Mining of Concurrent Text and Time Series

Victor P. Lavrenko · Matt Schmill · Dawn Lawrie · Paul Ogilvie · David D. Jensen

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Time Series Analysis and Forecasting

Abstract

ABSTRACT We present a unique approach to identifying news 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 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. AEnalyst can then 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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