Interrelation of Capital Markets in the Context of Increased Audit Oversight in the European Union – Evidence on Third-Country Auditors
Bibliographic record
Abstract
Abstract We identified a notable lack of academic literature on the issue of third-country auditors and the main contribution of our article is to address this gap. This research builds on adjacent audit oversight and capital markets literature and we extend this literature by providing evidence on third-country auditors. Specifically, we test the relationship between market capitalization and number of foreign IPOs of listed companies in representative EU countries (on one hand) and the existence of third-country auditors in those respective countries (on the other hand). Our research was performed in the second half of 2018 and is based on the latest data available. We have found that there are about 200 third-country auditors present in the public registers of audit oversight bodies in 11 EU countries. According to our network analysis, only European countries with a developed capital market have attracted third-country auditors. Most of the relationships of these developed EU capital markets are nurtured with non-EU capital markets that are at the same level of development (e.g. USA, Switzerland, Canada, Israel, and Australia). Our research hypotheses were validated: (1) the higher the market capitalization of a EU country, the higher the likelihood for the registration of third-country auditors; (2) the higher the number of foreign IPOs relative to the total IPOs on the stock exchange market, the higher the likelihood for the registration of third-country auditors.
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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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".