Examining Validity of Known Dividend Models in Indian Companies
Bibliographic record
Abstract
Abstract- Dividend declaration is considered as one of the key focus areas of the firm’s financial policy. The core of dividend policy includes the decision like whether to distribute profits to the shareholders in the form of dividend or to retain. The dividend decision, one of the widely researched topics, yet named as dividend puzzle, has been a center of attraction for the past number of decades. The outcome of the past researches has resulted in development of number of models trying to explain the dividend behavior of the companies. Some of the well-known dividend models are: Lintner’s model, Brittain’s model, Watt’s model and Aharony’s and Swary’s model. Considering the importance of the models, an attempt has been made to study their applicability in Indian conditions. This study investigates whether these models can be used to explain Indian companies ’ dividend payments or not. 172 companies listed with BSE with continuous dividend payments from 2004-08 have been selected in four industrial sectors: Engineering, FMCG, IT and Textiles. The study bring forth that out of all the models, Lintner’s model does have a good fit in the selected Indian companies. Keywords- Dividend; Lintner’s model; Brittain,s model; Watts model; Aharony and Swary’s model 1.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".