The Relationship between Promoters’ Holdings, Institutional Holdings, Dividend Payout Ratio and Firm Value: The Firm Age and Size as Moderators
Bibliographic record
Abstract
The present paper aims to empirically examine the effect of promoters’ holdings and institutional holdings on dividend payout ratio and the firm value. Most importantly, this paper explores the age and size of the firm as the moderators in the relationships. Data collected from 23 companies from India and 253 data points were analyzed to test the hypothesized relationships. The results indicate that promoters’ holdings and institutional holdings are positively associated with dividend payout ratio and firm value. Further, moderator hypotheses suggest that (i) firm age moderates the relationship between promoters’ holdings and dividend payout ratio, (ii) firm size moderates the relationship between institutional holdings and dividend payout ratio, (iii) firm age moderates the relationship between promoters’ holdings and firm value, and (iv) firm size moderates the relationship between institutional holdings and firm value. The implications for theory and practice are discussed. The conceptual model developed and tested in this research contributes to both the literature on dividend payout ratio and firm value and to the needs of institutional investors interested in increasing the firm value.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| 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) | 0.005 | 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".