JOURNAL ARTICLE

Corporate Investment and Financing Dynamics.

  • Published In: Review of Corporate Finance Studies, 2024, v. 13, n. 3. P. 625 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Hackbarth, Dirk; Sun, Dongming 3 of 3

Abstract

The article investigates the behavior of corporate leverage ratios within a dynamic trade-off model that incorporates multiple stages of irreversible investment and endogenous financing decisions. It develops and solves two versions of the model—a single-stage benchmark and a multistage model with sequential investment options—highlighting an intertemporal effect where firms underutilize debt initially to preserve financial flexibility for future investment stages. Structural estimation using simulated method of moments (SMM) on COMPUSTAT data reveals that firms with back-loaded investment opportunities tend to have lower leverage ratios, while front-loaded investment firms exhibit higher leverage, explaining observed heterogeneity in leverage targets. Simulated capital structure regressions replicate empirical stylized facts, including the negative leverage-profitability and leverage-growth relations, and the model accounts for the persistence and path-dependence of leverage ratios over decades, addressing puzzles such as low and zero leverage in firms. Overall, the study emphasizes the critical role of the investment process’s structure in shaping corporate financing behavior and suggests that recognizing this heterogeneity is essential for understanding capital structure dynamics.

Additional Information

  • Source:Review of Corporate Finance Studies. 2024/08, Vol. 13, Issue 3, p625
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2024
  • ISSN:2046-9128
  • DOI:10.1093/rcfs/cfad009
  • Accession Number:178650316
  • Copyright Statement:Copyright of Review of Corporate Finance Studies is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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