Apropos of Accounting Information Indicators as Determinants of Cash Dividend Policy Decision: A Comparative Study on Amman Stock Exchange (2001-2013)
Bibliographic record
Abstract
Investor’s psychological behavior normally seek to invest in companies that are characterized by stable and positive dividend stream. Dividend policy is related to the decision of whether to distribute or not to distribute cash to shareholder. This type of decision is not taken in isolation from other related financial factors, as such decision is considered an integrated part of the company’s overall financial decisions. This study aims at investigating the apropos of accounting information indicators (financial indicators) and their role in determining cash dividend policy adopted by companies listed within the major sectors of Amman Stock exchange. Extracting the accounting information indicators pertaining to the three main sectors (Banking, Industrial and Services) of ASE, and by applying the simple linear regression statistical approach, the results indicated that the dividend policy adopted by the three sectors were mainly determined by accounting information indicators and that the impact of these indicators on cash dividend policy vary due to dissimilarity of the sectors’ nature, whereas the results pointed out that different indicator affect different sector, which means that the impact of the accounting information is not identical on cash dividend policy decision.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".