The Influence of Cash Ownership on Financial Performance: An Examination of Disruptors and Acquirers
Bibliographic record
Abstract
Cash ownership emits a powerful positive signal. We examine four sources of cash in firms, i.e., cash flows, cash holdings, cash proceeds from debt, and cash proceeds from equity. We examine the effects of cash ownership for firms growing by disruption, and firms growing by acquisition. Information signaling theory maintains that free cash flows may be used to increase shareholder wealth. Two-stage least squares regressions determined the impact of cash funding on disruptors and size of acquisition in the first stage, and cash-funded disruption or cash-funded acquisition in the second stage, for a US sample of 832 disruptor firms and 924 acquirers, from 2000–2020. Disruptions funded by cash holdings, cash flow, and cash proceeds from debt, significantly increased stock returns. A size effect was observed, with small disruptors showing significant effects. Acquisitions funded by cash holdings, cash flow, and cash proceeds from debt, significantly increased stock returns and return on assets. Agency costs significantly reduced returns and profits. Results for disruptions and acquisitions support signaling theory with free cash flows signaling higher share prices for both disruptors and acquirers, and higher profits for acquirers.
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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 | 0.002 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".