Bibliographic record
Abstract
Purpose The purpose of this study is to examine whether audit quality influences auditees' liquidity policy. Design/methodology/approach The author uses ordinary least squares (OLS) estimators, and we focus on a panel of US publicly traded companies (36,118 company-year observations) over the period of 2004–2019 to examine the effect of audit quality on auditees' cash reserves. Findings The author finds that high quality audits are negatively related to auditees' cash reserves. Additional analyses show that the potential channel by which audit quality influences these reserves is financial constraints (FC). Particularly, his results suggest that an auditee's FC serve as an intermediary in the association between audit quality and auditee's cash reserves. Ultimately, we show that high quality audits raise the market value relevance of an extra dollar in cash reserves. Originality/value By linking two distinct research lines of audit quality and corporate cash reserves, this study adds to both lines of literature, as it is a novel one (to the best of the author’s knowledge) to provide evidence about the effect of audit quality on the auditees' liquidity policy (a real economic decision and internal financial policy) that ultimately boosts the auditees' investment efficiency. The author’s findings are consistent with influential monitoring and an insurance-like function of high quality audits in reducing information asymmetry and its consequences. His results also support the argument that auditees' transparency through high quality audits can be a pivotal determinant of their liquidity policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".