fake news in financial markets - gsb.columbia.edu · •we have 171 for-sure fake and 334 non-fake...
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Fake News in Financial MarketsShimon Kogan
(MIT Sloan School of Management, IDC)
Tobias J. Moskowitz
(Yale School of Management, NBER, AQR)
Marina Niessner
(Yale School of Management)
Detecting fake news is hard:
Detecting fake news is hard:
A study by Sam Wineburg et. al. (2016) from Stanford, assesses students’ ability to distinguish ads from articles, neutral sources from biased ones and fake accounts from real ones.
The authors described the results as “dismaying,” “bleak” and “threat to democracy.”
Does Fake News matter in Finance?
While the existence of fake new may be problematic in certain settings, it is
not clear it matters for financial markets
In a perfectly efficient financial securities market, where the cost of
information approaches zero, misinformation will have no impact
Does Fake News matter in Finance?
While the existence of fake new may be problematic in certain settings, it is
not clear it matters for financial markets
In a perfectly efficient financial securities market, where the cost of
information approaches zero, misinformation will have no impact
How prevalent are fake news on knowledge sharing platforms?
What effect do fake news have on financial markets?
Detecting Lying
How can we detect lying?
• Polygraph tests (about 60-65% accuracy)
• Could try linguistic methods. Do we speak differently when we lie?
Detecting Lying
How can we detect lying?
• Polygraph tests (about 60-65% accuracy)
• Could try linguistic methods. Do we speak differently when we lie?
It turns out the answer is yes:
• When people lie they use fewer self-references (I-words)
• They also tend to use more discrepancy verbs, like could, that assert that an event might have occurred, but possibly didn't
Detecting Lying
Our Paper
Use a linguistic measure of authenticity developed by James Pennebackerand co-authors (LIWC)
Obtain fake news articles that were published on knowledge sharing platforms, like Seeking Alpha, to test the measure in financial setting
Estimate the prevalence of fake news on financial information sharing platforms, and which authors
Examine the effects of fake news on returns and insider trading
Data
Knowledge Sharing Platforms
Seeking Alpha:
A moderated, crowd-sourced content service for financial markets
Was founded in 2004
Have 7 million unique visitors generating 40 million visits monthly (as of 2017)
Our data is 2005-2015, and contains 203,545 articles
Motley Fool:
Financial-services company that provides financial advice for investors through various stock, investing, and personal finance services
Was founded in July 1993
Alexa’s rank in the US is 631 (vs. 546 for Seeking Alpha)
Our data is 2009-2014 , and contains 147,916 articles
Knowledge Sharing Platforms
Such platforms can enhance the speed with which information is disseminated and lower the cost of obtaining information
Also offer scope for providing misleading or false information
Knowledge Sharing Platforms and Fake News
2014 Rick Pearson went undercover to investigate other authors and uncovered fake paid-for articles
He turned over the evidence to the SEC
SEC filed lawsuits against individuals, PR firms, and some companies on 31 October, 2014 and on 10 April, 2017
We obtained 171 articles by 20 authors about 47 firms
We further obtain 334 articles by the same authors that are non-fake
Linguistic Inquiry and Word Count Software
LIWC was developed by James Pennebaker and his colleagues
Outputs the percentage of words in a given text that fall into one or more of over 80 linguistic, psychological, and topical categories
From “The secret life of pronouns: what our words say about us”
From “The secret life of pronouns: what our words say about us”
Discrepancy words (examples: should, would)
Insight words (examples: think, know)
Relativity words (information about location and time)
Fake vs. non-fake articles by same authors
Fake news have: Much lower authenticity scores
Much fewer self-references
Shorter sentences
Lower insight score (examples: think, know)
Lower relativity score (information about location and time)
Higher discrepancy score (examples: should, would)
Higher clout score
Authenticity: For-sure Fake vs. Non-fake Articles
Identifying Fake Articles
in Overall Sample
Mapping into Probabilities
Generate the authenticity score for all articles published on Seeking Alpha and Motley Fool
Not clear how to interpret the cardinal nature of the authenticity score
Develop a mapping of the authenticity score into the probability space
Mapping into Probabilities
Authenticity score vs. conditional probability of being fake (Prob(F|S))
• Classify an article as fake if
P(Fake) > 20%
• Classify an article as non-fake
if P(Fake) < 1%
FakeNon-Fake
Other
Average probability of being fake:
Identifying Fake Articles
Type I and Type II errors
• We have 171 for-sure fake and 334 non-fake articles
• We use our algorithm to classify these articles into “fake articles” and “non-fake articles”
• Our algorithm identifies 18 articles as fake a 165 as non-fake
Our Algorithm For-sure Fake Non Fake Total
Fake 18
Non-Fake 165
Rick's and SEC articles
Type I and Type II errors
• We have 171 for-sure fake and 334 non-fake articles
• We use our algorithm to classify these articles into “fake articles” and “non-fake articles”
• Our algorithm identifies 18 articles as fake a 165 as non-fake
Our Algorithm For-sure Fake Non Fake Total
Fake 17 1 18
Non-Fake 165
Rick's and SEC articles
Our Algorithm For-sure Fake Non Fake Total
Fake 17 1 18
Non-Fake 14 151 165
Rick's and SEC articles
Type I and Type II errors
• We have 171 for-sure fake and 334 non-fake articles
• We use our algorithm to classify these articles into “fake articles” and “non-fake articles”
• Our algorithm identifies 18 articles as fake a 165 as non-fake
Impact of Fake News
on Financial Markets
We examine cumulative abnormal returns (measured as equal-weighted 4-factor residuals) for for-sure fake articles (171 articles)
Return Reaction: Rick’s and SEC articles
Small Firms Medium Firms
Return Reaction and Firm Size (all articles)
Small Firms Medium Firms
Large Firms
Insider Trading
and
Firm Actions
Fake Stories and insider trading: weekly
Fake Stories and insider trading: weekly
Conclusion
First paper in Finance to systematically identify fake articles on knowledge sharing platforms, and examine their prevalence
Document the effect of fake news on financial markets
Provide evidence of insider trading around the fake articles
Thank You!
Impact of Articles and Article Characteristics
Summary Statistics