Validity of Altman Z-Score Model to Predict Financial Failure: Evidence From Jordan
Bibliographic record
Abstract
This paper aims to investigate the Validity of Altman z-score model to predict financial failure in insurance companies listed on Amman Stock Exchange (ASE) over the period 2011-2016. To achieve the goal of the study, the study depended on the different statistics analytical method and Multiple Linear Regression through doing the statistical analysis of the independent variables on the dependent variable related to the subject of the study through the (E-views) program in order to cover the analytical part of the study, in addition to the descriptive method through relying on books, periodicals, previous studies and financial reports of the insurance companies of the study’ sample, whether the direct or the indirect ones, to cover the theoretical part. The result of the study finds a high predictive power for Z-score model. Moreover, the findings reveal that Z-Score model could be valuable instrumental indicators for many users of financial statement such as financial managers, auditors, lenders, investors, to make right decisions in the face of financial failure.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".