Predicción de quiebras empresariales en economías emergentes: uso de un modelo logístico mixto // Bankruptcy Prediction in Emerging Economies: Use of a Mixed Logistic Model
Bibliographic record
Abstract
Este trabajo replica y adapta el modelo de Jones y Hensher (2004) a los datos de una economía emergente con el propósito de evaluar su validez externa. Se compara el desempeño del modelo logístico estándar en relación con el modelo logístico mixto para predecir el riesgo de crisis en el periodo 1993-2000, utilizando estados contables de empresas argentinas y ratios definidos en estudios de Altman y Jones y Hensher. Como en estudios anteriores, rentabilidad, rotación, endeudamiento y flujo de fondos operativos explican la probabilidad de crisis financiera. La contribución de esta nueva metodología reduce la tasa de error del tipo I a un 9 %. Se demuestra que el modelo logístico mixto, que tiene en cuenta la heterogeneidad no observada, supera ampliamente el desempeño del modelo logístico estándar. || This study is a replication and adaptation of Jones and Hensher (2004) model in an emerging economy with the purpose of testing its eternal validity. It compares the logistic standard model's performance with the logistic mixed model to predict bankruptcy risk of Argentinean companies between 1993-2000 by using financial statements and ratios defined in previous studies by Altman and Jones and Hensher. Similar to previous studies, profitability, asset turnover, debt and cash flow from operations explain financial distress' probability. The main contribution of this new methodology is the important reduction of error type I to the 9 %. This study asserts that the logistic mixed model, that considers the effect of non-observed heterogeneity, significantly improves the performance of the logistic standard model.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".