| Last update 18-02-2019||
2nd degree (Master)
Prof Noah STOFFMAN
Coordinator Office Hours:
This course will introduce students to recent develops in the fast-growing literature that
aims to understand the behavior of investors and firms by analyzing text and other new
sources of information.
Students are expected to come to class prepared to discuss the assigned papers (see below).
Each class will consist of a brief introduction by me, followed by a lengthy discussion of the
papers. I expect students to participate actively in these discussions. In particular, students
should consider the following questions when reading the papers:
1. What is the primary research question that the authors set out to answer?
2. How do the authors try to answer the question? What data do they use, and what
are the econometric approaches taken?
3. What do they find?
4. Are the results believable? Are the conclusions supported by the evidence?
5. How could you improve the paper?
6. Is there a related question that you could answer through your own research?
Learning outcomes - On successful completion of this module, students should be able to:
understand the behavior of investors and firms by analyzing text and other new
sources of information
Teaching arrangement and method of instruction:
All papers will be discussed in class, and should be read before coming to class. However,
those marked with a * will be discussed in detail, and students should be especially
prepared to discuss these.
1. Overview of techniques and findings
* Loughran, Tim, and Bill McDonald, 2016, Textual analysis in accounting and
finance: A survey, Journal of Accounting Research 54, 1187-1230.
* Gentzkow, Matthew, Bryan T. Kelly and Matt Taddy, Text as data, working paper.
Das, Sanjiv R., 2014, Text and context: Language analytics in finance, Foundations
and Trends in Finance 8, 145-261.
Optional book-length introduction to natural language processing: Bird, S., Klein, E.
and Loper, E., 2009, Natural language processing with Python: analyzing text with
the natural language toolkit, OReilly Media, Inc.
2. Media in financial economics
Baker, S. R., N. Bloom, and S. J. Davis, 2016, Measuring economic policy uncertainty,
Quarterly Journal of Economics 131, 1593-1636.
Bhattacharya, Utpal, Neal Galpin, Rina Ray, and Xiaoyun Yu, 2009, The role of the
media in the internet IPO bubble, Journal of Financial and Quantitative Analysis 44,
* Dougal, Casey, Joseph Engelberg, Diego Garcia, and Christopher A Parsons, 2012,
Journalists and the stock market, The Review of Financial Studies 25, 639-679.
* Engelberg, Joseph E., and Christopher A. Parsons, 2011, The causal impact of media
in financial markets, The Journal of Finance 66, 67-97.
Gentzkow, Matthew, and Jesse M. Shapiro, 2010, What drives media slant? Evidence
from U.S. daily newspapers, Econometrica 78, 35-71.
Griffin, John M., Nicholas H. Hirschey, and Patrick J. Kelly, 2011, How important is
the financial media in global markets?, The Review of Financial Studies 24, 3941-
* Gurun, Umit G., and Alexander W. Butler, 2012, Don't believe the hype: Local
media slant, local advertising, and firm value, The Journal of Finance 67, 561-598.
* Tetlock, Paul C., 2007, Giving content to investor sentiment: The role of media in
the stock market, The Journal of Finance 62, 1139-1168.
3. Investor sentiment and attention
Antweiler, Werner, and Murray Z. Frank, 2004, Is all that talk just noise? The
information content of internet stock message boards, The Journal of Finance 59,
Barber, Brad M., and Terrance Odean, 2007, All that glitters: The effect of attention
and news on the buying behavior of individual and institutional investors, The
Review of Financial Studies 21, 785-818.
Chen, Hailiang, Prabuddha De, Yu Hu, and Byoung-Hyoun Hwang, 2014, Wisdom of
crowds: The value of stock opinions transmitted through social media, The Review of
Financial Studies 27, 1367-1403.
Cookson, J. Anthony and Marina Niessner, 2016, Why Don't We Agree? Evidence
from a Social Network of Investors, Working Paper.
* Da, Zhi, Joseph Engelberg, and Pengjie Gao, 2011, In search of attention, The
Journal of Finance 66, 1461-1499.
* Da, Zhi, Joseph Engelberg, and Pengjie Gao, 2014, The sum of all FEARS: investor
sentiment and asset prices, The Review of Financial Studies 28, 1-32.
Das, Sanjiv R., and Mike Y Chen, 2007, Yahoo! For amazon: Sentiment extraction
from small talk on the web, Management science 53, 1375-1388.
* Garcia, Diego, 2013, Sentiment during recessions, The Journal of Finance 68, 1267-
* Tetlock, Paul C., 2011, All the news that's fit to reprint: Do investors react to stale
information?, The Review of Financial Studies 24, 1481-1512.
4. Corporate reporting and the media
* Ahern, Kenneth R., and Denis Sosyura, 2014, Who writes the news? Corporate
press releases during merger negotiations, The Journal of Finance 69, 241-291.
Boudoukh, Jacob, Ronen Feldman, Shimon Kogan, and Matthew Richardson, 2013,
Which news moves stock prices? A textual analysis, NBER Working Paper.
* Brown, Stephen V., and Jennifer Wu Tucker, 2011, Large-sample evidence on firms
year-over-year MD&A modifications, Journal of Accounting Research 49, 309-346.
* Cohen, Lauren, Dong Lou, and Christopher Malloy, 2013, Playing favorites: How
firms prevent the revelation of bad news, NBER Working Paper.
Cohen, Lauren, Christopher J Malloy, and Quoc H. Nguyen, 2016, Lazy prices,
Hillert, Alexander, Alexandra Niessen-Ruenzi, and Stefan Ruenzi, 2014, Mutual fund
shareholder letter tone: Do investors listen?, Working Paper.
* Huang, Allen H., Reuven Lehavy, Amy Y. Zang, and Rong Zheng, 2017, Analyst
information discovery and interpretation roles: A topic modeling approach,
Loughran, Tim, and Bill McDonald, 2017, The use of EDGAR filings by investors,
Journal of Behavioral Finance 18, 231-248.
Mayew, William J., and Mohan Venkatachalam, 2012, The power of voice:
Managerial affective states and future firm performance, The Journal of Finance 67,
5. Extracting meaning from corporate filings
* Bodnaruk, Andriy, Tim Loughran, and Bill McDonald, 2015, Using 10-K text to
gauge financial constraints, Journal of Financial and Quantitative Analysis 50, 623-
* Hoberg, Gerard, and Gordon Phillips, 2016, Text-based network industries and
endogenous product differentiation, Journal of Political Economy 124, 1423-1465.
Henry, E. and Leone, A.J., 2015, Measuring qualitative information in capital markets
research: Comparison of alternative methodologies to measure disclosure tone,
Accounting Review, 91, 153-178.
* Jegadeesh, Narasimhan, and Di Wu, 2013, Word power: A new approach for
content analysis, Journal of Financial Economics 110, 712-729.
Li, Feng, 2010, The information content of forward-looking statements in corporate
filingsa naïve bayesian machine learning approach, Journal of Accounting Research
* Loughran, Tim, and Bill McDonald, 2011, When is a liability not a liability? Textual
analysis, dictionaries, and 10-Ks, The Journal of Finance 66, 35-65.
Loughran, Tim, and Bill McDonald, 2014, Measuring readability in financial
disclosures, The Journal of Finance 69, 1643-1671.
Tetlock, Paul C., Maytal Saar-Tsechansky, and Sofus Macskassy, 2008, More than
words: Quantifying language to measure firms' fundamentals, The Journal of Finance
See course content.
Additional Reading Material:
End of year written/oral examination 0 %
Presentation 0 %
Participation in Tutorials 100 %
Project work 0 %
Assignments 0 %
Reports 0 %
Research project 0 %
Quizzes 0 %
Other 0 %
The course grade will be determined by a students preparedness for and participation in
Students needing academic accommodations based on a disability should contact the Center for Diagnosis and Support of Students with Learning Disabilities, or the Office for Students with Disabilities, as early as possible, to discuss and coordinate accommodations, based on relevant documentation. |
For further information, please visit the site of the Dean of Students Office.