Abstract
Given a corpus of financial news items labelled according to the market reaction following their publication, we investigate ‘cotemporeneous’ and forward-looking price stock movements. Our approach is to provide a pool of relevant textual features to a machine learning algorithm to detect substantial stock price variations. Our two working hypotheses are that the market reaction to a news item is a good indicator for labelling financial news items, and that a machine learning algorithm can be trained on those news items to build models detecting price movement effectively.
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Notes
- 1.
- 2.
This list is augmented by the words up, down, above and below to follow [7].
- 3.
The corpus was a random selection of texts from Yahoo, Motley Fool and other financial sites.
- 4.
Sanjiv Das, personal communication.
- 5.
- 6.
Wordnet synset number 100005598: causal agency#n#1, cause#n#4 and causal agent#n#1
- 7.
Using the PERL package [13].
- 8.
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Généreux, M., Poibeau, T., Koppel, M. (2011). Sentiment Analysis Using Automatically Labelled Financial News Items. In: Ahmad, K. (eds) Affective Computing and Sentiment Analysis. Text, Speech and Language Technology, vol 45. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-1757-2_9
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