The Hierarchy Level of Strategy: A Bankruptcy Prediction of the Company using the Altman Z-Score Method in the Coronavirus Disease Period (An Empirical Study on Manufacturing Companies of Various Industry Sub-Sectors Listed in Indonesia Stock Exchange in
Bibliographic record
Abstract
Coronavirus disease (COVID-19) weakens many business sectors, including the corporate sector.This research uses quantitative method with explanation theory. Thus, researchers are interested in finding outwhether there are differences and influence of financial conditions in the 1st and 2nd Quarter of 2020 onmanufacturing companies of various industry sub-sectors listed in Indonesia Stock Exchange. This researchutilized the Altman Z-Score method. In addition, method of analysis used multicollinearity and binary logisticregression, which data sources were from financial reports in the 1st quarter and the 2nd quarter of 2020 with asample of 36 x 2 = 72 observations; utilizing purposive sampling technique. The results showed that there wasno difference in financial conditions in the 1st quarter and the 2nd quarter. However, there was a significanteffect on the variables studied. Therefore, the Altman Z-Scores method is proven to be able to predict financialbankruptcy of manufacturing companies of various industry sub-sectors. Additionally, this research provides acontribution for companies carrying out hierarchical strategies to gain a competitive advantage.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".