Data Science in der Finanzökonomik

Diese Gruppe konzentriert sich auf die Entwicklung und Anwendung neuartiger Data-Science-Tools im Bereich der Finanzökonomik. Ein Schwerpunkt ist die Anwendung von Data-Science-Methoden zur Gewinnung ökonomischer Indikatoren aus unstrukturierten Daten wie Text- und Bilddaten oder Web-Scraping.

Finanzresilienz und Regulierung

Ihr Kontakt

Juniorprofessor Dr. Fabian Wöbbeking
Juniorprofessor Dr. Fabian Wöbbeking
Mitglied - Abteilung Finanzmärkte
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Referierte Publikationen


"Let Me Get Back to You" — A Machine Learning Approach to Measuring NonAnswers

Andreas Barth Sasan Mansouri Fabian Wöbbeking

in: Management Science, im Erscheinen


Using a supervised machine learning framework on a large training set of questions and answers, we identify 1,364 trigrams that signal nonanswers in earnings call questions and answers (Q&A). We show that this glossary has economic relevance by applying it to contemporaneous stock market reactions after earnings calls. Our findings suggest that obstructing the flow of information leads to significantly lower cumulative abnormal stock returns and higher implied volatility. As both our method and glossary are free of financial context, we believe that the measure is applicable to other fields with a Q&A setup outside the contextual domain of financial earnings conference calls.

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Correlation Scenarios and Correlation Stress Testing

Natalie Packham Fabian Wöbbeking

in: Journal of Economic Behavior and Organization, January 2023


We develop a general approach for stress testing correlations of financial asset portfolios. The correlation matrix of asset returns is specified in a parametric form, where correlations are represented as a function of risk factors, such as country and industry factors. A sparse factor structure linking assets and risk factors is built using Bayesian variable selection methods. Regular calibration yields a joint distribution of economically meaningful stress scenarios of the factors. As such, the method also lends itself as a reverse stress testing framework: using the Mahalanobis distance or Highest Density Regions (HDR) on the joint risk factor distribution allows to infer worst-case correlation scenarios. We give examples of stress tests on a large portfolio of European and North American stocks.

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