A causal approach to test empirical capital structure regularities
File(s)1-s2.0-S2405918822000125-main.pdf (899.38 KB)
Published version
Author(s)
Cenci, Simone
Kealhofer, Stephen
Type
Journal Article
Abstract
Capital structure theories are often formulated as causal narratives to explain which factors drive financing choices. These narratives are usually examined by estimating cross–sectional relations between leverage and its determinants. However, the limitations of causal inference from observational data are often overlooked. To address this issue, we use structural causal modeling to identify how classic determinants of leverage are causally linked to capital structure and how this causal structure influences the effect-estimation process. The results provide support for the causal role of variables that measure the potential for information asymmetry concerning firms’ market values. Overall, our work provide a crucial step to connect capital structure theories with their empirical tests beyond simple correlations.
Date Issued
2022-11
Date Acceptance
2022-09-09
Citation
The Journal of Finance and Data Science, 2022, 8, pp.214-232
ISSN
2405-9188
Publisher
Elsevier BV
Start Page
214
End Page
232
Journal / Book Title
The Journal of Finance and Data Science
Volume
8
Copyright Statement
© 2022 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article
under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Identifier
http://dx.doi.org/10.1016/j.jfds.2022.09.002
Publication Status
Published
Date Publish Online
2022-09-16