This study involved companies engaging in real estate, property, and building construction companies listed in IDX for 2013 - 2019 period as the population. Unbalanced panel data regression was employed using the SPSS version 22 and E-Views version 10 to analyze the data and to test the hypotheses. The results showed that the previous year's dividend had a positive effect on dividend policy, while company size had a negative effect on dividend policy. This study proved that previous year's dividend and company size were key variables that determined companies’ dividend policy and they were major investment considerations for investors in order to obtain optimal returns.

This research examines the effect of the crisis due to the COVID-19 pandemic on dividend policy in Indonesia. The purposive sampling method was used to collect data from corporates listed on the IDX from 2014 to 2020 and analyzed using static and dynamic panel data approaches. The fixed-effect models (FEM) were selected for the static panel data regression. Meanwhile, the first difference-generalized method of moments (FD-GMM) and system-generalized method of moments (SYS-GMM) were used for determine the robustness of the estimated dynamic panel data. The results showed that the crisis due to the pandemic led to higher dividend distribution on SYS-GMM. Furthermore, companies maintained the dividend level as a positive signal for investors which lifted the sluggish trade condition in the capital market. Profitability and previous year dividends positively affect dividend policy robustly. Furthermore, the results showed that age affects dividend policy on FD-GMM. Financial leverage has a robust effect, and firm size has an effect on FD-GMM in different directions, while investment opportunity does not affect dividend policy. Statistically, the FEM selected that violates the best linear unbiased estimation was proven to form parameters that were not much different from the estimates produced by the dynamic model, both from the coefficient of influence direction and significance, and the omitted variable bias occurs as evidenced in the robust test with dynamic model was solved. This research is also used as a reference for considering investors’ investment decisions in the new normal condition. Therefore, dividend policy can be considered as a positive signal to investors with the ability to stock trading activities in the capital market.

<p>The inconsistent distribution of dividends is a unique phenomenon and it needs to be examined. Therefore, the purpose of this study is to examine ten predictors affecting dividend policy of the inconsistent distribution of dividends. This study used the purposive sampling method to analyze the data that were obtained from a total sample of 133 observation objects collected in the 19 real estates, property, and building construction companies listed on the IDX Between 2013 - 2019. Furthermore, the method used is hypotheses testing and statistical analysis tool used is the hierarchical multiple panel data regression with the Least Squares Dummy Variables. The results obtained from panel A are firm risk, financial leverage, and investment opportunity that affect the dividend policy. Meanwhile, the panel B results are company risk, financial leverage, investment opportunity, and previous dividend, although the previous dividend had no effect due to the different direction of influence. This study proves the determinants and relevance of the parametric statistical analysis in the inconsistent distribution of dividends. Moreover, it is useful for managerial practitioners to pay attention to predictors for increasing company performances and to ensure investors obtain optimal return of their dividend.</p>

This research investigates the impact of crisis due to the COVID-19 pandemic on the dividend policy of green index companies in Indonesia, namely the Sustainable and Responsible Investment (SRI) by Biodiversity (KEHATI) Foundation, or SRI-KEHATI indexed companies. The purposive sampling technique was used to collect data from companies listed from 2014 to 2020, using static and dynamic panel data models. From the several panel data models tested, the static panel data regression with random effects model (REM) and fixed effect model (FEM) uses the least square dummy variable-robust standard error (LSDV-RSE) technique are the best econometric models feasible. The system generalized method of moments (SYS-GMM) is used as a suitable econometric model with a robustness test used to determine static panel data regression. It is reported that SRI-KEHATI indexed companies tend to distribute dividends positively during this crisis, and is also statistically proven robust. This gives a positive signal to the capital market concerning the sluggish trading activity. The market reaction test, using two-approaches, showed that this business did not provide a positive reaction to the capital market, which turned out to be pessimistic. Furthermore, profitability and financial leverage have a robust effect, while dividends from the previous year affect dividend policy on the static panel data model, and firm size affect dividend policy on SYS-GMM. Predictors that proved influential with a direction not in line with the hypothesis were investment opportunities on REM and SYS-GMM, and firm age on SYS-GMM. The parameter estimation that passes the model specification test is feasible, whiles the biased and inconsistency of parameter estimation due to the alleged correlation between and failed to occur in static panel data regression. The endogeneity issue was resolved by dynamic panel data regression with the strongest corrective effect. This research can be used as a reference for investors to obtain optimal returns on green index companies in the country. An optimal dividend policy can increase the value of the SRI-KEHATI indexed companies; therefore, it can contribute optimally to sustainability and responsibility for social and environmental aspects.