Does summarization help stock prediction? A news impact analysis
Author(s)
Wang, Philips Fu Lee
Xie, Haoran
Author(s)
Li, X.
Song, Y.
Zhu, S.
Li, Q.
Date Issued
2015
Publisher
IEEE
Journal
IEEE Intelligent Systems
Volume
30
Issue
3
Start page
26
End page
34
Abstract
The authors study the problem of how news summarization can help stock price prediction, proposing a generic stock price prediction framework to enable the use of different external signals to predict stock prices. Experiments were conducted on five years of Hong Kong Stock Exchange data, with news reported by Finet; evaluations were performed at individual stock, sector index, and market index levels. The authors' results show that prediction based on news article summarization can effectively outperform prediction based on full-length articles on both validation and independent testing sets.
SFU Affiliated Publication
Yes
Availability at SFU Library
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