Firm Size Does Matter: New Evidence on the Determinants of Cash Holdings
Bibliographic record
Abstract
We study the financial determinants of cash holdings and discuss the importance of firm size in the post-crisis period. We employ panel data regression analysis on a sample of 6629 non-financial and non-utility listed companies in the United Kingdom from 2010 to 2018. We focus on the comparative analysis of large, medium, and small size firms in terms of cash holdings. Our findings indicate that cash levels are higher for firms with riskier cash flows, more growth opportunities, and higher R&D expenditures. In contrast, the firms’ cash holdings decrease when the substitutes of cash, cash flows, and capital expenditures increase. We show that small-sized firms tend to hold more cash than their larger counterparts due to precautionary motives. Further, we confirm a significant and varying association between managerial ownership and cash holdings. The study is robust to different regression specifications, additional analyses, and endogeneity tests. Overall, we add to the prior literature by identifying the effect of firm-level attributes and governance characteristics on cash policy during the post-crisis period. To the best of the authors’ knowledge, this is the first work that provides insights on the way that firm characteristics impact cash holdings, considering the differences among firm size groupings.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".