Master Thesis Topics FSS 2021 - Chair of Finance - Prof. Dr. Erik Theissen - bwl.uni ...
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Master Thesis Topics FSS 2021 Chair of Finance – Prof. Dr. Erik Theissen
Master Thesis Topics
• Presentation is downloadable on our website:
https://www.bwl.uni-mannheim.de/en/theissen/teaching/master-courses/master-thesis/
2Chair of Finance (I)
• Address:
– L 9, 1-2
– Secretary: third floor (“3. OG“)
– Assistants: second, fourth, and fifth floor
• Office hours:
– By appointment
– General questions: Please visit our homepage first
– Secretary: Mo-Fr 09.00 – 12.00 am
3Chair of Finance (II)
• Research at the Chair of Finance
a) Market Microstructure
b) Empirical Asset Pricing
c) Blockchain & Cryptocurrency
4Master Thesis Topics
• Prerequisite:
– You must have successfully completed one seminar of the area "Banking,
Finance, and Insurance" (Prof. Albrecht, Prof. Bucher-Koenen, Prof. Maug, Prof.
Niessen-Ruenzi, Prof. Ruenzi, Prof. Spalt, Prof. Theissen, Prof. Weber/Wimmer).
• The assignment of topics is carried out jointly by the finance area.
• Assignment to the topics will be based on your priority list and the
grade in the respective seminar.
5Time Schedule
• Application period:
– Monday, 08.03.2021 – Tuesday, 16.03.2021
• Topics Allocation Announcement:
– Tuesday, 23.03.2021
• Registration Period:
– Tuesday, 23.03.2021 – Tuesday, 30.03.2021
• Starting Date
– Tuesday, 30.03.2021
• Colloquium
– Friday, 28.05.2021 (probably online via Zoom)
• Submission Deadline
– Friday, 30.07.2021
6Guide to Scientific Writing
• An information sheet on writing a seminar paper or a master thesis
is provided on our website:
https://www.bwl.uni-
mannheim.de/media/Lehrstuehle/bwl/Theissen/Services/Leitfaden_wissenschaftliche_Arbeite
n_SeminarMaster.pdf
• Most important rules:
– Your thesis should be 45 pages (+/- 10%)
– 50 pages is the absolute maximum
– Tables and figures have to be included in the text (and count towards the page
restriction)
– Only supplementary material that is not needed to read and understand the
thesis may be collected in an appendix
7Important Remarks
• Plagiarism policy:
– Your master thesis will be analyzed by plagiarism detection software (Turnitin).
– Our chair has a zero-tolerance policy regarding plagiarism.
– Students who submit plagiarized work will be graded with 5.0.
• Language quality:
– Grading of your master thesis takes also into account the language quality.
– Linguistic shortcomings negatively impacts your final grade.
– The master thesis can be either written in English or German.
8Master Topics
Questions ???
9T1. Beta and Data Frequency
Prof. Dr. Erik Theissen
Topic Description
• Most classical empirical asset pricing studies estimate betas from monthly data. More recently,
an increasing number of studies uses daily data (often over a shorter horizon, e.g. one year of
daily data instead of five years of monthly data). The objective of the present thesis is to
perform an empirical comparison of betas estimated from data at different frequency (daily
and monthly). The analysis should address the following questions:
• (How) do betas measured at different frequencies differ (e.g. when we sort against size)
• Which betas are more stable over time (i.e., which provide better estimates of future
betas)
• (How) does it matter which betas we use in asset pricing tests (e.g. in Fama and MacBeth
cross-sectional regressions)?
Requirements
The empirical work requires the use of large databases (i.e. CRSP). The databases are readily accessible for
affiliates of the University of Mannheim. The candidate should feel comfortable in the use of a statistical
software program (such as STATA) and econometric methods.
10T1. Beta and Data Frequency
Prof. Dr. Erik Theissen
Starting References
• Cohen, K., G. Hawawini, S. Maier, R. Schwartz and D. Whitcomb (1983): Estimating and
Adjusting for the Intervalling Effect Bias in Beta. Management Science 29, 135-148.
• Gilbert, T., C. Hrdlicka, J. Kalodimos and S. Siegel (2014): Daily Data is Bad for Beta: Opacity and
Frequency-Dependent Betas. Review of Asset Pricing Studies 4, 78-117.
• Hollstein, F. and M. Prokopczuk (2019): Estimating beta: Forecast adjustments and the impact
of stock characteristics for a broad cross-section. Journal of Financial Markets 44, 91-118.
11T2. Investor Herding in Cryptocurrency Markets
Stefan Scharnowski
Topic Description
• Herding in financial markets describes the inclination of investors to mimic the investment
decisions of others instead of relying on other information. Herd behavior might lead to
inefficient prices as investors disregard fundamental information, potentially leading to
irrational bubbles. Understanding such behavior is important for investors and regulators alike.
• When it comes to the relatively new asset class of cryptocurrencies, herding behavior is
particularly interesting. Firstly, there is relatively little fundamental information available,
potentially leading to higher levels of herding as investors follow the market instead of relying
on coin-specific information. Secondly, because the market for cryptocurrencies is still young
and developing, price inefficiencies might be more pervasive than in other markets. Thirdly,
with a large fraction of retail traders, the investor base of cryptocurrencies is different from
other, more mature asset classes.
• The aim of this thesis is to empirically study the presence of herding behavior in
cryptocurrency markets. A special emphasis should be placed on finding potential factors that
influence investor herding.
Requirements
The empirical work requires the use of large datasets. The candidate should feel comfortable in the use of a
statistical software program (such as Stata) and econometric methods.
12T2. Investor Herding in Cryptocurrency Markets
Stefan Scharnowski
Starting References
• Christie, W. G., & Huang, R. D. (1995). Following the Pied Piper: Do Individual Returns Herd around the Market? Financial
Analysts Journal, 51(4), 31–37. https://doi.org/10.2469/faj.v51.n4.1918
• Chang, E. C., Cheng, J. W., & Khorana, A. (2000). An examination of herd behavior in equity markets: An international
perspective. Journal of Banking and Finance, 24(10), 1651–1679. https://doi.org/10.1016/S0378-4266(99)00096-5
• Bouri, E., Gupta, R., & Roubaud, D. (2019). Herding behaviour in cryptocurrencies. Finance Research Letters, 29, 216–221.
https://doi.org/10.1016/j.frl.2018.07.008
• Vidal-Tomás, D., Ibáñez, A. M., & Farinós, J. E. (2019). Herding in the cryptocurrency market: CSSD and CSAD approaches.
Finance Research Letters, 30, 181–186. https://doi.org/10.1016/j.frl.2018.09.008
• da Gama Silva, P. V. J., Klotzle, M. C., Pinto, A. C. F., & Gomes, L. L. (2019). Herding behavior and contagion in the
cryptocurrency market. Journal of Behavioral and Experimental Finance, 22, 41–50.
https://doi.org/10.1016/j.jbef.2019.01.006
• Kaiser, L., & Stöckl, S. (2020). Cryptocurrencies: Herding and the transfer currency. Finance Research Letters, 33, 101214.
https://doi.org/10.1016/j.frl.2019.06.012
• Gurdgiev, C., & O’Loughlin, D. (2020). Herding and anchoring in cryptocurrency markets: Investor reaction to fear and
uncertainty. Journal of Behavioral and Experimental Finance, 25. https://doi.org/10.1016/j.jbef.2020.100271
• Philippas, D., Philippas, N., Tziogkidis, P., & Rjiba, H. (2020). Signal-herding in cryptocurrencies. Journal of International
Financial Markets, Institutions and Money, 65, 101191. https://doi.org/10.1016/j.intfin.2020.101191
• Ballis, A., & Drakos, K. (2020). Testing for herding in the cryptocurrency market. Finance Research Letters, 33.
https://doi.org/10.1016/j.frl.2019.06.008
• Coskun, E. A., Lau, C. K. M., & Kahyaoglu, H. (2020). Uncertainty and herding behavior: evidence from cryptocurrencies.
Research in International Business and Finance, 54(2), 101284. https://doi.org/10.1016/j.ribaf.2020.101284
13T3. Retail Trading in Derivatives
Thomas Johann
Topic Description
• There exists a large body of literature analyzing retail investor trading in the equity market.
Much less research has shed light on retail traders‘ behavior in the derivative market.
• Bauer et al. (2009) show that retail traders usually trade options for gambling purposes rather
than for hedging. They also show that those investments have a negative alpha on average.
These findings are consistent with those of Henderson/Pearson (2011) and Choy (2015).
• Against the background of increased retail trader participation in the stock market and cases
like the Gamestop rally, it is important to better understand retail trader portfolio
composition.
• In this master thesis the author should provide a broad literature review on the use of
derivatives by retail traders. What options are available to retail traders to hedge their
portfolios (warrants, structured products, options, futures, …)? If used, how costly are these
strategies? And are options actually used for risk management at all? This literature review will
be complemented by a short empirical section analyzing a dataset of retail trades in a
derivatives market to shed light on above questions.
• Recommended skills: Time Management, Programming (Stata, R or Python).
14T3. Retail Trading in Derivatives
Thomas Johann
Starting References
• Rob Bauer, Mathijs Cosemans, Piet Eichholtz, Option trading and individual investor performance, Journal
of Banking & Finance, Volume 33, Issue 4, 2009, Pages 731-746.
• Brian J. Henderson, Neil D. Pearson, The dark side of financial innovation: A case study of the pricing of a
retail financial product, Journal of Financial Economics, Volume 100, Issue 2, 2011, Pages 227-247.
• Siu-Kai Choy, Retail clientele and option returns, Journal of Banking & Finance, Volume 51, 2015, Pages 26-
42.
• Nicolaus, David. Derivative Choices of Retail Investors: Evidence from Germany. Working Paper, 2010.
15T4. Socially Responsible Investments
Lukas Zimmermann
Topic Description
• A large strand of literature focusses on socially responsible investment (SRI). Relevant studies
generally deal with the questions whether employing information about the environmental,
social, and governance (ESG) performance to implement investment strategies gives rise to a
premium, and whether using those information in investment decision has an impact on
investment outcomes.
• The objective of this study is to examine ESG based investing. The thesis should consists of a
shorter literature part and a large empirical part.
• At the beginning, the thesis should study the literature concerning corporate social investment
and give an thorough overview.
• The empirical part should focus on two central questions concerning ESG based strategies.
First, it should be studied whether investment strategies based on ESG measures used in the
literature are profitable (i.e. whether there is an ESG premium for socially responsible firms).
Second, it should be tested whether controlling for the ESG performance of firms when
constructing important anomalies (e.g. value, profitability, quality, on which many factor-ETFs
are based) has a significant impact on the anomaly returns and whether ESG management
comes at a cost or improves performance.
16T4. Socially Responsible Investments
Lukas Zimmermann
Starting References
• Albuquerque, R., Koskinen, Y., and Zhang, C. (2018). Corporate Social Responsibility and Firm
Risk: Theory and Empirical Evidence. Management Science.
• Alessandrini, F., and Jondeau, E. (2020). ESG-Investing: From Sin Stock to Smart Beta. Working
Paper
• Alessandrini, F., and Jondeau, E. (2020). Optimal Strategies for ESG Portfolios. Working Paper.
• Antoncic M., Bekaert, G., Rothenberg, R., and Noguer, M. (2020). Sustainable Investment –
Exploring the Linkage Between Alpha, ESG, and SDGs. Working Paper.
• Bennani, L., Le Guenedal, T., Lepetit, F., Ly, L., Mortier, V., Roncalli, T., and Sekine, T. (2019).
How ESG Investing has Impacted the Asset Pricing in the Equity Market. Working Paper.
• Lioui, A. (2018). ESG Factor Investing: Myth or Reality? Working Paper.
17T5. Features and Social Media Sentiment of Cryptocurrency
Yanghua Shi
Topic Description
• The relatively new asset class of cryptocurrencies, of which Bitcoin is by far the most popular,
has received a lot of attention in recent years, both in media and in academic research.
• However, the impact of design features on cryptocurrency market value (e.g. price, trading
volume and market capitalization) is not well understood, including their influence via social
media sentiment.
• Research question: To what extent does design features impact the cryptocurrency market
value via social media sentiment?
• Design feature data will be provided. The student are also welcomed to come up with their
own design feature data.
Requirements
The empirical work requires the use of large databases on cryptocurrency market data. The candidate should
feel comfortable in the use of a statistical software program (such as STATA) and econometric methods.
18T5. Features and Social Media Sentiment of Cryptocurrency
Yanghua Shi
Starting References
• Hayes, A. S. (2015). What factors give cryptocurrencies their value: An empirical analysis.
Available at SSRN 2579445.
• Lamon, Connor, Eric Nielsen, and Eric Redondo. "Cryptocurrency price prediction using news
and social media sentiment." SMU Data Sci. Rev 1.3 (2017): 1-22.
• Phillips, Ross C., and Denise Gorse. "Mutual-excitation of cryptocurrency market returns and
social media topics." Proceedings of the 4th international conference on frontiers of
educational technologies. 2018.
• Phillips, Ross C., and Denise Gorse. "Predicting cryptocurrency price bubbles using social
media data and epidemic modelling." 2017 IEEE symposium series on computational
intelligence (SSCI). IEEE, 2017.
19T6. Mutual fund flow-induced return comovement
Mengnan Wu
Topic Description
• A rapidly expanding literature has used the investor flows to and from mutual funds as a
source of exogenous price pressure. If the required sales from individual investors are
sufficiently large, the funds’ liquidity needs may put downward pressure on prices that is
unrelated to the fundamental value of the underlying stocks (Wardlaw, 2020).
• The flow-induced trading, across mutual funds, have a significant impact on individual stock
returns and drive stock prices temporarily away from their information-efficient benchmarks.
The flow-based mechanism can potentially cause stock return comovement (Lou, 2012).
• Previous papers have examined the implications of mutual fund flow-induced trading for
stock return comovement. Greenwood and Thesmar (2011) center on the covariance
structure of investment flows across mutual funds. Anton and Polk (2014) focus on common
institutional ownership across stocks.
• Following Lou (2012), the student is required to 1) measure institutional price pressure in
equity markets, and 2) test whether stocks held by mutual funds with similar flows tend to
experience correlated flow-induced trading, and thus comove with one another, if mutual
funds with similar flows also have similar holdings, or mutual funds receive correlated inflows
or face correlated outflows.
• Knowledge of econometric software is appreciated for the thesis.
20T6. Mutual fund flow-induced return comovement
Mengnan Wu
Starting References
• Barberis, N., Shleifer, A., & Wurgler, J. (2005). Comovement. Journal of financial economics,
75(2), 283-317.
• Greenwood, R., & Thesmar, D. (2011). Stock price fragility. Journal of Financial Economics,
102(3), 471-490.
• Lou, D. (2012). A flow-based explanation for return predictability. The Review of Financial
Studies, 25(12), 3457-3489.
• Anton, M., & Polk, C. (2014). Connected stocks. The Journal of Finance, 69(3), 1099-1127.
• Wardlaw, M. (2020). Measuring mutual fund flow pressure as shock to stock returns. The
Journal of Finance. Advance online version. doi: 10. 1111/jofi.12962.
21T7. Demand shocks, excess comovement and return predictability
Mengnan Wu
Topic Description
• Barberis et al. (2005) distinguish two explanations for return comovement: the traditional
view, which attributes it to comovement in news about fundamental value, and an alternative
view, in which frictions or sentiment delink it from fundamentals.
• Broman (2020) specifies the trading location of security as the source of the non-
fundamental demand shocks.
• Many factors may contribute to the formation of local preferred habitats: Fund providers’
catering to local investor demand, lack of information, constraints on investors’ attention, or
familiarity bias that arises when investors are unwilling to deviate from the status quo.
• The goal of the thesis is to broadly replicate Broman (2020), examining the excess
comovement and the subsequent return reversal patterns in major European stock
exchanges. The student should 1) measure the quantity of excess comovement resulting from
local demand shocks, 2) identify the source of mispricing and differentiate between excess
comovement that arises due to local non-fundamental demand, local fundamental demand
(information diffusion), and stale pricing, and 3) test whether the peer-group price gap
predicts future ETF returns.
• Knowledge of econometric software is appreciated for the thesis.
22T7. Demand shocks, excess comovement and return predictability
Mengnan Wu
Starting References
• Barberis, N., Shleifer, A., & Wurgler, J. (2005). Comovement. Journal of financial economics,
75(2), 283-317.
• Cao, H. H., Han, B., Hirshleifer, D., & Zhang, H. H. (2011). Fear of the unknown: Familiarity and
economic decisions. Review of finance, 15(1), 173-206.
• Anton, M., & Polk, C. (2014). Connected stocks. The Journal of Finance, 69(3), 1099-1127.
• Brown, D. C., Davies, S. W., & Ringgenberg, M. C. (2020). ETF Arbitrage, Non-Fundamental
Demand, and Return Predictability. Review of Finance.
• Broman, M. S. (2020). Local demand shocks, excess comovement and return predictability.
Journal of Banking & Finance, 119, 105910.
23T8. Macroeconomic News and Stock Market Anomalies
Can Yilanci
Topic Description
• The concepts of weak and semi-strong form market efficiency state that investors should not be able to earn risk-
adjusted returns by analyzing past prices/returns and doing fundamental analysis (Fama, 1970). Yet, there exist
various market anomalies that challenge these concepts. Black (1972) finds that stocks with low (high) beta have
high (low) alphas (“beta anomaly”). Fama and French (1992) find that stocks with low market capitalization
outperform stocks with large market capitalization. Moreover, they show that stocks with high book-to-market
ratio outperform stocks with low book-to-market ratio. Last but not least, Jegadeesh and Titman (1993) show that
stocks with high (low) returns in the past continue to have high (low) returns in the future (“momentum effect”). All
these anomalies challenge the concept of market efficiency.
• Recently, Savor and Wilson (2014) show that the low-beta anomaly disappears when beta is estimated over
macroeconomic announcement days (FOMC interest rate, unemployment, and inflation announcements). Does this
mean that stock market participants pay more attention to the stock market when macroeconomic news are
announced? And can macroeconomic announcement days help to explain other stock market anomalies as well?
• The student’s task is twofold. First, he/she should replicate the evidence in Savor and Wilson (2014) for the low-
beta anomaly. Second, he/she should extend the analysis to other anomalies. The size, value, and momentum
effects may serve as a starting point.
Requirements
The empirical work requires the use of large databases (i.e. CRSP and Compustat). The databases are readily accessible
for affiliates of the University of Mannheim. The candidate should feel comfortable in the use of a statistical software
program (such as STATA) and econometric methods.
24T8. Macroeconomic News and Stock Market Anomalies
Can Yilanci
Starting References
• Black, B. 1972. Capital Market Equilibrium with Restricted Borrowing. The Journal of Business.
45(3): 444-455
• Fama, E. 1970. Efficient capital markets: A review of theory and empirical work. The Journal of
Finance. 25(2): 383-417
• Fama, E., K. French. 1992. The cross-section of expected stock returns. The Journal of Finance.
47: 427-465
• Fama, E., K. French. 1993. Common risk factors in the returns on stocks and bonds. Journal of
Financial Economics. 33(1): 3-56
• Jegadeesh, N., and S. Titman. 1993. Returns to Buying Winners and Selling Losers: Implications
for Stock Market Efficiency. The Journal of Finance. 48 (1): 65-91
• Savor, P., M. Wilson. 2013. How much do investors care about macroeconomic risk? Evidence
from scheduled economic announcements. Journal of Financial and Quantitative Analysis. 48:
343-375
• Savor, P., M. Wilson. 2014. Asset pricing: A tale of two days. Journal of Financial Economics.
113(2): 171-201
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