Auditor Opinion, IFRS Adoption and Macro Determinants of Financial Distress
Bibliographic record
Abstract
This study aims to investigate the impact of auditor opinion, IFRS adoption, and macro-level factors on financial distress using a sample of 221 non-financial UK manufacturing firms listed between 2014 and 2023. A panel fixed-effects regression model is applied to test the research hypotheses, with Altman’s Z-Score serving as a proxy for financial distress. The findings reveal a significant negative association between IFRS adoption and Altman’s Z-Score, while auditor opinion exhibits a significant positive relationship with the Z-Score. Additionally, strong evidence suggests that a composite measure of country-level index variables is significantly linked to higher financial distress. This paper makes a valuable contribution to the financial distress literature by addressing the limited research on the predictive role of IFRS adoption and auditor opinion in financial distress. Furthermore, by examining macro-level influences, this study adds to the existing literature, which predominantly focuses on firm-specific factors.
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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 | 0.002 | 0.009 |
| 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 | 0.000 | 0.000 |
| 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".