Purpose
The purpose of this study is to examine the influence of firm characteristics such as profitability, growth opportunities, size, leverage and maturity on dividend policy of Indian firms.
Design/methodology/approach
The study analyzes the determinants of dividend policy of manufacturing firms in India using panel data. Because of the non-linearity behaviour of dividend pay-out by firms, the study uses quantile regression method to examine whether the determinants of dividends vary depending on the company’s level of dividends.
Findings
Overall, the results show important difference between ordinary least square and quantile regression estimates and depict differential effect on dividend at different levels. The notable difference occurs because either the significance changes (e.g. for profitability and growth opportunities) or because the magnitude of coefficients changes (e.g. for size, profitability and growth opportunities).
Originality/value
This finding is useful in identifying the dividend issuing companies. Further, results of this study would be helpful to the mangers to manage their financial positions that subsequently help in retaining and attracting the probable investors.
The objective of this study is to empirically examine the capital structure theories that can explain the capital structure choice made by the firms that are operating in China, India, and South Africa. The study tests the capital structure theories as a stand-alone basis as well as an integrated framework of nested models using advanced dynamic panel data methods with a data-set of 1,183 firms with 12,187 firm-year observations spanning the period 1999-2016. Findings suggest that the firms adjust toward target leverage very quickly and trade-off theory explains the firms' capital structure choice better than pecking order theory in the stand-alone model as well as the model nesting these two theories. This study contributes to the empirical literature of capital structure in the following way. First, this study uses error correction framework as a general specification of the widely used partial adjustment model. Second, the study uses advanced panel data estimators to estimate partial adjustment model and error correction model. Finally, the different specifications are tested using a large data-set of firms in China, India, and South Africa that has not been done so far.
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