Abstract: This paper addresses the power of event studies in corporate bond markets. While an approach using standardized abnormal returns is well specified under standard conditions, we identify two market phenomena negatively impacting the informative value of results. In particular, we show that test power decreases rapidly in the presence of event-induced variance. Moreover, illiquidity becomes a material concern when the samples are geared towards above-average maturities and credit risks. Therefore, we suggest a refinement to the current standard approach and provide open-source tools to implement event studies.

Open-source code available via GitHub.

Presentations: DGF 2024°

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. 

Internet Appendix

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. 

Abstract: With the help of more than 700 reviewers, we assess the reproducibility of nearly 500 articles published in the journal Management Science before and after the introduction of a new Data and Code Disclosure policy in 2019. When considering only articles for which data accessibility and hardware and software requirements were not an obstacle for reviewers, the results of more than 95% of articles under the new disclosure policy could be fully or largely computationally reproduced. However, for 29% of articles, at least part of the data set was not accessible to the reviewer. Considering all articles in our sample reduces the share of reproduced articles to 68%. These figures represent a significant increase compared with the period before the introduction of the disclosure policy, where only 12% of articles voluntarily provided replication materials, of which 55% could be (largely) reproduced. Substantial heterogeneity in reproducibility rates across different fields is mainly driven by differences in data set accessibility. Other reasons for unsuccessful reproduction attempts include missing code, unresolvable code errors, weak or missing documentation, and software and hardware requirements and code complexity. Our findings highlight the importance of journal code and data disclosure policies and suggest potential avenues for enhancing their effectiveness.

Available open source via www.tidy-finance.org.

Buy a print version at www.routledge.com (affiliate link).

Available open source via www.tidy-finance.org.

Buy a print version at www.routledge.com (affiliate link).