The impact of liquidity risk, credit risk, and operational risk on financial stability in conventional banks in Jordan
Bibliographic record
Abstract
This study examines the impact of unsystematic financial risk, including liquidity risk, credit risk, and operational risk, on financial stability in conventional banks listed on the Amman Stock Exchange in Jordan. Understanding and managing these risks is crucial for protecting investors, maintaining financial stability, encouraging foreign investment, and strengthening the financial sector in Jordan. The study adopts a descriptive approach to collect and describe data and utilizes cross-sectional panel data over five years from 2016 to 2021 to establish cause-and-effect relationships between study variables, while controlling for other relevant factors that may influence the relationship. The findings suggest that while liquidity risk may not directly impact financial stability, it remains a critical risk factor that requires attention in risk management strategies. Credit risk has a negative impact on financial stability, highlighting the importance of effective credit risk management strategies to maintain a stable financial system. The study finds that operational risk has no direct impact on financial stability. Still, unsystematic operational risks can have significant implications for individual financial institutions and may indirectly affect overall stability. The study underscores the importance of comprehensive risk management strategies to mitigate the negative impact of unsystematic financial risk on financial stability. Future research may consider analyzing the impact of other types of risks on financial stability.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".