Bibliographic record
Abstract
Purpose – The purpose of this paper is to examine whether high analyst coverage increases or decreases accounting conservatism.\nDesign/methodology/approach – Sample firms were selected from the Compustat and I/B/E/S databases for years 1989-2006. The authors used both accrual-based and market-value-based measures of accounting conservatism, also the extent to which negative cash flow from operations is more timely recognized via accruals than positive cash flow from operations to measure accounting conservatism. The regression analyses are conducted to test the hypotheses.\nFindings – Strong evidence was found that analyst coverage is positively associated with accounting conservatism. The results suggest that firms choose more conservative accounting methods when they are followed by more analysts than when they are followed by fewer analysts. The results are robust to a battery of sensitivity analyses.\nOriginality/value – This paper sheds light on how analyst coverage affects firms' accounting choices and extends the limited research on the monitoring role of analyst coverage. The findings are consistent with the notion that analyst coverage plays an important corporate governance role in the financial reporting process. This paper also adds to the literature on the economic determinants of accounting conservatism, and provides some implications for practitioners.
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 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 | 0.009 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".