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Predicting the Short-Term Market Reaction to Asset Specific News: Is Time Against Us?

Robertson, Calum, Geva, Shlomo, & Wolff, Rodney C. (2007) Predicting the Short-Term Market Reaction to Asset Specific News: Is Time Against Us? Emerging Technologies in Knowledge Discovery and Data Mining (LNCS), 4819, pp. 15-26.

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Abstract

The efficient market hypothesis states that investors immediately incorporate all available information into the price of an asset to accurately reflect its value at any given time. The sheer volume of information immediately available electronically makes it difficult for a single investor to keep abreast of all information for a single stock, let alone multiple. We aim to determine how quickly investors tend to react to asset specific news by analysing the accuracy of classifiers which take the content of news to predict the short-term market reaction. The faster the market reacts to news the more cost-effective it becomes to employ content analysis techniques to aid the decisions of traders. We find that the best results are achieved by allowing investors in the US 90 minutes to react to news. In the UK and Australia the best results are achieved by allowing investors 5 minutes to react to news.

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ID Code: 8110
Item Type: Journal Article
Additional Information: For more information, please contact the author. Author contact details: s.geva@qut.edu.au
Additional URLs:
Keywords: Document Classification, Stock Market, News, SVM, C4, 5
DOI: 10.1007/978-3-540-77018-3_3
ISSN: 0302-9743 (Print) 1611-3349 (Online)
Subjects: Australian and New Zealand Standard Research Classification > ECONOMICS (140000) > ECONOMETRICS (140300) > Economic Models and Forecasting (140303)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Artificial Intelligence and Image Processing not elsewhere classified (080199)
Divisions: Current > QUT Faculties and Divisions > QUT Business School
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2007 Springer
Deposited On: 18 Jun 2007
Last Modified: 30 Jun 2014 11:20

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