Isabel Walcher joins research platform

Isabel Walcher recently joined the research platform after accepting a position as university assistant at the Department of Banking and Finance under the supervision of Roland Mestel. Isabel studied Business Administration with a major in Finance at the University of Graz. Together with her supervisor, as well as Stefan Palan, Erik Theissen, and Alexander Brauneis, she will study the automated market making (AMM) trading mechanism. Her research focuses on comparing market performance and efficiency in AMM markets to that of the well-established continuous double auction.

Johanna Stauder joins research platform

Johanna Stauder recently joined the research platform as a PhD student and university assistant supervised by Univ.-Prof. Dr. Andrea Schertler at the Institute of Banking and Finance. Johanna completed her master's degree in Business Administration at the University of Graz and is currently working on her first research paper, exploring how governance structures, regulatory frameworks, and market discipline interact. Specifically, Johanna investigates stock market responses to banks' governance incidents, such as money laundering, corruption and fraud. Her dissertation will focus on banking, corporate governance, and risk management, emphasizing the interconnectedness of these areas in ensuring financial stability.

New paper on integrating multiple sources of ordinal information in portfolio optimization

In a new paper, recently accepted for publication in Annals of Operations Research, FiRe member Roland Mestel and his co-authors Eranda Cela, Stephan Hafner and Ulrich Pferschy present two different approaches for incorporating multiple qualitative views specified as total orders of the expected asset returns in a mean–variance portfolio optimization model. (1) In the robust optimization approach they first compute a posterior expectation of asset returns for every given total order by an extension of the Black–Litterman (BL) framework. Then these expected asset returns are considered as possible input scenarios for robust optimization variants of the mean–variance portfolio model. (2) In the order aggregation approach rules from social choice theory are used to aggregate the individual total orders into a single “consensus total order”. Then expected asset returns are computed for this “consensus total order” by the extended BL framework. Finally, these expectations are used as an input of the classical mean–variance optimization. Using empirical data the authors empirically compare the success of the two approaches in the context of portfolio performance analysis and observe that aggregating orders by social choice methods mostly outperforms robust optimization based methods.

Link to paper (open access)