Free Cash Flow, Growth Opportunities, And Dividends: Does Cross-Listing Of Shares Matter?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>Corporate dividend policy should strike a balance between paying cash to shareholders when there are excess resources and retaining sufficient resources in the company to fund worthwhile projects. Using excess resources to pay dividends can help to avoid overinvestment by the company in inappropriate projects and/or other potential misuse of funds by managers for their own benefit. However, companies also need to avoid paying too much in dividends to ensure that adequate resources are available within the company to fund projects that could increase shareholder wealth (i.e., to avoid underinvestment). Cross-listing of company shares can improve governance and oversight, which may make the dividend policies of cross-listed companies more likely to avoid both over and underinvestment.</p> <p>Using a sample of Chinese listed companies from 2003 to 2011, we find that cross-listed companies pay higher dividends than non-cross-listed companies when there are excess resources (measured by free cash flow), thereby reducing the potential for overinvestment/misuse of the resources by cross-listed companies. We also find that the dividends of cross-listed companies are lower than those of non-cross-listed companies when there are greater growth opportunities (measure by the market-to-book ratio), reflecting the reduced potential for underinvestment by cross-listed companies. We find more limited evidence that cross-listings may influence the relationship between dividend volatility and free cash flow and growth opportunities. Overall, our results suggest that companies cross-listing their shares have dividend policies that are more responsive than those of non-cross-listed companies to potential shareholder concerns about over and underinvestment.</p>
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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