Impact of Financial Distress on the Dividend Policy of Banks in India
Bibliographic record
Abstract
The present study primarily examines the impact of financial distress (FD) on the dividend policy of 33 banks working in the Indian economy from 2010 to 2019. In addition, we further explore the association between financial distress and dividend policy under the influence of shareholder activism (SHA). Using the static panel data regression technique, it is revealed that financial distress is non-linearly associated with the dividend policy of banks in an inverted U-shape. In the initial phase of a distressing situation, banks tend to have a liberal dividend policy. However, after reaching the pressure point, the banks start to squeeze dividend distribution to the stakeholders. Furthermore, the significant impact of shareholder activism has been found in the association between financial distress and the dividend payout policy of banks. From the policy perspective, the study will provide the policymakers with a clear all-round perspective of distressing situations, as the current research involves exploring the impact of distress on the dividend policy that will help the experts in basically understanding the adverse effect of financial distress and the repercussions, respectively, on the earning of the shareholders.
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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 | 0.000 | 0.003 |
| 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 | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".