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Sentiment Analysis
1. MORE THAN WORDS:
QUANTIFYING LANGUAGE TO MEASURE
FIRMS’ FUNDAMENTALS
JOURNAL OF FINANCE, 2008
CONVERSATIONS ON FINANCE
PRESENTED BY:
SAHITHI GADDAM | UDIT GUPTA | JOHN LIU | BEEJAL SHAH
2. SHOULD THIS IMPACT STOCK PRICE?
Source: Wall Street Journal (Oct 23, 2014)
4. AGENDA
Part I.
Motivation For The Study
Part II.
Base Paper - Overview
Case Study
Principal Idea Explored
Testing For Predictability Power
Conclusion
Part III.
Discussion on Present Scenario
5. MOTIVATION FOR THE STUDY
(1/2)
Efficient Markets claim
Firm’s Value = Expected [Present Value (Cash Flows)]
Conditional ‘Expectation’ based on Investor’s Information Set
Investor’s Information Set = Quantitative + Qualitative
Abundant literature studying Quantitative information
However, substantial stock price movements are not explained
by quantitative measures (of firm’s fundamentals)
Qualitative information may help explain stock returns
Firm’s business environment, operations and prospects etc.
6. MOTIVATION FOR THE STUDY
(2/2)
Possible advantages from quantifying language
1) Allows researchers to study the impact of limitless variety of
events (e.g. the Microsoft case)
2) May have incremental explanatory power for future earnings
and returns
If analysts’ forecasts and accounting variables are
incomplete or biased
Using Negative vs. Positive words
Literature in psychology
‘Negative’ words, best summarize the cross-sectional variation
in the word list, as compared to other categories
In the following study - primary focus is negative news
7. BASE PAPER - OVERVIEW
Does ‘language’ predict firms’ ‘accounting earnings’ and
‘stock returns’
Major findings:
1) Negative words in firm-specific news stories forecasts low
earnings
2) Stock prices briefly underreact to the information embedded
in negative words, but incorporate fully with a slight delay
3) Negative words in stories that focus on fundamentals – have
highest predictability power (on earning and return)
Findings suggest: Investors quickly incorporate information
on firms’ fundamentals available in linguistic media, into
stock prices
8. PRINCIPAL IDEA EXPLORED IN THE
PAPER
Principal idea explored:
Can a simple quantitative measure of language be used to
predict individual firm’s earnings and stock returns
If yes, then how to quantify the language used in financial new
stories
Unit of Measure (defined in the paper):
Raw metric: 𝑁 =
𝑛𝑜.𝑜𝑓 𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝑊𝑜𝑟𝑑𝑠
𝑛𝑜. 𝑜𝑓 𝑇𝑜𝑡𝑎𝑙 𝑊𝑜𝑟𝑑𝑠
Standardized metric: 𝑛𝑒𝑔 =
𝑁 − 𝜇 𝑁
𝜎 𝑁
𝐌𝐞𝐭𝐫𝐢𝐜 𝐮𝐬𝐞𝐝 𝐚𝐬 𝐢𝐧𝐝𝐞𝐩𝐞𝐧𝐝𝐞𝐧𝐭 𝐯𝐚𝐫𝐢𝐚𝐛𝐥𝐞: 𝒏𝒆𝒈(−𝟑𝟎,−𝟑)
(i.e. treat all news stories in the [-30,-3] trading day period, prior
to an earnings announcement, as one composite story)
9. CONTENT ANALYSIS METHODOLOGY
Research area:
Qualitative analysis; Natural language processing
Content analysis: Two-step process
Word Category Freq. Value
Alleged Negativ 1/29 1
Abuse Negativ 1/29 1
Worse Negativ 1/29 1
Happy Pstv 0/29 1
Neutral Passive 0/29 1
Step 1:
Mapping
Step 2:
Summarizing
Number of negative words
> 99% of all news articles
Example:
10. DATASET
MEASURING NEGATIVITY
Harvard-IV-4 psychological dictionary to categorize
positive and negative words
Around 12,000 words (rows) and 180 categories
(columns)
Measure negativity by negative word frequency
Standardized fraction of negative words per story
Combine all stories per firm for each trading day to
measure frequency
Source: General Inquirer Website
11. DATASET
FIRMS AND STORIES
1980 to 2004
S&P 500 firms
Represent ¾ of U.S. market capitalization
DJNS and WSJ stories
350,000 stories
100,000,000 words
Stories for 95.8% of S&P 500 firms
Center for Research on Security Prices for stock price data
Institutional Brokers’ Estimates System for analyst forecast
data
Compustat for accounting data
Factiva database for news stories
12. MICROSOFT’S CASE STUDY (1/2)
Second sentence: “The alleged ‘pricing abuse will only get
worse if Microsoft is not disciplined sternly by the antitrust
court,’ said Mark Cooper, director of research for Consumer
Federal of America.”
Hypothesis: fraction of negative words relates to effect of
news on market value
Source: Factiva
1999 DJNS article headline:
13. MICROSOFT’S CASE STUDY (2/2)
Source: Google Finance
Fraction of negative words is in 99th percentile of negative
sentences
Microsoft had irregularly low stock returns around news
story
Cumulative abnormal stock return of -42, -141 and -194
bps for the 3 trading days surrounding the news event
14. TESTING FOR PREDICTABILITY POWER
In order to impact stock returns, at least one relationship
must hold:
1) Negative words predict Earnings (proxy for cash flows)
2) Negative words predict Discount Rates (proxy by returns)
OLS regression tests performed
using different dependent variables and control variables
15. TEST 1 - EARNINGS PREDICTABILITY
Dependent Variable: Two measures of quarterly earnings
used
Standardized Unexpected Earnings (SUE)(1)
- Raw metric: 𝑈𝐸𝑡 = 𝐸𝑡 − 𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑣𝑎𝑙𝑢𝑒 𝑜𝑓 𝐸𝑡
- Standardized metric: 𝑆𝑈𝐸𝑡 =
𝑈𝐸𝑡−µ 𝑈𝐸 𝑡
𝜎 𝑈𝐸 𝑡
Standardized Analysts Forecast Errors (SAFE)
- SAFE =
Median analyst forecast error
σUEt
Control Variables:
lagged earnings, neg-30,-3, size, B/M, trading volume, recent
stock returns (3 measures), analyst forecast revision(2) etc.
Winsorize SUE & all analysts forecast variables at 1% level
Similar results from both ‘SUE’ and ‘SAFE’
Note: (1) Based on Bernard and Thomas (1989), who use a seasonal random walk with trend model for
each firm’s earnings. (2) Using Chan et. al. (1996) methodology.
16. TEST 1 - EARNINGS PREDICTABILITY
MAIN RESULT
All 6 estimates are significant at 99% level
Several control variables also exhibit strong explanatory power, as expected
Predictability is robust even when using ‘before forecasts’ news stories
Note: Table presented above has been truncated, and does not include all control variables. Please
refer to the appendix for complete details.
17. TEST 2 - RETURN PREDICTABILITY
Following two ideas are tested
Return predictability in daily returns
Is there a trading strategy possible off this under-reaction?
Considerations:
Data at daily frequency
Dependent variable: return based on closing price (t=0 & t=1)
Cut-off time: DJNS (up to 3:30pm), WSJ (same day)
Control Variables - Earnings, size, B/M, trading volume, recent
stock returns (5 measures)
18. TEST 2 - RETURN PREDICTABILITY
DAILY RETURNS, MAIN RESULT
neg robustly predicts slightly lower returns on the following trading day
neg coeff. is significant in 4 cases (where DJNS data in included)
Coeff. for DJNS source is higher, as compared to WSJ
Low R2, as expected in efficient markets (< 0.0026)
Note: Table presented above has been truncated, and does not include all control variables. Please
refer to the appendix for complete details.
19. TEST 2 - RETURN PREDICTABILITY
TRADING STRATEGY
Two equal weighted portfolios – constructed by ranking firms on the
basis of positive/ negative news
Long-short strategy, with daily rebalancing
Cumulative raw returns would be 21.1% per year (no trading costs)
Strategy will not be profitable if trading cost is considered
Note: Standard errors calculated using White (1980) heteroskedasticity-consistent covariance matrix
approach.
20. IS THERE A SUBSET OF NEWS
WITH BETTER PREDICTABILITY
Hypothesis:
Negative words in news stories containing word-stem ‘earn’
have better predictability
Results Expected:
Better earnings predictability
Stronger contemporaneous relationship with returns
Magnitude of under-reaction should be greater
21. ADDING NEW INDEPENDENT VARIABLES
Regression (similar to previous case):
Add 2 new independent variables to capture specific effects
‘Fund-30,-3’: words in stories containing word-stem ‘earn’,
divided by total words across all stories
Interaction term: neg-30,-3 * Fund-30,-3
Not
News Stories
Not “About” Firm
Fundamentals
“About” Firm
Fundamentals
neg-30,-3
Fund-30,-3
22. REPEATING REGRESSIONS WITH
TWO ADDITIONAL VARIABLES
Coeff. of both new terms is strongly negative and significant
Interaction: negative words in earnings-related stories are much better predictors
Note: Tables presented above has been truncated, and does not include all control variables. Please
refer to the appendix for complete details.
Strong contemporaneous relationship exists
5x larger response from negative words in earnings-related stories
23. CONCLUSION
More than ‘negative’ words
Contain valuable information
Forecast low earnings
Return Predictability
Slight delay in reaction to negative news
Predictability in t+1 day return
‘Simple’ trading strategy does not exist
Words from specific type of news carry more information
Negative words from earnings related stories are better
predictors
24. POWER OF SOCIAL MEDIA:
THE HASH CRASH INCIDENT
Demonstrated social media’s potential to move markets
Dow Jones fell more than 150 points, the price of crude oil
plummeted, and US bond yields dropped, briefly wiping $121
billion off the value of companies in the S&P 500 index, before
recovering minutes later
25. 1. ‘SNTMNT’- OVERVIEW
Launched an API to monitor Twitter-based stock sentiment
World’s first API that makes predictions based about future stock
price movement for all stocks in the S&P 500
Accuracy as high as 60%, averaging at 54% (company estimates)
Should traders rely on Twitter sentiment alone for their trades?
Signal-to-noise ratio on social media channels is too low to provide
standalone trading signals, but definitely high enough to provide
an innovative trading indicator
Based on work of Professor Johan Bollen, an academic who found
correlations between the stock market and activity on Twitter
27. 2. SOCIAL MARKET ANALYTICS -
METHODOLOGY
Social Market Analytics produces a family of metrics, called
S-Factors – designed to capture the signature of financial market
sentiment
SMA applies these metrics to data captured from social media
sources to estimate sentiment for indices, sectors, and individual
securities
28. SOME USEFUL ONLINE RESOURCES
IBM’s Watson and the Jeopardy! Challenge:
https://www.youtube.com/watch?v=P18EdAKuC1U
Free online course on ‘Natural Language Processing’,
offered on Coursera by Stanford University:
https://www.coursera.org/course/nlp
General Inquirer website:
http://www.wjh.harvard.edu/~inquirer/
IBM Publications:
http://researcher.watson.ibm.com/researcher/view_group.
php?id=147
List of words in spreadsheet format:
http://www.wjh.harvard.edu/~inquirer/spreadsheet_guide.
htm
Princeton’s WordNet: http://wordnet.princeton.edu/
29. REFERENCES
Tetlock, Paul C., Maytal Saar-Tsechansky, and Sofus Mackassy.
2008. “More than words: Quantifying language to
measure firms’ fundamentals.” The Journal of Finance 63
Issue 3 p. 1437-1467.
“Welcome to the General Inquirer Home Page.” Web. 6 April
2015. < http://www.wjh.harvard.edu/~inquirer/
spreadsheet_guide.htm>.
30. QUESTIONS AND DISCUSSION
Can you think of other examples in which any news had an
impact on returns?
Do you think big firms are using such analysis for alpha
generation?