Indicators of financial distress condition in Indonesian banking industry
Bibliographic record
Abstract
This study conducts the theme of The Causes of Financial Distress conditions by samples from Indonesian banking sector registered in the Financial Services Authority of Indonesia within the period of 2015-2019. The title of this study: "Indicators of Financial Distress condition in Banking sector in Indonesia” during the period of 2015-2019" with a multiple correlation approach. The purpose of this study is to determine the effect of leverage of Credit Risk, CAR, ROA, and LDR to the Financial Distress conditions. The sample of population in this study are all conventional commercial banks in Indonesia registered in the Financial Services Authority of Indonesia. The number of samples in this study were included 37 commercial banks that their profitabilities were being declined, with a total number 146 observations. The method carried out in determining the sample is “Purposive” sampling. Based on the results of study and data analysis using the panel data method, it shows that capital, credit risk, profitability and liquidity have a positive effect on Financial Distress. The implication of the above conclusion is that it required further research to perform preventive actions to anticipate the measures of financial performance of the Bank, and it is expected to select a larger population of samples and variables that might have not been included in research on banking Financial Distress in Indonesia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".