Business Failure Prediction: A Tri-dimensional Approach
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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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".