Seminar "Selected Topics from Finance"

The seminar is open to Master students.

In this seminar, we will study research from two fields: (i) predictability and (ii) financial research on carbon emissions

To successfully pass the seminar you need to write a paper and give a presentation. Papers can be written in either German or 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. You need to hand in a printed version and also a digital one (PDF). The seminar talks should be given 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.

 

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. 30.01.2023 - Thu. 02.02.2023 Students must submit their preferences over seminars for the first matching round. Link to platform for submission of preferences:  English     German
  • Fri. 03.02.2023 1st round of seminar matching.The matching algorithm runs during that day.
  • Tue. 07.02.2023 2nd round of seminar matching.The matching algorithm runs during that day.
  • Thu. 16.02.2023 15:00  General information about seminar, introductory meeting 
  • Fri. 16.02.2023 -  Sun 05.03.2023    Topic allocation on Taddle
  • Mon. 03.04.2023 - Sun. 23.04.2023     Registration at the Higher Services Portal
  • Until Fri. 28.04.2023     Contact your supervisor to discuss the outline of the paper
  • Sun 04.06.2023      Submission of the paper
  • Fri. 16.06- Sat. 17.06.2023     Presentations (detailed schedule to be announced later)

Topics

The list of topics will be extended, but the topics to be added will be similar to the ones already listed here.

General Remark: All topics include an empirical part for which you will prepare and analyze data. We will provide you with hints on how to download or obtain the data. You can choose which software to use to analyze the data. In your presentation, you shall also give an insight into your data preparation and coding. Do  n o t  ask the authors of the original papers if they can provide you with their code. We expect you to do the coding yourself, and we expect all members of a team to be familiar with the coding and data preparation. 

In addition to the related literature provided below please have a look at Landis, Conrad and Skouras, Spyros, Guidelines for asset pricing research using international equity data from Thomson Reuters Datastream (August 2, 2018). Available at SSRN: https://ssrn.com/abstract=3225371 for more information on how you can derive high quality international equity data from Datastream. 

Carbon emissions in financial research (list of topics will be extended before the topic allocation)

1.    Expected and unexpected carbon emissions and the market value of firms
Present the approach that Ott and Schiemann (2022) use for assessing the value relevance of carbon emissions. Then replicate and update key parts of their analysis using the most recent data available. Your supervisor will tell you which parts of the paper you should replicate and update.
Literature: Ott, C. and Schiemann, F., 2022. The market value of decomposed carbon emissions. Journal of Business Finance & Accounting.
 

2.    Board diversity and carbon emissions 
Present the approach that Rjiba and Thavaharan (2022) use for assessing whether there is an association between female representation on boards and carbon emissions. Then replicate and update key parts of their analysis using the most recent data available. Your supervisor will tell you which parts of the paper you should replicate and update. As part of your analysis, you shall also test whether the results are robust to defining carbon intensity without taking logarithms.
Literature: Rjiba, H. and Thavaharan, T., 2022. Female representation on boards and carbon emissions: International evidence. Finance Research Letters, 49, p.103079.

3.    Carbon emissions and default risk
Present the approach that Kabir et al. (2021) use for assessing whether there is an association between carbon emissions and default risk. Then replicate and update key parts of their analysis using the most recent data available. Your supervisor will tell you which parts of the paper you should replicate and update. 

Literature: Kabir, M.N., Rahman, S., Rahman, M.A. and Anwar, M., 2021. Carbon emissions and default risk: International evidence from firm-level data. Economic Modelling103, p.105617.

Stock market predictability (list of topics will be extended before the topic allocation)


4.    Equity premium prediction with combination forecasts
Present the combination approach for equity premium prediction used in Rapach, Strauss, and Zhou (2010). Then replicate and update key parts of their analysis using the most recent data available. Your supervisor will tell you which parts of the paper you should replicate and update.
Literature: Rapach, D.E., Strauss, J.K. and Zhou, G., 2010. Out-of-sample equity premium prediction: Combination forecasts and links to the real economy. The Review of Financial Studies, 23(2), pp.821-862.
 

5.    Economic growth and stock market returns 
Present the approach for equity premium prediction used in Møller and Rangvid (2015). Then replicate and update key parts of their analysis using the most recent data available. Your supervisor will tell you which parts of the paper you should replicate and update.

Literature: Møller, S.V. and Rangvid, J., 2015. End-of-the-year economic growth and time-varying expected returns. Journal of Financial Economics, 115(1), pp.136-154.

6.    Predicting the returns on industry portfolios 
Present the approach for the prediction of industry returns used in Rapach et al. (2019). Then replicate and update key parts of their analysis using the most recent data available. Your supervisor will tell you which parts of the paper you should replicate and update.

Literature: Rapach, D.E., Strauss, J.K., Tu, J. and Zhou, G., 2019. Industry return predictability: A machine learning approach. The Journal of Financial Data Science1(3), pp.9-28.

 

News

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Instructors

Gunter Löffler

Wasif Syed Hussain

Niklas Paluskiewicz

 

Dates and Room

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Module description

This seminar is open for Master students.