Do (Audit Firm and Key Audit Partner) Rotations Affect Value Relevance? Empirical Evidence from the Italian Context
Bibliographic record
Abstract
This research aims to investigate whether and to what extent investors place more weight on accounting amounts disclosed in annual reports issued by entities that experienced a rotation of the audit firm or of the key audit partner. Analysing a sample of 97 non-financial entities listed in the Milan stock exchange over the period 2006-2014, the paper provides evidence that rotations positively affect the value relevance of accounting amounts. In addition, the paper shows how the audit firm rotation and the key partner rotation act only in part as substitutes, as the former is more capable than the latter of positively affecting the value relevance of earnings and book value. These results provide a theoretical contribution to the literature and have significant implications for standard setters. The rotations, even though could determine a loss of client-specific knowledge, nonetheless improve the value relevance of accounting amounts. New rules that require the key partner rotation have a positive effect as long as they do not consider such rotation as an alternative to the rotation of the audit firm being the partner rotation less capable to affect the value relevance of accounting amounts than the key audit firm rotation.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".