We present a unique approach to identifying news stories that influence the behavior of financial markets. Specficially, we describe the design and implementation of e-analyst, a system that can recommend interesting news stories - stories that are likely to affect market behavior. e-analyst 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 procedure. We use language models to represent patterns of language that are highly associated with particular labeled trends. e-analyst can then identify and recommend 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 e-analyst could be used to profitably predict forthcoming trends in stock prices.
Mendeley saves you time finding and organizing research
Choose a citation style from the tabs below