Professor Dr Fabian Wöbbeking

Professor Dr Fabian Wöbbeking
Current Position

since 1/23

Head of the Research Group Data Science in Financial Economics

Halle Institute for Economic Research (IWH) – Member of the Leibniz Association

since 9/22

Economist in the Department of Financial Markets

Halle Institute for Economic Research (IWH) – Member of the Leibniz Association

since 12/22

Assistant Professor

Martin Luther University Halle-Wittenberg

Research Interests

  • data science
  • financial intermediation
  • systemic risk analytics

Fabian Wöbbeking joined the Department of Financial Markets in September 2022. He is Assistant Professor at Martin Luther University Halle-Wittenberg since December 2022. His research focuses on applications of data science methods to generate economic indicators from unstructured data as well as financial intermediation, risk management, systemic risk, machine learning, and Bayesian methods in finance.

Fabian Wöbbeking received his bachelor's and his master's degree from Frankfurt School of Finance & Management and his PhD degree from Goethe University Frankfurt.

Your contact

Professor Dr Fabian Wöbbeking
Professor Dr Fabian Wöbbeking
- Department Financial Markets
Send Message +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, No. 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, No. 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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Working Papers


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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