I am an assistant professor of finance at Reykjavik University. My research interests focus on empirical asset pricing and, particularly, topics on the intersection between asset pricing and corporate finance for stocks and corporate bonds.
Tidy Finance with R is available as a print version from Chapman & Hall/CRC
Tidy Finance with Python is currently under development and scheduled for print release in 2024.
Additionally, we provide the entire material online and open source for everyone (including a blog).
Book: We signed a contract with Chapman & Hall/CRC for the publication of Tidy Finance with Python in 2024.
Conference: Non-Standard Errors in Portfolio Sorts will be presented at the DGF 2023 and EFA 2023.
I will join the DGF 2023 in Hohenheim to present Non-Standard Errors in Portfolio Sorts.
Dominik Walter will present Non-Standard Errors in Portfolio Sorts at the EFA 2023 in Amsterdam.
The print version of Tidy Finance with R (joint work with Christoph Scheuch and Stefan Voigt) was released on April 5, 2023.
Abstract: We systematically study the variation in returns induced by varying 14 methodological decisions in portfolio sorts. These non-standard errors range between 0.14 and 0.39 percent per month and are larger than standard errors. However, for most sorting variables, mean return differentials and alphas are pervasively positive, statistically significant, and increase monotonically. Decisions such as excluding firms with negative earnings or the information time lag have an impact comparable to size-related ones. Non-standard errors are countercyclical, raising concerns about non-classical measurement error in predictive regressions. Using our publicly available code to report distributions of estimated premia provides an easy remedy.
Presentations: Australasian 2022, AWG 2022, DGF 2023°, EFA 2023°, EFMA 2023*, PFMC 2022*, University of Vienna
Abstract: In this paper, we analyze the key drivers of bond covenant prices by employing a novel measurement approach based on secondary market data. We find that covenant prices vary significantly over time and are associated with market-wide credit risk, volatility, and macroeconomic variables. Apart from the time-series dynamics, there is also significant variation across bond and firm characteristics. In particular, covenant prices increase with the riskiness of bonds 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 2021, AWG 2021, EFMA 2022, DGF 2022, Reykjavik University, SFA 2022*, VGSF Conference 2019
Refereed Journal Publications
Published in The Journal of Financial Economics.
Abstract: We show that firms with longer debt maturities earn risk premia not explained by unconditional factors. Embedding dynamic capital structure choices in an asset-pricing framework where the market price of risk evolves with the business cycle, we find that firms with long-term debt exhibit more countercyclical leverage. The induced covariance between betas and the market price of risk generates a maturity premium similar in size to our empirical estimate of 0.21% per month. We also provide direct evidence for the model mechanism and confirm that the maturity premium is consistent with observed leverage dynamics of long- and short-maturity firms.
Presentations: BI Norway (BB 2017)*, VGSF Conference 2017, ESSFM/Gerzensee 2018*, DGF 2018, University of Lugano (2018)*, AFA 2019*, Cass*, CAFIN Workshop 2019, CEU*, SFS Cavalcade 2019*, FIRS 2019*, TAU Finance Conference 2019*, HEC-McGill Winter Finance Workshop*
Forthcoming in The Journal of Finance.
Joint work as part of the FINCAP team.
Abstract: In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in 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 (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.
(* presentation by coauthor; ° scheduled)