Narrow Results

The main purpose of this paper is to apply Johansen's vector error-correction model (VECM) to investigate the existence of the dividend signalling effect in the Singapore aggregate market through impulse response analysis, forecast error variance decomposition and Granger-causality test. Our findings show that a unit shock increase in dividend payout leads to a permanent increase in future earnings over time. These results imply that there exists informational/signalling content in dividend payout in the Singapore market over the long run. We further find that at least half of the forecast error variance in earnings can be accounted for by innovations in the dividend payout. In addition, the payout ratio is also shown to Granger-cause earnings in the Singapore market.

This study explores the application of machine learning models to predict dividend payouts for publicly listed companies in the Vietnamese stock market. The authors compared various machine learning algorithms to identify the most suitable one for this task. Based on our evaluation, the test shows that the Random Forest algorithm outperformed other methods in terms of accuracy, F1 score, and time efficiency. Furthermore, we analyzed the feature’s importance to understand the key factors driving dividend payouts. The results can help investors, managers, and government agencies make informed decisions and improve financial market transparency. The study also highlights the potential of using machine learning algorithms to automate and enhance the credit rating process, leading to more accurate and real-time assessments of business creditworthiness in Vietnam.

Despite empirical research, the relationship between dividend policy and stock price volatility is continuously debatable. This study investigated the relationship between dividend policy and stock price volatility of Indian listed companies. The study examined 260 listed companies based on the reliable dividend-paying manners of nonfinancial companies listed on the Bombay Stock Exchange (BSE) for the financial period from 2014–2015 to 2020–2015. To analyze the data, this study used the panel data models: fixed effects, random effects, and the Hausman test. Finally, this study applied the fixed effect model after careful examination of multicollinearity, endogeneity, and causality issues related to the dataset. The analyses revealed a significant negative relationship between dividend payout and stock price volatility meanwhile, dividend yield and stock price volatility have a positive association. The study outcomes provide information for investors and managers about dividend decision. This study provides an extensive understanding of the emerging stock market fluctuation on the relationship with the dividend policy.