Bibliographic record
Abstract
The aim of this research is to describe corporate bankruptcy across Western European countries and propose a simple and reliable default prediction model for private manufacturing firms in six EU member states. Using firm-level accounting data taken from the Orbis-Europe Database, published by Bureau Van Dijk, we first propose a simple Indebtedness index which considers the multifaceted aspects of debt and allows to make interesting comparison among firms, countries, industrial sectors and over time. Second, we estimate a logit model, based on both the first step computed Indebtedness score and additional non-financial firms’ characteristics, which allows to compute firms’ predicted probabilities of default in each country. The empirical findings show that the Indebtedness score is statistically significant in explaining bankruptcy and it enters all the regressions with the highest coefficient and level of significance. However, while the indebtedness score is a valuable bankruptcy predictor for Italy, Germany, Portugal and Spain, which are bank-based economies, it is relatively less important for France and UK, being countries more strongly oriented toward the financial market. The overall evidence highlights a good reliability of our multi-country model for the prediction of corporate bankruptcies across Europe.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".