• The seminar places will be allocated in one application round. The allocation is done manually, the time of application (within one application round) is not important. As criteria, we take into account both grades and previous knowledge in the area in Finance (from lectures, seminars, etc.). In addition, we also take into account the priority of the application as stated in the wiwi portal.
• When applying, you have the possibility to indicate your desired topics. We will try to take your preferences into account as far as possible. The final allocation of topics will only be communicated after the second application round has been completed.
• Please note that we consider the acceptance of an allocated seminar place via the wiwi portal as a binding registration for the seminar. After that, a withdrawal from the seminar is only possible in justified exceptional cases after prior consultation.
• As an official start of the seminar we will hold a joint kick-off.
• Relevant for grading are an individual thesis and a group presentation in a block seminar.

The prices of stocks aggregate all available information. But exactly what information is relevant to investors in their valuation is often unclear. Advances in data science have recently enabled financial market researchers to understand much better what information is relevant for asset prices. This seminar will discuss various recent works in this area. In addition, various results will be complemented by students' own analyses of a data set provided by the chair.

Further details including the specific topics will be available soon on our homepage and on the WiWi portal.

- Application deadline: 24 March 2023, 23:55

- Block seminar: 13./14. July 2023

Eine zentrale Fragestellung im Asset Pricing ist die Vorhersagbarkeit von Aktienrenditen. Werden Variablen identifiziert, die es ermöglichen Aktienrenditen vorherzusagen, stellt dies aber nicht zwangsläufig eine Arbitragemöglichkeit dar. Es kann sich dabei auch um eine Kompensation in Form einer Risikoprämie für riskantere Investitionen handeln. Ziel des Seminars ist es zu analysieren, inwiefern verschiedene Machine Learning Methoden unter Verwendung von Optionsdaten dazu beitragen, die Aktienrenditen im Querschnitt besser zu erklären.

Weitere Details inklusive den Themenbeschreibungen gibt es in Kürze auf unserer Homepage und im WiWi-Portal.

Termine:

- Bewerbungsfristen: 01.04.2022

- Blockseminar: 14./15.07.2022