Business Distress Prediction in Albania: An Analysis of Classification Methods
Bibliographic record
Abstract
This article investigates the effectiveness of various classification techniques in predicting financial distress for Albanian firms. The dataset includes 16 financial ratios from the financial statements of 187 of the largest non-financial businesses operating in Albania, covering the period from 2011 up to 2014, and ranked by 2014 revenues. The methods used in predicting financial distress are logistic regression, Ada Boost, Naïve Bayes, decision trees, support vector machine (SVM), neural network, and random forest. To compare the effectiveness of the models applied we used Classification Accuracy (CA), confusion matrix, and area under the curve (AUC) as evaluation criteria. The results demonstrate the superior predictive ability of ensemble methods, with random forest achieving more accurate forecasts than other methods, followed by Ada Boost. The research contributes to the literature by showing the added value of machine learning models in emerging markets with unique practice and economic conditions and proposing an alternative classification approach for the classification of financial distress when lacking bankruptcy data. Finally, the empirical findings evidence that the strengths of ensemble learning methods are reinforced in unbalanced not-big datasets of a unique emerging economy. These insights are relevant for lending institutions and researchers aiming to refine credit risk models in unique markets where access to relevant data is a challenge.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".