Omaha, Nebraska, United States of America.

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Abstract

A popular theory of markets is that they are efficient: all available information is deemed to provide an accurate valuation of an asset at any time. In this paper, we consider how the content of market-related news articles contributes to such information. Specifically, we mine news articles for terms of interest, and quantify this degree of interest. We then incorporate this measure into traditional models for market index volatility with a view to forecasting whether the incidence of interesting news is correlated with a shock in the index, and thus if the information can be captured to value the underlying asset. We illustrate the methodology on stock market indices for the USA, the UK, and Australia.

Impact and interest:

4 citations in Scopus
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2 citations in Web of Science®

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356 since deposited on 29 Oct 2007
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ID Code: 10458
Item Type: Conference Paper
Keywords: Stock Market, Document Classification, Text Processing
DOI: 10.1109/ICDM.2007.74
ISBN: 9780769530185 ; 0769530184
ISSN: 15504786
Subjects: Australian and New Zealand Standard Research Classification > ECONOMICS (140000) > ECONOMETRICS (140300) > Econometric and Statistical Methods (140302)
Australian and New Zealand Standard Research Classification > ECONOMICS (140000) > ECONOMETRICS (140300) > Time-Series Analysis (140305)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Natural Language Processing (080107)
Divisions: Current > QUT Faculties and Divisions > QUT Business School
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2007 IEEE
Copyright Statement: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Deposited On: 29 Oct 2007
Last Modified: 29 Feb 2012 23:31

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