Juniorprofessor Dr. Fabian Wöbbeking

Juniorprofessor Dr. Fabian Wöbbeking
Aktuelle Position

seit 1/23

Leiter der Forschungsgruppe Data Science in der Finanzökonomik

Leibniz-Institut für Wirtschaftsforschung Halle (IWH)

seit 9/22

Wissenschaftlicher Mitarbeiter der Abteilung Finanzmärkte

Leibniz-Institut für Wirtschaftsforschung Halle (IWH)

seit 12/22


Martin-Luther-Universität Halle-Wittenberg


  • Data Science
  • Finanzintermediation
  • Analyse systemischen Risikos

Fabian Wöbbeking ist seit September 2022 wissenschaftlicher Mitarbeiter in der Abteilung Finanzmärkte und seit Dezember 2022 Juniorprofessor an der Martin-Luther-Universität Halle-Wittenberg. Er beschäftigt sich mit Data Science Methoden zur Generierung ökonomischer Indikatoren aus unstrukturierten Datensätzen und forscht zu den Themen Finanzintermediation, Risikomanagement, Systemische Risiken, Machine Learning und Bayesianische Methodik in der Finanzökonomie.

Fabian Wöbbeking hat an der Frankfurt School of Finance & Management studiert und an der Goethe-Universität Frankfurt promoviert.

Ihr Kontakt

Juniorprofessor Dr. Fabian Wöbbeking
Juniorprofessor Dr. Fabian Wöbbeking
- Abteilung Finanzmärkte
Nachricht senden +49 345 7753-851



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

Andreas Barth Sasan Mansouri Fabian Wöbbeking

in: Management Science, Nr. 10, 2023


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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Cryptocurrency Volatility Markets

Fabian Wöbbeking

in: Digital Finance, Nr. 3, 2021


By computing a volatility index (CVX) from cryptocurrency option prices, we analyze this market’s expectation of future volatility. Our method addresses the challenging liquidity environment of this young asset class and allows us to extract stable market implied volatilities. Two alternative methods are considered to compute volatilities from granular intra-day cryptocurrency options data, which spans over the COVID-19 pandemic period. CVX data therefore capture ‘normal’ market dynamics as well as distress and recovery periods. The methods yield two cointegrated index series, where the corresponding error correction model can be used as an indicator for market implied tail-risk. Comparing our CVX to existing volatility benchmarks for traditional asset classes, such as VIX (equity) or GVX (gold), confirms that cryptocurrency volatility dynamics are often disconnected from traditional markets, yet, share common shocks.

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How to Talk Down Your Stock Performance

Andreas Barth Sasan Mansouri Fabian Wöbbeking Severin Zörgiebel

in: SSRN Discussion Papers, 2020


We process the natural language of verbal firm disclosures in order to study the use of context specific language or jargon and its impact on financial performance. We observe that, within the Q&A of earnings conference calls, managers use less jargon in responses to tougher questions, and after a quarter of bad economic success. Moreover, markets interpret the lack of precise information as a bad signal: we find lower cumulative abnormal returns and a higher implied volatility following earnings calls where managers use less jargon. These results support the argument that context specific language or jargon helps to efficiently and precisely transfer information.

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