joint work with Dominik Walter (VGSF) and Rüdiger Weber (WU). July 2022.

Abstract: We study the size and drivers of non-standard errors (Menkveld et al., 2021) in portfolio sorts across 14 common methodological decision nodes and 40 sorting variables. These non-standard errors range between 0.05 and 0.26 percent and are, on average, larger than standard errors. Supposedly innocuous decisions cause large variation in estimated premiums, standard errors, non-standard errors, and t-statistics. The impact of decision nodes varies widely across sorting variables. Irrespective of choices in portfolio sorts, we find pervasively positive premiums and alphas for almost all sorting variables. This suggests that while the size of these premiums is uncertain, their sign is remarkably stable. Our code is publicly available.

Internet Appendix, open source code on Github

Presentations: Finance and Banking Conference 2022°, Austrian Working Group on Banking and Finance 2022°

joint work with Lukas Handler (WU) and Rainer Jankowitsch (VGSF & WU). April 2021.

Abstract: This paper provides a novel approach to empirically determine prices of bond covenants based on transaction data for the US corporate bond market. Thereby, we are the first to measure price effects over the whole lifetime of bond contracts. We find that covenant prices vary significantly over time and are associated with changes in market-wide credit risk, volatility, and macroeconomic variables. During and after the financial crisis, this relationship is particularly pronounced leading to significantly higher covenant prices in the range of 5% of the overall bond yields. Apart from these time-series dynamics, there is also significant variation across bond and firm characteristics. In particular, covenant prices increase with interest rate, credit, and liquidity risk and are higher for firms that have more growth options, more tangible assets, and are smaller. Furthermore, we document a positive correlation between the prices of covenants and their subsequent inclusion rates.

Presentations: Australasian Finance and Banking Conference 2021, Austrian Working Group on Banking and Finance 2021, EFMA 2022, DGF 2022°, Reykjavik University, SFA 2022°, VGSF Conference 2019

joint work with FINCAP team. November 2021.

Abstract: In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.