Proposals for Master's theses

Please note our general information on theses. In addition, please note that most theses supervised by the institute have an empirical part. We therefore recommend that students interested in writing a Master thesis attend the modeling and research courses offered by the finance institutes (Financial Modeling, Research in Finance).


Do green bonds help the environment?

Green bonds are bonds whose proceeds are used for projects that benefit the environment. You shall update key parts of a recent paper on green bonds, and extend it through the analysis of additional research questions.   Contact: Prof. Löffler

Literature to get started: ElBannan, M.  & Löffler, G. (2022). How effectively do green bonds help the environment? Working paper.



Predictability of stock market returns

First you shall replicate the core parts of a recent paper on the long-run predictability of stock market returns. Then you shall examine whether additional variables also help to predict stock market returns. The selection of additional variables will be discussed in advance.   Contact: Prof. Löffler

Literature to get started: Golez, B., & Koudijs, P. (2018). Four centuries of return predictability. Journal of Financial Economics127(2), 248-263.



Prediction of industry returns

Your main taks is to update a recent paper that presents a machine learning approach for predicting industry returns and that also derives a trading strategy based on these predictions. You shall also apply other popular trading strategies to industry returns and compare the performance to that of the machine learning approach.

Literature to get started: Rapach, D. E., Strauss, J. K., Tu, J., & Zhou, G. (2019). Industry return predictability: A machine learning approach. The Journal of Financial Data Science, 1(3), 9-28.



Performance of value-at-risk models: an update

The last two years have seen a large number of extreme stock market moves, e.g.., in the early days of the Covid pandemic. In your thesis, you shall test for a representative set of value-at-risk (VaR) models how they performed during recent years here and how this performance compares to their prior performance.

Literature to get started: Nieto, M.R. and Ruiz, E., 2016. Frontiers in VaR forecasting and backtesting. International Journal of Forecasting32(2), 475-501.