Liquidity Risk Mediation in the Dynamics of Capital Structure and Financial Performance: Evidence from Jordanian Banks
Bibliographic record
Abstract
Maximising financial performance while maintaining adequate liquidity is a crucial and ongoing challenge for bank management, particularly in emerging markets. This study focuses on the relationship between capital structure and financial performance in Jordanian banks, with the mediating role of liquidity risk. Using panel data from 13 central Jordanian banks over the 2015–2022 period, we employ structural equation modelling (SEM) to analyse how capital structure ratios (equity-to-asset, debt-to-loan, and deposit-to-asset) influence financial performance metrics (return on assets and net income-to-expenditure ratio). Our findings reveal a significant positive association between capital structure and financial performance. However, liquidity risk fully mediates this effect. Capital structure primarily impacts performance by influencing a bank’s liquidity risk profile. Furthermore, the strength of this mediating effect is noteworthy—capital structure exhibits a statistically more robust association with liquidity risk than its direct impact on performance. This highlights the crucial role of managing liquidity risk within the complex dynamics of bank operations. This research makes a significant contribution to the existing literature by demonstrating the positive impact of capital structure on performance using the underlying mechanism through which this effect occurs. The insights of this research provide several implications for practice in the context of banking industries.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".