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Professor Dr Fabian Woebbeking

Professor Dr Fabian Woebbeking
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

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Research Interests
  • data science
  • financial intermediation
  • systemic risk analytics

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

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Professor Dr Fabian Woebbeking
Professor Dr Fabian Woebbeking
- Department Financial Markets
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Publications

Citations
146

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The Limits of Local Laws in Global Supply Chains: Cutting Ties or “Edutrading” Procurement Partners?

Hendrik Keilbach Michael Koetter Melina Ludolph Fabian Woebbeking

in: Journal of Development Economics, Vol. 182 (June), 2026

Abstract

We study the procurement patterns of non-listed firms and examine how these often-overlooked, yet pivotal players in global supply chains adjust their sourcing when they anticipate accountability for externalities beyond their organizational boundaries. Using granular customs data and a surprise information release about the German Supply Chain Due Diligence Act, product-level regressions reveal that importing firms are 3.5 percentage points less likely to source a product from countries where the relevant production sector exhibits elevated ESG-related risks, suggesting that firms tend to cut ties with higher-risk suppliers. The effects are concentrated among firms with well-diversified supplier networks for a product and higher profitability, suggesting they have the necessary flexibility to respond quickly to anticipated regulatory pressure. Our findings suggest that mandates requiring firms to incorporate broad sustainability considerations into their operational decisions may have limits, particularly for non-listed firms.

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

Andreas Barth Sasan Mansouri Fabian Woebbeking

in: Management Science, Vol. 69 (10), 2023

Abstract

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 Woebbeking

in: Journal of Economic Behavior and Organization, Vol. 205 (January), 2023

Abstract

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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Working Papers

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The Price of Beauty: Biodiversity Effects on Residential Housing Markets

Michael Koetter Birte Winter Fabian Woebbeking

in: IWH Discussion Papers, No. 21, 2025

Abstract

We study how and why local biodiversity affects residential property values. Leveraging remotely sensed greenness indicators and a novel dataset of granular property listings, we examine how changes in vegetation load on real estate prices. Hikes in greenness are associated with higher listing prices, fewer properties listed, and reduced liquidity in housing markets. These results suggest that price hikes in housing markets are driven by supply-side constraints instead of a “greenium” that buyers might be willing to pay due to innate preferences. Exogenous zoning shocks to foster biodiversity corroborate the presence of supply side constraints as price drivers in residential housing markets. Our findings emphasize the need to calibrate biodiversity and (social) housing policy objectives more explicitly.

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Information Flow and Market Efficiency - The Economic Impact of Precise Language

Andreas Barth Sasan Mansouri Fabian Woebbeking

in: IWH Discussion Papers, No. 13, 2025

Abstract

This paper examines the impact of complex yet precise language, particularly financial jargon, on information dissemination and ultimately market efficiency. As a natural laboratory, we analyze the information exchanged during earnings conference calls, where we instrument jargon with the Plain Writing Act of 2010. Our findings suggest that the Act‘s promotion of plain language usage results in a reduction in complex financial jargon for US firms. However, in contrast to the presumed benefits of accessible language, this reduction in jargon is associated with a decrease in market efficiency, implying that the Act may inadvertently hinder information flow. This finding is particularly important at the juncture where human-generated information is received by machines, which are known to be vunerable to ambiguous inputs.

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Contractionary Macroprudential Policy, Collateral Valuation, and Risk-shifting in EU Banking

Michael Koetter Felix Noth Fabian Woebbeking

in: IWH Discussion Papers, No. 4, 2025

Abstract

We study real estate lending responses to tighter macroprudential policy (MPP) in the form of lower required loan-to-value (LTV) ratios. Contract details of 2.4 million mortgage loans originated between 2008 and 2020 reveal significantly fewer new loan issuances in response to contractionary MPP, commensurate with an average reduction in aggregate lending of 21 percent. Loan-level analyses reveal, however, that banks comply with lower LTVs by systematically more benevolent valuations of residential real estate pledged as collateral instead of reducing loan size. Exploiting earthquakes as plausible exogenous shocks to property values corroborates these risk-shifting patterns by banks in the form of inflated property valuations after LTV shocks.

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