What Determines Cash Holding of Listed Deposit Money Banks? Evidence from Nigeria
Bibliographic record
Abstract
The study examined the determinants of cash holdings by 12 deposit money banks in Nigeria using data that covered the 2008-2020 sample period. Panel data was sourced from the Nigerian Stock Exchange fact book, Annual financial statements, and cash flow reports of the selected banks. Econometric tools were employed to analyze such variables of interest as return on assets, asset tangibility, leverage, bank size and volume of deposits to assets. The empirical findings revealed that asset tangibility is a negative and an 'important factor in the determination of cash holding behaviour of deposit money banks in Nigeria. Return on assets does not have any significant relationship with cash holding; leverage has an insignificant positive impact on cash holdings; bank size has an insignificant negative relationship with cash holding; and volume of deposits to assets has a weak negative impact on deposit money banks’ cash holding behaviour. The empirical outcome calls for stringent cash holding policies which ensures that as a bank increases its cash holding, it will in turn enhance the overall performance of the bank. Also, since asset tangibility is found to be major determinant of banks cash holding behavior, it follows that banks should hold more cash in order to increase tangible assets. Thus, there is need for a policy framework that will ensure that as bank increases its cash holding, its corresponding tangible assets would also be enhanced.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".