Fair value accounting and the cost of corporate bonds: the role of auditor expertise
Bibliographic record
Abstract
Purpose This study aims to examine the association between fair value accounting and the cost of corporate bonds, proxied by bond yield spread. In addition, this study explores the moderating role of auditor industry expertise at both the national and the city levels. Design/methodology/approach This study first examines the effect of the use of fair value on yield spread by estimating firm-level regression model, where fair value is the testing variable and yield spread is the dependent variable. To test the differential impact of the three levels of fair value inputs, this paper divides the fair value measures based on the three-level hierarchy, Level 1, Level 2 and Level 3, and replace them as the test variables in the regression model. Findings This study finds that the application of fair value accounting is generally associated with a higher bond yield spread, primarily driven by Level 3 estimates. The results also show that national-level auditor industry expertise is associated with lower bond yield spreads for Level 1 and Level 3 fair value inputs, whereas the impact of city-level auditor industry expertise on bondholders is mainly on Level 3 fair value inputs. Research limitations/implications The paper innovates by exploring the impact of fair value accounting in a setting that extends beyond financial institutions, the traditional area of focus. Moreover, most prior research considers private debt, whereas this study examines public bonds, for which investors are more likely to rely on financial reporting for their information about a firm. Finally, the study differentiates between city- and national-level industry expertise in examining the role of auditors. Practical implications This research has several practical implications. First, firms seeking to raise debt capital should consider involving auditors, with either industry expertise or fair value expertise, due to the roles that auditors play in safeguarding the reliability of fair value measures, particularly for Level 3 measurements. Second, from standard-setting and regulatory perspectives, the study’s findings that fair value accounting is associated with higher bond yield spread cast further doubt on the net benefits of applying a full fair value accounting regime. Third, PCAOB may consider enhancing guidance to auditors on Level 2 fair value inputs, to further enhance audit quality. Finally, creditors can be more cautious in interpretating accounting information based on fair value while viewing the employment of auditor experts as a positive signal. Originality/value First, the paper extends research on the role of accounting information in public debt contracting. Second, this study adds to the auditing literature about the impact of industry expertise. Finally, and more generally, this study adds to the ongoing controversy on the application of fair value accounting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".