Business Failure Prediction: A Tri-dimensional Approach
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
Investigations of corporate failure prediction research usually implement binary classification into one of the distinguished groups – Distress or non-Distress companies. This study looks at a tri-dimensional approach which cluster firms into three (3) distinct dimensions namely - non-distress, semi-distressed and distressed. The study used secondary data from 2011 to 2015 obtained from the Ghana Stock Exchange (GSE) spanning across six industries, namely, Banking & Finance, Distribution, Food & Beverage, Insurance, Manufacturing and Mining & Oil. The study initially adopted the Altman (1968) Z score bankruptcy model to classify companies into non-distress, semi-distressed and distressed. Further analysis was conducted using the Hierarchical agglomerative cluster analysis to cluster companies into non-distress, semi-distressed and distressed. A comparison was then made between the Hierarchical agglomerative clustering against the Altman (1968) Z score bankruptcy classification to obtain higher classification. The outcome of the analysis revealed that the Hierarchical agglomerative cluster analysis and the Altman (1968) Z score bankruptcy model can both be used to classify companies into nondistress, semi-distressed and distressed based on the tri-dimensional approach instead of the binary classification (distressed and non-distressed). The study recommends that future research can explore other clustering methods for bankruptcy prediction to achieve higher and better classification.
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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 | 0.000 | 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 | 0.001 |
| 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