Language models for financial news recommendation

  • Lavrenko V
  • Schmill M
  • Lawrie D
 et al. 
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Abstract

We present a unique approach to identifying news stories that influence the behavior of financial markets. Specifically we descibe the design and implementation of AEnalyst, a system that can recommend intersting news stories - stories that are likely to affect market behavior. AEnalyst operates by correlating the content of news stories with trends in financial time series. We identify trends in time series using piecewise linear fitting and then assign labels to the trends according to an automated binning proedure. We use language models to represent patterns of language that are highly associated with particular labeled trends. AEnalyst can identify and recomend news stories that are highly indicative of future trends. We evaluate the system in terms of its ability to recommend the stories that will affect the behavior of the stock market. We demonstrate that stories recommended by AEnalyst could be used to profitably predict forthcoming trends in stock prices.

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Authors

  • Victor Lavrenko

  • Matt Schmill

  • Dawn Lawrie

  • Paul Ogilvie

  • David Jensen

  • James Allan

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