Exploring the internal factors influencing financial distress
Bibliographic record
Abstract
This study aims to examine the effects of different factors influencing on financial distress. The population of this study includes industrial companies listed on the Indonesia Stock Exchange. Samples were processed by choosing 69 companies for three years of information which leaves us to have 150 observations. The sampling technique uses purposive random sampling and data is analyzed using PLS. The results show that firm size and liquidity negatively affect the financial distress while leverage positively affects the financial distress. In addition, institutional ownership moderates liquidity towards financial distress, firm size negatively affects liquidity, and liquidity does not mediate the effect of firm size on financial distress. The conclusion of this research is that management teams can avoid financial distress if they are able to manage liquidity ratios and leverage well, both ratios must be maintained so that they would not exceed firms’ financial abilities. Companies with big amount of total assets have an advantage in competition since it is not overshadowed by the condition of financial distress and they can easily gain stakeholders’ confidence. Institutional ownership in this study seems to encourage management to take risks related to company liquidity to generate profits by utilizing long-term debt in financing its operations.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".