1. Advances in Machine Learning for Computational Finance
International Workshop, July 20-21 ‘09 London, U.K.
http://web.me.com/davidrh/AMLCF09/Workshop.html
Abstract for Invited Presentation
Identification of Actionable Financial News
Using Machine Learning
David Leinweber, UC Berkeley Haas School of Business, Leinweber & Co.
Jacob Sisk, Leinweber & Co.
Richard W. Brown, Thomson Reuters
William Fang, Thomson Reuters
Many previous studies of the effects on news on the prices of stocks and other securities
have found that price changes often precede the release of a news story. Yet we know
from daily experience that this is not true for all stories, and that market reaction
following some news reports can be large and exploitable.
There are many characteristics of news stories that are potentially useful in distinguishing
“old news” from “predictive news”. These include the type and source of news, subject
and firm characteristics, natural language measurements on content, such as sentiment,
and the context of the story in relation to others, and in relation to quantitative market
information.
Can machine learning methods, supervised and unsupervised, be used to distinguish
stories that are predictive of future price and volatility from those that are not? This talk
presents results from research on this topic sponsored by Thomson Reuters, one of the
world’s largest financial information firms. It draws on an unusually long and deep
history of both news and prices on intraday time scales.
Author Contact Information:
David Leinweber <djl@haas.berkeley.edu>, (corresponding author)
Jacob Sisk <jacob.sisk@gmail.com>,
Richard W. Brown" <richard.w.brown@thomsonreuters.com>,
William Fang <william.fang@thomsonreuters.com>
Rev. April 16, 2009