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Master Thesis FSS 2015
Stefan Ruenzi, Florens Focke, Lena Jaroszek, Anja Kunzmann, Pavel Lesnevski, Michael Ungeheuer,
What are the prerequisites?
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You are a master student.
You must have successfully completed a seminar at one of the finance chairs (Albrecht,
Bühler, Maug, Niessen-Ruenzi, Ruenzi, Schmid, Terberger, Theissen, Weber).
Some knowledge of statistics and econometrics is useful and participants should be
motivated to undertake empirical work.
You are available in the time period from March to July.
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Schedule
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Friday, February 27: Topics announcement
Wednesday, March 4: Topics presentation
Monday, March 16- Friday, March 20: Submission of priority list
Wednesday, March 25: Topics allocation announcement
Wednesday, March 25 - Tuesday, March 31: Registration period
Tuesday, March 31 : Starting date
Mid May: 1st colloquium
Mid June: 2nd colloquium
Friday, July 31, 12pm (noon): Submission of master thesis
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Colloquia
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There will be two block seminars approximately two months and three months after
the starting date, respectively.
The block seminars provide a platform to discuss the structure of your thesis, present
(first) empirical results, raise questions, and to further stimulate your research.
The colloquia are not graded.
Participation in the block seminars is mandatory for all students.
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How to apply?
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Submit your priority list online (link on our website) .
You can combine topics from different chairs.
– E.g., first preference: “3rd Topic, Chair of Prof. Weber”; second preference: “10th
Topic, Chair of Prof. Theissen”; third preference: “4th Topic, Chair of Prof. Ruenzi”
Please only choose topics you are really willing to work on.
The allocation of topics is based on the grade in the finance seminar.
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How do we grade?
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Supervision of the thesis by Prof. Ruenzi and the assigned advisor.
100% paper
The colloquia are not graded.
Plagiarism: no excuse policy
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How should your thesis look like?
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~50 pages (without appendix)
Language: English
Detailed formal requirements: see the guidelines provided on our website.
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R1: Investors' FEARS and the Reaction of the Stock Market
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Advisor: Anja Kunzmann
Empirical topic
Motivation:
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In classical finance theory, mispricing induced by investor sentiment is assumed to be corrected for by
rational arbitrageurs.
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However, if these rational arbitrageurs experience limits to their arbitrage opportunities (e.g. short-selling
constraints), sentiment induced mispricing can persist.
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Researchers have used “traditional” measures for investor sentiment: market-based and survey-based.
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A new and more direct measure of investor sentiment can be obtained via internet search queries. Da et al.
(2015) use Google search volume to create the “Financial and Economic Attitudes Revealed by Search
(FEARS) Index” for investor sentiment.
Research Question:
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Is the FEARS Index able to predict stock market outcomes?
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Does the FEARS index reliably reflect phases of high and low investor sentiment, e.g. before and after
important macroeconomic events?
Data: Google Trends data (partially provided). Financial markets and firm data (access to databases provided).
Requirements: Knowledge on Stata. Spending some time on collecting data from Google Trends.
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R2: An Empirical Analysis of Industry Attractiveness and Firm Valuations
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Advisor: Anja Kunzmann
Empirical topic
Motivation:
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To simplify stock picking decisions, investors tend to categorize stocks according to common features, e.g.
large-cap stocks or value stocks.
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Prior literature has shown that stocks from the same industry also show important commonalities. Based on
these findings, Che et al. (2015) hypothesize that industry categorizations could also be used by investors.
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They argue that more attractive industries (measured in terms of industry profitability dispersion) receive
more investor attention. Stocks from these industries will be highly demanded, which will cause an
overvaluation of the stocks.
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Recently, new and more direct measures of investor attention have been developed (e.g. Wikipedia page
counts used by Focke et al. (2015)).
Research Question:
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Is there a relationship between industry profitability dispersion and the equity value of firms within the
industry?
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Does this relationship also apply to different measures of industry attractiveness?
Data: Wikipedia data (will be provided). Financial markets and firm data (access to databases provided).
Requirements: Knowledge on Stata.
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R3: Investor Attention and the Pricing of Earnings News
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Advisor: Michael Ungeheuer
Empirical topic
Motivation:
• Investor attention varies across stocks and time
• It might increase or decrease mispricing
• Evidence: more investor attention  less earnings momentum
• Attention measures used in these studies unavailable for many stocks and years
Tasks:
• Analyze the impact of investor attention on earnings momentum
• Use a novel, broadly-available measure of attention: Wikipedia page views
Data: U.S. (CRSP, Compustat, I/B/E/S) stock market data and Wikipedia page views
Requirements: Econometric analysis in Stata
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R4: An Investigation of Mutual Fund Performance Using the Most Recent Factor Models
Advisor: Pavel Lesnevski
Empirical topic
Motivation:
• Models by Hou, Xue, and Zhang (2014) and Fama and French (2014)
– outperform the existing benchmark models .
– good candidates for becoming new workhorse benchmark models .
– might change estimation results and interpretation of many findings in the financial
literature.
• Xue (2012) applies the q-factor model from Hou, Xue, and Zhang (2014) and
– finds that funds’ risk-adjusted performance changes significantly.
– questions existing measures of manager’s skill.
Goal:
• Replicate Xue (2012).
• Use an alternative model of Fama and French (2014).
• Test skill measures published in the literature.
Data: Datasets from WRDS (available at the University of Mannheim). The dataset on mutual
fund holdings will be provided.
Requirements: Knowledge and/or willingness to acquire knowledge in portfolio management.
Experience in Stata.
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R5: The Role of Analyst Coverage in Market Efficiency and Performance of Mutual Funds
Advisor: Pavel Lesnevski
Empirical topic
Motivation:
• Investors should be compensated for eliminating misvaluation with gains that are larger
than the costs of obtaining information (Grossman and Stiglitz (1980)).  Informed
investors should earn higher returns in stocks that are difficult to evaluate.
• Schultz (2010) finds evidences in favor of this hypothesis.
• But he does not consider sell-side analysts (play an important role in the dissemination of
information about the firms).
Goal:
• Replicate Schultz (2010).
• Extend his results by introducing analyst coverage .
• Test the implications using the quasi-natural experiment of change in analyst coverage from
Kelly and Ljungqvist (2012).
Data: Datasets from WRDS (available at the University of Mannheim). The dataset on mutual
fund holdings will be provided. The dataset on brokerage closures from Kelly and Ljungqvist
(2012) should be constructed by the candidate.
Requirements: Knowledge and/or willingness to acquire knowledge in portfolio management.
Experience in Stata.
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R6: Improving credit card decisions by fostering self-control
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Advisor: Lena Jaroszek
Experimental topic
Motivation:
• Americans are borrowing more than they are saving and they are more likely to participate
in the credit card market than to hold public equity.
• Outstanding credit card debt amounts to around $800 billion and according to Zinman
(2014) finance charges and fees amount to roughly $120-$150 billion.
• Individuals with low self-control are likely to overborrow (Heidhues, Kösezegi 2010).
Research Question:
• Is it possible to increase consumers’ awareness about time-inconsistency in their
preferences and/or help them to correctly assess the degree of low future self-control?
• Do changed self-assessments impact credit decisions?
Set up: Experimental study (online experiment).
Requirements: Knowledge and/or willingness to acquire knowledge about design, setup and
conduction of an online experiment. Willingness to understand microeconomic theory models
and to apply econometric models.
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R7: How does the media report on earnings announcements? An empirical
analysis.
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Advisor: Florens Focke
Empirical topic
Motivation:
• The media are an important information source for investors and consumers.
• A frequently made claim is that the media tends to sensationalize news. This could occur
either by "hyping" good news, or by emphasizing negative news.
• García (2014) analyzes the content of particular columns from the New York Times and the
Wall Street Journal in relation to prior day Dow Jones Industrial Average returns.
Research Question:
• How does the media report on earnings announcements?
• In particular, what is the relationship between the content of earnings announcements and
subsequent media coverage?
Data: Media and firm-level data (access provided).
Requirements: Knowledge and/or willingness to acquire knowledge about textual analysis.
Knowledge on Stata.
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R8: How does the media report on macroeconomic announcements? An
empirical analysis.
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Advisor: Florens Focke
Empirical topic
Motivation:
• The media are an important information source for investors and consumers.
• A frequently made claim is that the media tends to sensationalize news. This could occur
either by "hyping" good news, or by emphasizing negative news.
• García (2014) analyzes the content of particular columns from the New York Times and the
Wall Street Journal in relation to prior day Dow Jones Industrial Average returns.
Research Question:
• How does the media report on macroeconomic news announcements?
• In particular, what is the relationship between the content of macroeconomic news
announcements such as unemployment or GDP change news and subsequent media
coverage?
Data: Media and firm-level data (access provided).
Requirements: Knowledge and/or willingness to acquire knowledge about textual analysis.
Knowledge on Stata.
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Final Remarks
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Visit our website (http://intfin.bwl.uni-mannheim.de/). There you will find a detailed
description of every topic.
Pay attention to the deadlines (submission of priority list).
Apply only for topics you really want to work on.
In case of questions, do not hesitate to contact us.
– Florens Focke: florens.focke@gess.uni-mannheim.de
– Lena Jaroszek: jaroszek@bwl.uni-mannheim.de
– Anja Kunzmann: kunzmann@bwl.uni-mannheim.de
– Pavel Lesnevski: plesnevs@mail.uni-mannheim.de
– Michael Ungeheuer: ungeheuer@bwl.uni-mannheim.de
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