Timeliness of corporate annual financial reporting in Indonesian banking industry
Bibliographic record
Abstract
The financial performance of the banking sector globally can be seen on the capital markets of each country. One of the important sources of information in the investment business on the capital market is the financial reports that are provided by every company going public. The objectives of this study are (1) to determine the simultaneous and partial effect of liquidity factors, Debt Equity Ratio, company size on timeliness of financial reporting in the banking sector in Indonesia. (2) to determine what factors are dominant in the timeliness of financial reporting in the banking sector in Indonesia. This research uses secondary data with panel data analysis method. The results show the liquidity variable, Debt Equity Ratio and firm size positively influence on timeliness of financial reporting in the banking sector in Indonesia. Firm Size is the dominant factor that has a significant positive effect on the Timelines Financial Report of the banking sector in Indonesia. The findings of this research are that increasing liquidity, Debt Equity Ratio and Firm Size can increase the Timelines Financial Report of the banking sector in Indonesia. Firm Size as the dominant factor is the attraction and driving force for the Timelines Financial Report banking sector in Indonesia. The research can be used as a reference for future researchers on identifying efforts of the influence of Liquidity, Debt to Equity Ratio, Firm Size and Timelines Report.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".