Mining of Concurrent Text and Time Series

V. Lavrenko, M. Schmill, D. Lawrie, P. Ogilvie, D. Jensen, J. Allan. 2000. Mining of concurrent test and time series. CIIR Technical Report IR-203. University of Massachusetts Amherst.

Abstract

We present a unique approach to identifying new stories that influence the behavior of financial markets. We describe the design and implementation of AEnalyst, a system for predicting trends in stock prices based on the content of news stories that precede the trends. We identify trends in time series using piecewise linear fitting an d then assign labels to the trends according to an automated beginning procedure. We use language models to represent patterns of language that are highly associated with particular labeled trends. AEnalyst can identify news stories that are highly indicative of future trends. We evaluate the system in terms of its ability to predict forthcoming trends in the stock prices. We perform a market simulation, demonstrating that AEnalyst is capable of producing profits that are significantly higher than random.

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