Auditor changes and management's issuance of earnings forecasts
Bibliographic record
Abstract
Abstract Auditor changes are significant corporate events marking disruptions in the auditor‐client relationship. Prior studies have primarily examined the impact of such changes on audit quality and investment decisions of market participants. We study the effect of auditor changes on the voluntary disclosure of forward‐looking information. Managers may choose to reduce disclosure due to the possible adverse effect of the disruptions on disclosure credibility. Alternatively, shareholders may demand increased disclosure to intensify monitoring, as the auditor change signals potential issues between the company and the auditor. Employing multiple identification strategies, we find that firms are less likely to issue management earnings forecasts (MEFs) following auditor changes. We also find that governance quality mitigates the negative impact of auditor changes on the issuance of MEFs. Additionally, auditor changes are associated with lower market reactions to forecast releases. The overall evidence is consistent with the notion of reduced forecast credibility. Lastly, we conduct cross‐sectional analyses on characteristics of the auditor changes and find evidence consistent with signaling and anticipated successor audit quality to be underlying mechanisms for the association between auditor changes and MEFs. Our study provides the first large sample evidence that auditor changes have a disruptive effect on the voluntary disclosure of forward‐looking information.
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 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".