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What Do Analysts' Provision Forecasts Tell Us about Expected Credit Loss Recognition?

The Accounting Review 2021 96(1), 1-21 open access
We document potential cross-sectional differences in how expected loss accounting will affect provision timeliness to provide important policy insights and contribute to the literature regarding the estimation of the expected loss model adoption impact and provision timeliness determinants. Our findings that analyst provision forecasts incrementally predict future nonperforming loans (NPLs) and market returns suggest that the incurred loss provision does not incorporate all available future loss information. Higher incremental coefficients on provision forecasts for banks with greater unrecognized future losses and incurred loss constraints suggest CECL could affect cross-sectional provision timeliness differences by removing these constraints. Specifically, the provision forecast and future NPL association increases with banks' unconstrained future loss estimates reflected in loan fair value disclosures and incurred loss constraints indicated by heterogeneous loans individually reviewed for impairment. This association also increases with EPS forecast errors, but decreases with target price and NPL forecast errors.

Financial Reporting Quality, Private Information, Monitoring, and the Lease-versus-Buy Decision

The Accounting Review 2010 85(4), 1215-1238 open access
A flourishing stream of research suggests that liquidity-constrained firms with low accounting quality have limited access to capital for investments. We extend this research by investigating whether these firms are more likely to lease their assets. Lessors’ superior control rights allow them to provide capital to constrained firms with low-quality accounting reports. Consistent with this conjecture, we find that low accounting quality firms have a higher propensity to lease than purchase assets. To verify that leasing does not merely reflect these firms’ desire for off-balance-sheet accounting, we investigate whether banks’ access to private information and monitoring affect the relation between accounting quality and leasing. We find the association between accounting quality and leasing decreases when banks have higher monitoring incentives and when loans contain capital expenditure provisions. These results suggest that other mechanisms can substitute for the role of accounting quality in reducing information problems.

How Good Is Goodwill Accounting? Comparative Evidence on Post-FAS 141(R) Acquired Intangibles Accounting

The Accounting Review 2026 101(3), 1-37 open access
Concerns that goodwill impairments unresponsiveness to declining performance produces inflated goodwill led standard-setters to reconsider post-acquisition impairment-only accounting. We use granular large-scale data to provide institutionally relevant novel descriptive evidence motivated by this hotly debated accounting standard. Comparing goodwill versus other acquired intangibles growth rates for firms reporting goodwill throughout the 2010–2020 post-FAS 141(R) period provides no evidence of runaway goodwill inflation concerns. For firms with goodwill anytime during 2010–2020 we use Shapley values to explore the explanatory power of performance factors affecting goodwill impairments. Consistent with standard-setters’ intent, single-segment market performance explains 81 percent of goodwill impairment incidence variation (controlling for Fama-French-38 industry and time fixed-effects). Limited evidence of reduced impairment incidence after incorporating FASB sanctioned control premia or alternative market values provides little support for discretionary impairment avoidance. Conversely, higher impairment incidence when book values incorporate IFRS (2020) proposed off-balance-sheet headroom or market-to-book decreases supports discretionary impairment recognition. Data Availability: The data used in this study are obtained from commercial sources, including Compustat, Calcbench, I/B/E/S, FactSet Mergerstat/BVR, and Peters and Taylor's intangible capital dataset.