Growth Opportunities, Capital Structure and Dividend Policy in Emerging Market: Indonesia Case Study

The study attempts to analyze the impact of firm's risk on capital structure in the context of seasonal and non-seasonal businesses. We use two independent variables namely credit risk and systematic risk and one dependent variable to explore this connection. Sugar sector is taken as seasonal while the textile sector as non-seasonal businesses. The panel data of twenty-five firms from each sector are taken ranging for the period of 2012 to 2019 which has been retrieved from their annual reports for empirical analysis of the study. The results reveal the negative impact of credit risk on capital structure in both types of businesses. Increasing (decreasing) one point of credit risk causes a decrease (increase) leverage ratio by 0.27 points for seasonal while increasing (decreasing) one point of credit risk causes to decrease (increase) leverage by 0.15 points for non-seasonal businesses. Furthermore, the study shows positive impact of systematic risk on leverage ratio in non-seasonal business and no impact in seasonal business. Any increase (decrease) in the systematic risk causes an incline (decline) leverage ratio by 2.68 units for non-seasonal businesses. The study provides a guideline to managers for risk management in businesses. The research focusses on theoretical as well as managerial and policy implications on risk management in businesses.

Section: Discussionsupporting

confidence: 93%

“…They indicated that there was a negative impact of these variables on the leverage. Danila et al (2020) also found a significant effect of credit risk on leverage. Gilchrist and Mojon (2018) revealed that the credit risk increased from the nature of agreements.…”

Section: Literature Reviewmentioning

confidence: 84%

Firm’s Risk and Capital Structure: An Empirical Analysis of Seasonal and Non-Seasonal Businesses

Tahir¹,

Moazzam²,

Sultana³

et al. 2020

“…Hence, it explains the negative and significant relationship between FCF and DPE as found in this research. This finding is consistent with that reported by Danila et al (2020). This research hypothesizes a positive association between LDIVD and dividend payout.…”

Section: Descriptive Statistics Resultssupporting

confidence: 93%

Explaining Dividend Payout: Evidence from Malaysia’s Blue-Chip Companies

Che-Yahya¹,

Alyasa-Gan²

2020

This research investigates the explanatory factors governing the dividend payout to shareholders of blue-chip companies listed on Bursa Malaysia. In spite of continuous attention offered by empirical research on dividend payout of publicly-listed companies, paradoxically only few studies exclusively examined the explanatory factors from the perspective of blue-chip companies. Recognizing the capability of bluechip companies to serve as a stalwart indicator of stock market condition as well as a consistent income source to shareholders, more research should be carried out for better inference on the companies' dividend payout decision. This research is using 522 observations from a sample of 18 Malaysian blue-chip companies over a 29-year period (1990 to 2019) and utilizes a panel data regression analysis for the estimation of the impact of eight factors, namely, systematic risk, leverage, free cash flow, lagged dividends, market-to-book value, profit growth, total asset turnover, and company size. Measuring dividend payout using two specifications (dividend/earnings and dividend/total assets), this research reveals that systematic risk and free cash flow have a significant and negative impact on dividend payout. Meanwhile, past year dividends, market-to-book value, profit growth, total asset turnover and company size have a significant and positive impact on dividend payout.

“…The Hausman test is used to decide whether the (FEM) is appropriate or the (REM). If the result of Hausman's test is statistically significant, then this means that there is a serial correlation between the independent variables and the error term, and the (FEM) is the appropriate one, otherwise (REM) is appropriate (Gujarati & Porter, 2009;Danila et al, 2020). In this study, the results of Lagrange multiplier are significant as listed in Table (5) and the Haussmann's test indicates that the (REM) is the appropriate model for estimation.…”

Section: Regression Results and Discussionmentioning

confidence: 99%

Macroeconomic and Bank-Specific Variables and the Liquidity of Jordanian Commercial Banks

Al-Qudah¹

2020

This study aimed to explore the impact of macroeconomic (Real GDP growth (GDPG), Inflation rate (INF)) and bank-specific variables (profitability (ROA), capital adequacy (CADEQ), non-performing loans (NPL), deposit growth (DEPG)) on the liquidity (lIQ) of 13 listed Jordanian commercial banks for the period 2011-2018. Panel data analysis, Pooled least square, fixed effects model and random effects model, Lagrange multiplier test, and Hausman test were used. The random effects model output shows that, macroeconomic variables have a significant impact on Jordanian commercial banks liquidity since inflation has a positive impact while GDPG has a negative impact on banks (LIQ). On the other hand among the bank-specific variables capital adequacy and deposit growth have a positive significant impact on banks (LIQ), while (NPL) and (SIZE) have a negative significant impact on Jordanian commercial banks liquidity. But ROA has a negative insignificant impact on (LIQ). The findings of the study suggest that commercial banks departments need to pay attention to the economic and internal variables of banks in order to maintain acceptable levels of liquidity.