Mining of Concurrent Text and Time Series (2000)
Cached
Download Links
| Venue: | IN PROCEEDINGS OF THE 6 TH ACM SIGKDD INT'L CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING WORKSHOP ON TEXT MINING |
| Citations: | 30 - 0 self |
BibTeX
@INPROCEEDINGS{Lavrenko00miningof,
author = {Victor Lavrenko and Matt Schmill and Dawn Lawrie and Paul Ogilvie and David Jensen and James Allan},
title = {Mining of Concurrent Text and Time Series},
booktitle = {IN PROCEEDINGS OF THE 6 TH ACM SIGKDD INT'L CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING WORKSHOP ON TEXT MINING},
year = {2000},
pages = {37--44},
publisher = {}
}
Years of Citing Articles
Bookmark
 |
 |
 |
 |
 |
 |
OpenURL
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, demonstrate that AEnalyst is capable of producing profits that are significantly highly than random.
