Exploring the internal factors influencing financial distress
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it