Bibliographic record
Abstract
The relationship between liquidity and bank performance in finance literature remains an unresolved empirical issue. The main objective of this article was to investigate the relationship between liquidity mismatch index (LMI) initially developed by Brunnermeier, Gorton and Krishnamurthy (2012) and further developed by Bai, Krishnamurthy, and Weymuller (2018) and South African bank performance empirically. Different from other prior studies, the study undertook to determine the relationship employing the liquidity measure that integrates both market liquidity and funding liquidity within a context of asset liability mismatches. The unit of analysis was a panel of 12 South African banks over the period 2008–2018. Specifically, two liquidity measures – the bank liquidity mismatch index (BLMI) and the aggregate liquidity mismatch index (ALMI) were regressed against bank performance matrices. The newly developed liquidity measures are based on portfolio management theory and they account for the significance of liquidity spirals. Results revealed that, bank performance is negatively and significantly related with BLMI. While the bank performance is positively related to ALMI, the relationship is not significant. Also, the nature of relationship is dependent on the measure of profitability employed.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".