Bankruptcy Risk and Financial Performance of Companies Listed on the Stock Exchange of Thailand
Bibliographic record
Abstract
Financial performance is an important issue for entrepreneurs and investors. So far the number of studies on the effect of bankruptcy risk on financial performance of firms is small. Hence, this research investigates the impact of bankruptcy risk on financial performance of companies listed on the Stock Exchange of Thailand and the relevant data cover the period between 2015 and 2019. Excluded are companies operating in the finance industry. The data are analyzed by multiple regression analysis. Altman’s Z-score (1968) is used as a proxy for bankruptcy risk while ROA, ROE, and Tobin’s Q serve as proxies for financial performance. The control variables in this study are liquidity, capital structure, firm size, and inflation rate. Results reveal that Altman’s Z-score and firm size statistically and positively affect financial performance proxied by both accounting-based-measures, i.e. ROA and ROE; and market-based measures, i.e. Tobin’s Q. These results confirm that companies listed on Thailand’s Stock Exchange have low bankruptcy risk and large size is financially performed well.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".