Financial Statement Quality and Debt Contracting: Evidence from a Survey of Commercial Lenders
Bibliographic record
Abstract
Abstract We survey commercial bank lenders to better understand how they evaluate and react to variation in financial statement quality and how they view recent changes in accounting standards. A unique aspect of this study is that our respondents focus on medium‐size loans to private companies. In fact, more than 90 percent of the survey respondents primarily make credit decisions on loans between $250 thousand and $50 million. This is in contrast to prior archival research, which focuses primarily on very large loans to public firms or very small loans to private firms. We find that lenders in our sample distinguish among financial statements in terms of quality, including conservatism, primarily on the basis of accrual patterns and restatements. While this general result holds throughout our sample, financial statement quality is substantially more important for lenders making larger loans (over $10 million) as compared to very small loans (under $1 million). In addition, bank lenders are much more likely to respond to low‐quality reporting with collateral and guarantee requirements than with an increase in the interest rate charged. This finding is consistent for lenders making both larger and smaller loans. Finally, despite concerns in the academic literature, bank lenders in our sample actually hold a neutral‐to‐positive view of recent changes in accounting standards. In addition, most do not support current efforts to exempt private companies from some accounting standards.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".