Bankruptcy risk assessment of Polish listed companies using Asian multiple discriminant analysis models
Bibliographic record
Abstract
Purpose: The main purpose of this article is to evaluate whether multiple discriminant analysis models developed in Malaysia to forecast the bankruptcy of Malaysian indus-trial companies and Singaporean companies can be useful in assessing the risk of bank-ruptcy of Polish listed companies. Methodology/approach: To test the efficacy of these discriminant models, the finan-cial statements of 25 bankrupt Polish companies and 25 viable companies were used. The accuracy of the classification of these enterprises into two groups (potential bank-ruptcy and companies able to survive on the market) was analyzed using the models developed to forecast the bankruptcy of Malaysian and Singaporean companies. Z-scores for each model were calculated for one year, two years, and three years prior to bankruptcy. Findings: The model developed to forecast the bankruptcy of Malaysian industrial companies correctly classified bankrupted Polish listed companies. However, it incor-rectly classified a large percentage of non-bankrupt companies. The Singaporean model accurately (it was 100% when data from one year prior to bankruptcy was used) assessed the risk of bankruptcy of failed companies. The percentage of correctly classified companies still in business was lower, but still acceptable. Research limitations/implications: It is recommended to test the classification abili-ties of the models using the financial data of Polish companies from various sectors of the economy (separately for selected industries), and in particular, utilizing financial reports of industrial companies. Originality/value: The Polish-language literature often describes bankruptcy forecast-ing methods developed in highly developed countries, such as the USA, Canada, Germany and Great Britain. Polish scientists have also developed many bankruptcy forecasting tools. By contrast, little attention has been paid to Asian bankruptcy forecasting methods. The study, the results of which are presented in this article, broadens the knowledge on this subject and makes it possible to test and evaluate the usefulness of such methods for Polish companies.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".