Managers Views on the Determinants of Cash Holdings: Evidence from Kenya
Bibliographic record
Abstract
Cash is essential for firms to support their day-to-day operations, take care of uncertainties in future and to take advantage of profitable opportunities that arise. However, setting the optimal cash holding by firms is not an easy task for managers and continues to be a hotly debated subject especially after the latest development in the global financial system. Empirical evidence in Kenya on the subject is scant, and the purpose of the paper is to establish the determinants of cash holding among firms listed in Kenya’s Nairobi Securities Exchange(NSE) from a manager’s perspectives. 168 questionnaires were administered to senior and finance executives in 44 non-financial firms listed on NSE. The respondents agreed with the statements on the expected relationship between Cash holding and Interest rates and industry sector and disagreed with the constructs on size,levarage,cashflow and Market- to- book value (MTB). The results may point to some agency problem in Kenyan listed nonfinancial firms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".