Creditors’ and Shareholders’ Reporting Demands in Public Versus Private Firms: Evidence from Europe
Bibliographic record
Abstract
In this study we investigate whether the importance of accounting information in contracting and communication with shareholders and creditors affects earnings timeliness in publicly disclosed general‐purpose financial statements. To operationalize the relationship between timeliness demands and the importance of accounting information to shareholders and creditors, we compare the (asymmetry in) earnings timeliness of public firms with that of private firms. We attribute public versus private firm differences in timeliness to shareholders’ demands when a country’s institutions provide strong investor protection. Similarly, we attribute these differences to creditors’ demands when the institutions provide strong creditor protection. Our analysis of public and private firms in 13 Western European countries suggests that creditors and shareholders have different timeliness demands. In particular, we find that the public versus private firm difference in asymmetric timeliness is not associated with a country’s degree of investor protection but positively associated with a country’s degree of creditor protection. The results further suggest that shareholders demand symmetric rather than asymmetric timeliness. An important implication of our study is that general‐purpose financial statements are responsive to creditors’ reporting demands, which contrasts with the idea that these — primarily private — creditors would use special‐purpose reports.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".