Financial distress predictions with Altman, Springate, Zmijewski, Taffler and Grover models
Bibliographic record
Abstract
Several models have been developed to predict financial difficulties and corporate bankruptcy. In this research various models were employed, including the Altman model (referred to as the Z-Score), the Springate model (known as the S-Score), the Zmijewski model (designated as the X-Score), and the Grover model (referred to as the G-Score). These techniques serve the purpose of evaluating the likelihood of encountering financial difficulties, which in turn determines the probability of PT Garuda Indonesia (Persero) Tbk going bankrupt. The study utilized secondary data sourced from financial statements spanning the years from 2020 to 2022. The application of the Altman model for bankruptcy prediction revealed that PT Garuda Indonesia (Persero), Tbk experienced financial distress throughout the period from 2020 to 2022. According to the Springate model, the company was in a state of distress and declared bankruptcy in 2020 and 2022, while 2021 fell into a grey area. The Zmijewski model indicated that the company was on the brink of bankruptcy, with financial difficulties and a potential risk of bankruptcy within the next three years. Grover's model predicted bankruptcy for the company in 2020 and 2022, but indicated safety in 2021. Notably, the Taffler model emerged as the most accurate in forecasting bankruptcy, boasting a 100% accuracy rate with no errors. Meanwhile, the Zmijewski model achieved an 81.25% accuracy rate with an error rate of 18.75%, and the Springate model exhibited the lowest accuracy in bankruptcy prediction, scoring only 12.50% accuracy with an error rate of 87.50%.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".