• Working Paper

    • Loan Officer Specialization and Credit Defaults

      Joint work with Peter-Hendrik Ingermann

      Abstract: This paper shows that industry specialization of loan officers facilitates monitoring synergies and lowers credit default rates of small and medium-sized enterprises. We exploit a wave of early loan officer retirements as a quasi-natural experiment, in which the resulting borrower reallocations changed the industry specialization levels of the remaining loan officers. In a difference-in-differences analysis excluding all reallocated borrowers, we find that a negative shock to loan officer specialization increases default rates due to an inferior production of default risk information and excessive loan growth. A positive shock to loan officer specialization generates opposite effects. Our results suggest that loan officers can exploit industry specialization and related monitoring synergies to improve lending decisions and thereby contribute to lowering credit default rates in the bank’s borrower portfolio.

      Available at SSRN

    • The Benefits of Downside Risk Reduction Through Coinsurance

      Joint work with Lars Norden and Christian Rose

      Abstract: We investigate the benefits of downside risk reduction through coinsurance in diversified firms. Using a novel coinsurance measure based on industry default risk connections derived from credit default swap (CDS) spread changes of single-segment firms, we isolate the effects of downside risk reduction from upside potential in multi-segment firms. We find that diversified firms realize significantly larger debt-related coinsurance benefits (lower cost of debt and/or higher leverage) than suggested by prior studies based on total risk proxies. Coinsurance is costly for shareholders and has no effect on the WACC. However, the impact of coinsurance on the WACC and firm value strongly varies with financial constraints. When financial constraints are at intermediate levels, coinsurance creates value for debt holders, shareholders, and the overall firm. Important identification issues are addressed. Our findings shed new light on how diversified firms benefit from downside risk reduction through coinsurance.

      Available at SSRN

    • Ambiguity Aversion and Epistemic Uncertainty

      Joint work with Craig Fox and David Tannenbaum

      Abstract: We propose that ambiguity aversion reflects distaste for betting on one’s relative ignorance, but only to the extent that uncertainty is seen as inherently knowable (epistemic). In contrast, uncertainty viewed as random (aleatory) can provide an attractive hedge against betting on one’s ignorance. We refer to this account as the epistemic uncertainty aversion hypothesis, which allows for the simultaneous observation of ambiguity aversion and preference for compound lotteries involving both chance and ambiguity. In preregistered experiments involving Ellsberg urns and naturalistic events we show: (1) under conditions of ignorance, ambiguity averse decision makers prefer betting on a greater balance of aleatory to epistemic uncertainty; (2) this preference for an aleatory hedge increases as subjective knowledge decreases, and can lead decision makers to choose stochastically dominated alternatives; (3) an uncertain prospect can be framed as more epistemic or aleatory to influence its overall attractiveness. These findings collectively violate several prominent models of ambiguity aversion, but can be accomodated by generic source models.

      Available at SSRN

    • Getting more Wisdom out of the Crowd: The Case of Competence-Weighted Aggregates

      Joint work with Enrico Diecidue, Andreas Jacobs, and Thomas Langer

      Abstract: This paper shows that group discussions can serve as an instrument to improve individuals’ calibration, which in turn strongly increases the accuracy of competence-weighted, statistical aggregates. We conduct an experiment in which participants estimate quantities and report their self-perceived competence for various judgment problems. In addition, they engage in group discussions with other judges on unrelated judgment tasks. We find that prior to participating in the group discussions, judges’ self-perceived competence and their estimation accuracy are poorly aligned, which causes competence weighting to perform worse than prediction markets and simple averaging. However, the information exchange facilitated by the group discussions improved judges’ calibration, raising the accuracy of competence-weighted aggregates on subsequent judgment problems to prediction market levels and beyond.

      Available at SSRN