Cash holdings and firm value: Evidence for increasing marginal returns
Why this work is in the frame
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Bibliographic record
Abstract
Cash is an important strategic asset for firms and scholars have a longstanding interest in the optimum level of a firm's cash holdings. In this study, we revisit the relationship between cash holdings and firm value by conducting a re-examination of Kim and Bettis , who hypothesized and found positive but decreasing marginal returns of cash. We argue and demonstrate that the regression model configuration of Kim and Bettis leads to distorted regression results. Once we adjust their regression model configuration, our results show that the benefits of cash do not diminish but instead increase with increasing cash holdings. In further analyses, we find indicative evidence that these results may be driven by firms with very high investment opportunities. We also employ a larger sample over a longer period of time to corroborate the time generalizability of our findings, we perform several checks to establish their robustness, and we discuss their theoretical implications.
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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 | 0.006 | 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 | 0.001 | 0.002 |
| 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 it