A causal approach to test empirical capital structure regularities

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...

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Main Authors: Simone Cenci, Stephen Kealhofer
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2022-11-01
Series:Journal of Finance and Data Science
Subjects:
C1
C4
G3
Online Access:http://www.sciencedirect.com/science/article/pii/S2405918822000125
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author Simone Cenci
Stephen Kealhofer
author_facet Simone Cenci
Stephen Kealhofer
author_sort Simone Cenci
collection DOAJ
description 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.
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spelling doaj.art-57b954d394ef48638cdaa284cff72cea2023-02-19T04:26:25ZengKeAi Communications Co., Ltd.Journal of Finance and Data Science2405-91882022-11-018214232A causal approach to test empirical capital structure regularitiesSimone Cenci0Stephen Kealhofer1Leonardo Centre, Imperial College Business School, Exhibition Rd, London SW7 2BX, UK; Corresponding author.Blackstone Credit, San Francisco, 101 California St., CA, 94111, USACapital 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.http://www.sciencedirect.com/science/article/pii/S2405918822000125C1C4G3
spellingShingle Simone Cenci
Stephen Kealhofer
A causal approach to test empirical capital structure regularities
Journal of Finance and Data Science
C1
C4
G3
title A causal approach to test empirical capital structure regularities
title_full A causal approach to test empirical capital structure regularities
title_fullStr A causal approach to test empirical capital structure regularities
title_full_unstemmed A causal approach to test empirical capital structure regularities
title_short A causal approach to test empirical capital structure regularities
title_sort causal approach to test empirical capital structure regularities
topic C1
C4
G3
url http://www.sciencedirect.com/science/article/pii/S2405918822000125
work_keys_str_mv AT simonecenci acausalapproachtotestempiricalcapitalstructureregularities
AT stephenkealhofer acausalapproachtotestempiricalcapitalstructureregularities
AT simonecenci causalapproachtotestempiricalcapitalstructureregularities
AT stephenkealhofer causalapproachtotestempiricalcapitalstructureregularities