Financial Distress and It’s Prediction: A Case Study of the Textile and Garment Industry
Bibliographic record
Abstract
Aims: The objective of this research is to glance at the projections of financial distress in the textile and garment sub-sectors listed on the IDX. Methodology: The case study method is used in this study to employ the descriptive quantitative method approach. While the IDX is the source of the case study data, the purposive sampling method was used on the financial statements of textile and garment sub-sector companies in 2019 and the first quarter of 2020. While the cross-sectional method is used for case study analysis, or by comparing the Z-score (multiple discriminant analysis) that has been performed between one company and the standard zone that has been carried out simultaneously. Results: This study discovered that the case study using multiple discriminant analysis models in the first quarter of 2020 shows a significant impact of Covid-19 on the financial condition of companies listed on the IDX in the textile and garment industry, with 88 percent of companies in a stress zone. This study also shows that both internal and external factors can lead to a company's demise. As a result, corporate financial management decision-making must consider the company's liquidity, debt proportion, and the efficient use of working capital. Implication/Applications: The findings of this study can be useful not only for researchers, but also for practitioners who are interested in financial distress cases. The Originality of the Study: One of the study's limitations is that the sample is still limited to the research scope, which only covers the two sectors. Furthermore, this study only employs a single model of financial distress. As a result, it is hoped that in the future, research will be conducted with various types of company sectors and using various financial distress models.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".