Seminar "Selected Topics in Finance"

Preface

The seminar is open to Master students.

In this seminar, we will study current finance research related to different topics such as climate change or the value premium. See the topics below for more information.

To successfully pass the seminar you need to write a paper and give a presentation. Papers should be written in English and should have a length of 15-20 (team of two) or 20-25 pages (team of three). For hints on how to write a paper see our guidelines. The seminar talks are in English.

Please contact your supervisor to discuss the outline of your paper, your empirical part (if any), and any questions that you may have. For organizational questions, please ask Syed Wasif Hussain.

Relevant Information will be made Available via the Moodle page. 

FAQ & Organisational matters

  • Do we get a grade? Yes. Your paper and your presentation will be graded and lead to one grade (equally weighted). Both the paper and presentation have to be passed.
  • What do we have to hand in? An outline of your paper to discuss the content of your paper and your final paper one week before the presentation.
  • Who is responsible? For content-related questions, please contact your supervisor. For organizational questions, please ask Syed Wasif Hussain.

Time Table

  • Mon. 01.02.2021 - Fri. 05.02.2021 Students must submit their preferences over seminars for the first matching round.
    http://econ.mathematik.uni-ulm.de:3838/semapps/stud_en/   (English)
  • http://econ.mathematik.uni-ulm.de:3838/semapps/stud_de/   (German)
  • Sat. 06.02.2021 1st round of seminar matching.
  • Wed. 10.02.2021 2nd round of seminar matching.
  • Tue. 16.02.2021, 16:00 General information and Q&A session about the Seminar (Webex)  
  • Tue. 16.02.2021: Topic allocation (sort the topics on Taddle until Sun. 28.02.2021, 23:59)
  • Thur. 01.04.2021 - Sun. 25.04.2021 Registration at the Higher Services Portal
  • Until Fri. 30.04.2021 Contact your supervisor to discuss the outline of the paper
  • Mon. 21.06.2021 Submission of the paper on Moodle 
  • Fri. 25.06.2021(14:00-18:00) - Sat. 26.06.2021(10:00-14:00) Presentations on Moodle/Zoom

Topics

 

1. Refining the value factor 

Over the last decade, the value premium was negative. Does this mean that the value effect has disappeared? Or is it only necessary to update the definition of value and growth stocks? You shall review approaches for such refinements and the performance of these refined factor models. As part of your paper, Illustrate the intangible capital calculations with the help of some examples e.g.  stocks such as Citigroup and Paypal that are in the same sector but differ in their book-to-market value.

Main sources: 

Eisfeldt, A.L., Kim, E. and Papanikolaou, D., 2020. Intangible Value (No. w28056). National Bureau of Economic Research. https://www.nber.org/papers/w28056 (Available when connected to the university network)

Gulen, H., Li, D., Peters, R.H. and Zekhnini, M., 2021. Intangible Capital in Factor Models. Available at SSRN.  papers.ssrn.com/sol3/papers.cfm

      Supervisor: Justus Kalmbach

      Students: Javed Hussain, Kai Wang and Valentin Kutschker

 

2. Integrating carbon risk into a factor model

Görgen et al. (2019) suggest an approach that incorporates the risks from the likely transition to a low-carbon economy into a factor model. Present their approach and their results.  Also (i) estimate carbon betas for selected ETFs and other assets with large expected differences in carbon sensitivity in order to illustrate the usefulness of this approach and (ii) test whether the new factor is redundant given the factors of the Fama-French five-factor model, as in Table 6 of Fama/French (2015) “A five-factor asset pricing model”.  We will provide you with the BMG data used in the Görgen et al. paper. For (ii) use data for “Developed Factors” from Ken French’s webpage.

Main source:

Görgen, M., Jacob, A., Nerlinger, M., Riordan, R., Rohleder, M., & Wilkens, M. (2019). Carbon risk. Available at papers.ssrn.com/sol3/papers.cfm

      Supervisor: Niklas Paluskiewicz 

      Students: Ayush kumar and Despoina Chatzinikolaou

 

3. Cryptocurrencies and the role of investor attention
Even though cryptocurrencies have been around since 2009 when they were first introduced in the form of Bitcoin, interest in cryptocurrencies has skyrocketed recently. The market capitalization of Bitcoin has exceeded of $600bn in January 2021, and thousands of altcoins are also on the rise such as Ethereum, Tether etc. However, what drives these highly volatile prices is still a mystery. In your paper you should first summarize the work of Liu and Tsyvinski (2018) on the relationship of Investor attention and Cryptocurrency return. You should then empirically test this relationship using Google search volume as proxy for investor attention. 

Literature to get started: 

Liu, Y. and Tsyvinski, A., 2018. Risks and returns of cryptocurrency (No. w24877). National Bureau of Economic Research.

Nasir, M.A., Huynh, T.L.D., Nguyen, S.P. and Duong, D., 2019. Forecasting cryptocurrency returns and volume using search engines. Financial Innovation, 5(1), pp.1-13.

Zhang, W. and Wang, P., 2020. Investor attention and the pricing of cryptocurrency market. Evolutionary and Institutional Economics Review, 17(2), pp.445-468.
     

      Supervisor: Syed Wasif Hussain

      Students: Jasmin Murtic, Tim Frerich and Tobias Dorst

 

4. Covid-19 and stock market
No previous infectious disease outbreak, including the Spanish Flu, has affected the stock market as forcefully as the COVID-19 pandemic. With stock markets around the world crashing in response to the pandemic, the question still remains whether these reactions were due to fundamental developments (e.g. infection rates) or also due to investor attention to news. Your task is to summarize the literature on this and then conduct an empirical study of your own. 


Literature to get started: 

Engelhardt, N., Krause, M., Neukirchen, D. and Posch, P., 2020. What Drives Stocks during the Corona-Crash? News Attention vs. Rational Expectation. Sustainability, 12(12), p.5014.

Lyócsa, Š. and Molnár, P., 2020. Stock market oscillations during the corona crash: The role of fear and uncertainty. Finance Research Letters, 36, p.101707.
     

      Supervisor: Syed Wasif Hussain

      Students: Julia Langenbacher, Lena Notheisen and Viktor Moskvin

 

5. COVID-19, carbon intensities and ESG variables

The COVID-19 pandemic affects the whole economy, but the strength of negative effects differs between industries. Mukanjari and Sterner (2020) analyse the connections between the carbon intensities and decreases in stock value. Furthermore, they analyse ESG related information.

Can you find more evidence for a connection between carbon intensities and the effects of the pandemic by using other sources? Is there also support for a relationship with ESG criteria? Also, search for concrete positive and negative examples and present the share price development of these companies.  What are the implications for investors? Are green investments even more important in the future and the path of recovery out of the current crisis? If you find no other sources you can concentrate on the last question.

Source:

Mukanjari, Samson and Sterner, Thomas (2020): Charting a “Green Path” for Recovery from COVID-19, in: Environmental and Resource Economics, Vol. 76, Iss.4, pp. 825 - 853.

      Supervisor: Justus Kalmbach

      Students: Luis Arangu, Nikolai Khomenko and Sharang Rastogi

 

6. Analysing the effects of screening

To fulfil the moral expectations of investors a typical approach of fund management is the use of screening criteria in the investment process. Fund managers can use positive or negative screens and can apply screening with respect to all SRI criteria or just specific aspects as environmental or ethical criteria.

You shall analyse research results, search further sources, and discuss the application of screening criteria. Using factor models: Are there different exposures of portfolios to the factors depending on the application of screening criteria? Is there an outperformance of portfolios with positive or negative screening? Give examples of funds that apply such criteria and compare their performance among each other and to an appropriate benchmark.

Sources:

Durand, Robert B.; Koh, SzeKee and Limkriangkrai, Manapon(2013): Saints versus Sinners. Does Morality Matter?, in: Journal of International Financial Markets, Institutions & Money, Vol. 24, pp. 166 - 183.

Ibikunle, Gbenga and Steffen, Tom (2017): European Green Mutual Fund Performance: A Comparative Analysis with their Conventional and Black Peers, in: Journal of Business Ethics, Vol. 145, Iss. 2, pp. 337 - 355.

Lesser, Kathrin; Rößle, Felix and Walkshäusl, Christian (2016): Socially responsible, green and faith-based investment strategies: Screening activity matters!, in: Finance Research Letters, Vol. 16, pp. 171 - 178.

      Supervisor: Justus Kalmbach

      Students: Simon Maier and Victor Manuel Castillo Olvera

 

7. Improvement of Machine Learning with Economic constraints

A growing body of literature shows that machine learning methods outperform classical linear models in explaining stock returns. Recent research indicates that this improvement in performance can mainly be attributed to nonlinearities and interactions among predictor variables. Despite its success, simple out-of-box machine learning models often tend to underperform simpler approaches. Chen et al. (2020) show that adding economic structure in form of the no-arbitrage condition significantly helps to guide the learning process and improves model performance.

Start your seminar paper by shortly introducing the fundamental no-arbitrage assumption behind asset pricing. Further, give an overview of the approach of Chen et al. (2020) and discuss their most important results. Similar to Table II in their paper, test how well common risk factor models can explain the excess returns of decile-sorted portfolios based on their risk loading estimates. Also test if their estimated stochastic discount factor can explain return variations of test assets commonly used in the literature.
 

Source:

Chen, L., Pelger, M. and Zhu J. (2020) Deep Learning in Asset Pricing. Available at SSRN: https://ssrn.com/abstract=3350138

A basic understanding of neural networks and deep learning methods may be helpful for this topic. Data will be provided by the supervisor.

      Supervisor: Niklas Paluskiewicz

      Students: Hanbei Guo and Shenghao Gu

 

8. Can Data be seen differently?

The digital decade gives researchers access to vast amounts of novel data that was previously unavailable or limited computational resources prevented its usage in academic research. However, advances in artificial intelligence and processing power also enables researchers to find new insights in already existing and accessible data. In a recent paper, Jiang et al. (2021) combine stock-level-price charts with machine learning image methods to identify predictive patterns previously not found in the literature.

First, introduce Convolutional Neural Networks and summarize how Jiang et al. (2021) implement them for stock return prediction. Discuss which patterns are identified by their learning algorithm and if the results can be transferred to other markets and/or time scales. In the second part of your paper, you should implement a stock return prediction framework based on images as input and compare it to a benchmark. Implementation details will be discussed with your supervisor.

Source:

Jiang, J., Kelly, B. and Xiu, D. (2021) (Re-)Imag(in)ing Price Trends. Available at SSRN: https://ssrn.com/abstract=3756587

A basic understanding of neural networks and deep learning methods may be helpful for this topic.

      Supervisor: Niklas Paluskiewicz 

      Students: Zhang Enguang and Vu Nguyen Thanh