Regime-switching model estimates the impact of bank liquidity on bank performance across G20 countries: a moderate role for solvency, total loans, and total debt
Bibliographic record
Abstract
This study employs a Panel Smooth Transition Autoregressive (PSTAR) model to investigate the impact of bank liquidity on bank performance, using a sample of 113 banks across 14 G20 countries from 2000 to 2022. The empirical findings reveal a nonlinear relationship characterized by two LDR thresholds at 51.558 and 54.022. In the first regime, bank liquidity exerts an adverse effect on performance, reflecting the costs of excessive idle reserves. In the second regime, the impact of liquidity turns positive, albeit moderate, indicating that banks begin to deploy their liquid resources more efficiently. In the third regime, the positive effect intensifies, with a stronger coefficient, demonstrating that optimal liquidity levels can significantly enhance profitability. Robustness checks using the system GMM approach confirm this nonlinear, inverted-U relationship, with a positive effect of 0.067 and a negative squared term of 0.64e −3 , highlighting diminishing marginal returns to liquidity at higher levels. Furthermore, the analysis uncovers significant, positive interaction effects: liquidity combined with solvency strengthens bank performance; liquidity deployed through loans amplifies profitability; and the interaction between liquidity and debt ratios also positively affects performance. These findings indicate that regulators and central banks should adopt flexible liquidity policies that encourage banks to deploy excess funds productively while maintaining adequate buffers, with substantial capital and prudent leverage frameworks enhancing financial stability and sustainable profitability across G20 banking systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".