The Effect of Private Information and Monitoring on the Role of Accounting Quality in Investment Decisions
Bibliographic record
Abstract
We investigate how private information and monitoring affect the role of accounting quality in reducing the investment–cash flow sensitivity. We argue that access to private information and direct restrictions on investments are likely to affect the extent to which accounting quality reduces financing constraints. Our results suggest that, for financially constrained firms, banks’ access to private information decreases the value of accounting quality. We further find that, for both financially constrained and unconstrained firms, covenants directly restricting capital expenditures also mitigate the importance of accounting quality. Our results suggest that, when information asymmetry problems are likely to be the largest, accounting quality is most important. However, the importance of accounting quality is mitigated if outside capital suppliers have access to private information and is eliminated if they impose contractual restrictions on investment. We also provide evidence that banks’ access to private information reduces the cash flow sensitivity of cash and mitigates the importance of accounting quality in reducing this sensitivity. This additional evidence suggests that our investment–cash flow sensitivity results are not driven by measurement error of the investment opportunity set.
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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 | 0.012 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".