Corporate bankruptcy prediction models applied to emerging economies
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
Purpose The paper's aim is to test the usefulness of ratio analysis to predict bankruptcy in a period of stability of an emerging economy, such as the case of Argentina in the 1990s. Design/methodology/approach Financial profiles of 22 bankrupt and healthy companies are examined and a model is built using the multiple discriminant analysis technique, thus providing comparability with previous studies. Findings The set of models tested in this paper show that the financial data of Argentine companies in the 1990s do have information content, but the model to use depends on the preferences of the decision maker. Comparing models it is observed a common use of solvency ratios in terms of total assets and profitability ratios in terms of sales. Research limitations/implications Data availability constitutes the primary limitation of this and similar studies, here is reflected in the sample size: 11 healthy and 11 bankrupt. Practical implications The model can be used to assist investors, creditors, and regulators in Argentina and other emerging economies to predict business failure. The Z ′‐score model of Altman can be used for public companies in emerging economies because it pays attention to solvency indicators, but in rapid changing environment, profitability ratios should also be considered. Originality/value The incremental information content of profitability and solvency in predicting bankruptcy is examined and a simple and reliable failure prediction model for large Argentinean firms is developed. Also this paper offers a classification method that is publicly available to all investors and creditors interested in Argentinean companies.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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