JOURNAL ARTICLE

Accounting for Asymmetry in the Investment– q Relation: Redux of Financial Reporting Quality and Investment Efficiency.

  • Published In: Management Science (INFORMS), 2026, v. 72, n. 4. P. 3219 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Dutta, Sunil; Langer, Lukasz; Patatoukas, Panos N. 3 of 3

Abstract

This article investigates how the commonly used linear regression model linking corporate investment to Tobin's q—a ratio of the market value of capital to its replacement cost—misrepresents investment efficiency by ignoring asymmetry in capital adjustment costs. The authors find that investment is significantly less sensitive to Tobin's q values below one than above one, and firms with q below one tend to have lower financial reporting quality. Because prior studies pool observations with q above and below one, they overstate the positive association between financial reporting quality and investment efficiency. By proposing a piecewise linear model that accounts for this kink at q = 1, the study shows that the previously documented link between higher financial reporting quality and greater investment responsiveness, as well as reduced overinvestment, weakens substantially. The findings highlight the importance of modeling nonlinear investment–q relations, especially since the likelihood of q falling below one correlates systematically with financial reporting quality measures such as accrual quality, reporting complexity, analyst following, and Big-4 audit status.

Additional Information

  • Source:Management Science (INFORMS). 2026/04, Vol. 72, Issue 4, p3219
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2026
  • ISSN:0025-1909
  • DOI:10.1287/mnsc.2024.08732
  • Accession Number:192910502
  • Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences 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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