International Applicability of Corporate Failure Risk Models Based on Financial Statement Information: Comparisons across European Countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract: The objective of the study is, firstly, to analyse the predictability of financial distress in different European countries. Secondly, the objective is to compare predictability across countries. Thirdly, the objective is to investigate possibilities to develop a generic uniform model to predict distress in each country over Europe. The sample includes over one million active and tens of thousands financially distressed firms from 30 European countries. For each country, a prediction model of its own is estimated. The models and their performance in prediction accuracy are compared across countries. Finally, a uniform generic model is estimated for the sample including all countries and its prediction accuracy is assessed by country. The results show that there are differences in the form and strength of prediction models across different European countries. However, it is possible to develop a uniform generic model resulting in a reasonably high rate of classification accuracy for most countries.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it