Bank Characteristics Effect on Capital Structure: Evidence from PMG and CS-ARDL
Bibliographic record
Abstract
The main aim of this paper was to investigate the impact of bank characteristics on capital structure empirically. The study employed a panel data analysis, Pooled Mean Group (PMG) and Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) estimators were utilized, for the period spans between the years 2008 and 2018. Both the borrowing (leverage) ratio and equity ratio used in the analysis cover short-term deposits and long-term deposits as a fundamental determinant variable on the capital structure. The main findings confirm that the deposit ratio has a positive relationship with the size of the bank. In other words, big banks use more foreign sources than small banks to use the tax shield advantage. At the same time, a percentage increase in bank size and liquidity ratio enhance the bank deposit rate by 0.0068% and 0.479%, respectively, in the long-run, while a percentage change in interest income coverage will reduce the bank deposit rate by 0.004% in the long-run. Meanwhile, the significant causal relationship of growth rate with the bank deposit rate could not be established. In addition, the short-run coefficients of the variables reveal that size, interest coverage, and liquidity have a positive and significant causal relationship with bank deposit rate in the short-run. The findings of the study are in line with the results of capital structure theories, especially the hierarchy theory and balancing theory.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".