Introduction
Methodology
Results
Conclusions
Which News Moves Stock Prices?
A Textual Analysis
Jacbob Boudoukh – IDC
Ronen Feldman – Hebrew University
Shimon Kogan – University of Texas & IDC
Matthew Richardson – NYU & NBER
October, 2013 – Q Group Fall Seminar
Boudoukh, Feldman, Kogan, Richardson
Which News Moves Stock Prices?
Introduction
Methodology
Results
Conclusions
Motivation
Basic tenet of rational asset pricing:
returns are a function of new information
Supporting evidence came mainly from event studies
Ball Brown (1968) on earnings
Fama Fisher Jensen Roll (1969) on splits
...
Roll’s (1988) presidential address showed a weak link
between news and prices, along with
Shiller (1981)
Cutler, Poterba, and Summers (1989)
...
Boudoukh, Feldman, Kogan, Richardson
Which News Moves Stock Prices?
Introduction
Methodology
Results
Conclusions
Main Results
Roll posits that if information moves prices
R 2 (AllDays) << R 2 (NoNewsDays) ⇡ 100%
however, he finds that
R 2 (AllDays) = R 2 (NoNewsDays) = 20%,
while
Our story: the long-standing puzzle is a result of using
poor proxies for true relevant news
We exploit recent innovations in textual analysis to show
that
when able to identify true relevant news and their
importance, news matter!
different forms of news give rise to different patterns of
continuations and reversals
Boudoukh, Feldman, Kogan, Richardson
Which News Moves Stock Prices?
Introduction
Methodology
Results
Conclusions
Standard Approach
Standard approach towards identification of publicly released
news (e.g., Tetlock (2007)) is to
Obtain text data (e.g., news articles)
Link text with firms through name/ticker mention
Identify the tone of the text by signing each word with a
predetermined dictionary (e.g., positive vs. negative) and
summing up signs
Boudoukh, Feldman, Kogan, Richardson
Which News Moves Stock Prices?
Introduction
Methodology
Results
Conclusions
The Stock Sonar (TSS)
A proprietary information extraction platform specific to
finance, available freely on the web and through Dow
Jones
Differs from existing methodologies in the finance
literature:
1
2
3
Uses a dictionary but refines IV-4/Loughran&McDonald
(2011) to include sentiment modifiers, e.g., “highly” versus
“mostly”
Operates at the phrase level, not just on word, e.g., double
negatives (e.g., “reducing losses”), connectors (e.g.,
“despite”)
Sorts through the document and parses out the meaning in
the context of possible events relevant to companies, and
determines which companies map to the events.
Boudoukh, Feldman, Kogan, Richardson
Which News Moves Stock Prices?
Introduction
Methodology
Results
Conclusions
TSS Example
Boudoukh, Feldman, Kogan, Richardson
Which News Moves Stock Prices?
Introduction
Methodology
Results
Conclusions
Event List
!"#$%&%'%()
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9)45:8/+6"10@@")(4/&0)
=#"(&/+,6+%"A/+64/&)$
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*"1>)&145+9)45:8&8
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=0)/#41/+,6+9$#""@")/8
;<15B8&'&/:
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:19,(-10)'
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K0#2-0#1"
*%)+)"%+,
%&'&(")(
./012+3&)4)1&45
6"70#/8
30#"148/8
!"/#&1+=>4)$"
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304+,
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I7(4/"8+,6+I7$#4("8
Boudoukh, Feldman, Kogan, Richardson
Which News Moves Stock Prices?
Introduction
Methodology
Results
Conclusions
Data
Variances and R 2 s
Return Predictability
Complexity
Data Description
Dow Jones Newswire articles (with at least 50 words)
S&P500 firms (as of beginning of each year), 2000-2009
For each article, we obtain
1
2
3
4
Ticker(s)
Event(s)
Tone on a scale of ( 1, +1)
Time stamp
Restructure the data to follow <ticker-date> format, using a
1530 cutoff time
Boudoukh, Feldman, Kogan, Richardson
Which News Moves Stock Prices?
Introduction
Methodology
Results
Conclusions
Data
Variances and R 2 s
Return Predictability
Complexity
Summary Stats
Unid News: news coverage days without any corporate events
Iden News: news coverage days with a corporate event
Boudoukh, Feldman, Kogan, Richardson
Which News Moves Stock Prices?
Introduction
Methodology
Results
Conclusions
Data
Variances and R 2 s
Return Predictability
Complexity
Event Frequency
1
1
Stock and year assignments
Boudoukh, Feldman, Kogan, Richardson
Which News Moves Stock Prices?
Introduction
Methodology
Results
Conclusions
Data
Variances and R 2 s
Return Predictability
Complexity
Variance Ratios
Variance Ratios show more formally that when we identify
news, news matter for stock returns
Boudoukh, Feldman, Kogan, Richardson
Which News Moves Stock Prices?
Introduction
Methodology
Results
Conclusions
Data
Variances and R 2 s
Return Predictability
Complexity
Roll R 2
We go back to Roll’s (1988) analysis: do factor models’ R 2
change across days with and without news?
He posits:
returns on no-news days should be dominated by
systematic risk factors
... while returns on news days should not be
Roll finds R 2 s that are similar on both types of days!
We follow a similar procedure using both 1 and 4 factor
models (including size, value, and momentum factors)
Boudoukh, Feldman, Kogan, Richardson
Which News Moves Stock Prices?
Introduction
Methodology
Results
Conclusions
Data
Variances and R 2 s
Return Predictability
Complexity
Roll R 2 Results
For Unid News days, Roll’s results hold, but not for Iden News
days!
Boudoukh, Feldman, Kogan, Richardson
Which News Moves Stock Prices?
Introduction
Methodology
Results
Conclusions
Data
Variances and R 2 s
Return Predictability
Complexity
Daily Tone
Comparing TSS with IV4:
Correlation is positive but far from 1
TSS mean vary more across event types
TSS spread is larger within an event type
Boudoukh, Feldman, Kogan, Richardson
Which News Moves Stock Prices?
Introduction
Methodology
Results
Conclusions
Data
Variances and R 2 s
Return Predictability
Complexity
Roll R 2 with Tone
Including tone improves model’s R 2 by 12%
Boudoukh, Feldman, Kogan, Richardson
46%.
Which News Moves Stock Prices?
Introduction
Methodology
Results
Conclusions
Data
Variances and R 2 s
Return Predictability
Complexity
Return Predictability
Evidence so far supportive of market efficiency
Behavioral finance predicts:
“real” news ) continuation (i.e., partial adjustment)
“fluff” news ) reversal (i.e., over reaction)
Big literature on this:
Stickel and Verrachia (1994), Daniel, Hirshleifer and
Subrahmanyam (1998), Hirshleifer (2000), Pritamani and
Singal (2001), Chan (2003), Vega (2006), Barber and
Odean (2008), Gutierrez and Kelley (2008), Tetlock,
Sarr-Tsechansky, and MacsKassy (2008), Tetlock (2010,
2011)...
Our ability to differentiate real vs. fluff, offers a special
opportunity
Boudoukh, Feldman, Kogan, Richardson
Which News Moves Stock Prices?
Introduction
Methodology
Results
Conclusions
Data
Variances and R 2 s
Return Predictability
Complexity
Return-Based Predictability
Each day, sign stocks based on day t return
large positive move (>+ s.d.)
large negative move (<- s.d.)
Strategy consists of
long $1 across all large positive move stocks
short $1 across all large negative move stocks
Hold through the end of the following day
Boudoukh, Feldman, Kogan, Richardson
Which News Moves Stock Prices?
Introduction
Methodology
Results
Conclusions
Data
Variances and R 2 s
Return Predictability
Complexity
Reversals and Continuations
Reversal!
Continuation!
We observe reversals following No News days and
continuations following Iden News days
Boudoukh, Feldman, Kogan, Richardson
Which News Moves Stock Prices?
Introduction
Methodology
Results
Conclusions
Data
Variances and R 2 s
Return Predictability
Complexity
Reversals and Continuations
Iden News Cont - Score!
Iden News Cont + Score!
Focusing on Iden News days only, we document a strong
tone-based return predictability
Boudoukh, Feldman, Kogan, Richardson
Which News Moves Stock Prices?
Introduction
Methodology
Results
Conclusions
Data
Variances and R 2 s
Return Predictability
Complexity
Complexity
Look for days/stocks with complex information structure
more than 2 Iden articles (perhaps with similar events)
more than 2 Iden events (perhaps in one or two articles)
sentiment score dispersion (same event but different tone)
Boudoukh, Feldman, Kogan, Richardson
Which News Moves Stock Prices?
Introduction
Methodology
Results
Conclusions
Data
Variances and R 2 s
Return Predictability
Complexity
Complexity and Volatility
Complex days are associated with much higher volatility
Boudoukh, Feldman, Kogan, Richardson
Which News Moves Stock Prices?
Introduction
Methodology
Results
Conclusions
Data
Variances and R 2 s
Return Predictability
Complexity
Complexity and R 2 s
R 2 spread is even more pronounced (⇥ 3 instead of ⇥ 2)
Boudoukh, Feldman, Kogan, Richardson
Which News Moves Stock Prices?
Introduction
Methodology
Results
Conclusions
Data
Variances and R 2 s
Return Predictability
Complexity
Trading on Complexity
Complexity-based strategies yield substantial before-costs
alphas (⇡ 45% per year)
Boudoukh, Feldman, Kogan, Richardson
Which News Moves Stock Prices?
Introduction
Methodology
Results
Conclusions
Conclusions
When you can identify news and how important it is, news
matters!
Results are stronger for complex days
Events identification allows us to better understand when
stock prices
over-react – no / “fluff” information
under-react – “real” / complex information
Boudoukh, Feldman, Kogan, Richardson
Which News Moves Stock Prices?