Informational Value of Dividend Initiations: Impact of Cash Dividends on Share Prices of Manufacturing Companies in Sri Lanka
Bibliographic record
Abstract
The shareholder wealth consists of dividends and capital gains. The former is considered to be risk averse, whereas the latter is perceived to be risky. The risk return trade-off in these two returns drives the investor preference. The objective of a for-profit organization is to maximize shareholders’ wealth, however, disbursing dividends may not always be in the best interest of shareholders. Theoretically, retained earnings increase share prices as firms have more funds to be invested. The objective of the study is to measure the stimulus of cash dividends on share prices. We conduct empirical analysis based on data relating to companies listed on the Colombo Stock Exchange (CSE) under the manufacturing sector. As we show in our literature review, in order to reduce the risk of obtaining spurious results, this analysis requires the use of advanced modelling techniques allowing to model non-stationarity of time series, as well the presence of control variables and lagged variables. The novelty of our study is in the use of advanced modelling and data visualisation techniques (including the ‘xdPlot’ dataviz framework recently proposed by the authors), especially in application to CSE data. We conduct a thorough exploratory data analysis (EDA) aiming to spot data anomalies and initiate appropriate data transformations. Given the results of EDA and the nature of the data available, we select the Arellano-Bond estimator as the most adequate method for regression analysis. Market Price per share (MPS) termed as the dependent variable, whereas Dividend per Share (DPS) is viewed as the independent variable. The results validated theoretical literature such as signaling effect and bird in hand theory, but questioned some previous empirical studies. The study validated cash dividends as stimulus to investors given the positive relationship between DPS and MPS.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".