that activity. News gathering is increasingly supported by automation that monitors a
large and growing subset of the web and information in proprietary databases. There are
plenty of places to find potentially investment-relevant text. Consider three broad
classifications:
1.News News was once exclusively disseminated on paper, radio, television, teletype
‘‘wire’’, fax, and eventually via dedicated electronic feeds. It is now ubiquitous on the
web, and news vendors have moved dramatically upscale, with richly tagged news
suitable for ‘‘quantextual’’ investment and trading strategies.
2.Pre-news Pre-news is the raw material reporters read before they write news. It
comes from primary sources, the originators themselves: the Securities and Exchange
Commission (SEC), court documents, and other government agencies. This also
includes corporate sources, reputable blogs, and specialized news. At Thomson
Reuters, the news-gathering process has been aggressively automated to allow faster
reliable transformation of pre-news to news using a variety of IA (intelligence ampli-
fication) and AI (artificial intelligence) methods such as language translation and
entity extraction.
3.Social media The barriers to entry at the low end of the ‘‘news’’ business on the web
are vanishingly small. Anyone can tweet, create a blog, or post on message boards for
stocks or other topics. A great deal of this is genuinely useful—think of the product
reviews on Amazon—and some is just noise. On stock message boards, there have
been CEOs who reveal valuable information; but, for the most part, the typical
posting still reads like it came from some guy on vodka number nine.
This chapter deals only with the first category of text: news. Progress in news-gathering
automation moves pre-news to news faster and in greater volume. The methods
described here for news can also be applied to user-provided text. Detailed discussions
of this and related topics are found in the bookNerds on Wall Street: Math, Machines
and Wired Markets, particularly Ch. 9, ‘‘The text frontier’’ (Leinweber, 2009).
6.2 Previous work
6.2.1 Behavioral basis
How investors and traders respond to news is of ongoing interest in behavioral finance.
Ideas of attention and repetition, well known in advertising, have been explored in
previous work. There is a substantial amount of prior research in this area.
In ‘‘All that glitters: The effect of attention and news on the buying behavior of
individual and institutional investors,’’ Barber and Odean (2008) ‘‘confirm the hypoth-
esis that individual investors are net buyers of attention-grabbing stocks, e.g., stocks in
the news, stocks experiencing high abnormal trading volume, and stocks with extreme
one day returns.’’
In ‘‘Stock price reaction to news and no-news: Drift and reversal after headlines,’’
Chan (2003) compares return patterns for stocks with and without news and finds major
differences between the two sets. These persist even when earnings-related news (a
traditional quant analytic) is removed. Consistent with our expectations based on
investor attention, these effects are larger for smaller capitalization firms, an effect also
seen in this chapter.
150 News and abnormal returns