Free Cash Flow, Growth Opportunities, And Dividends: Does Cross-Listing Of Shares Matter?
Bibliographic record
Abstract
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. 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.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".