• Journal of Navigation and Port Research
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• v.39 no.1
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• pp.69-75
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• 2015

The concrete purpose of this study is to suggest actually a debt ratio to optimize the capital structure providing a kind of approach to estimate the proper debt ratio with an analytical model and empirical data in Korean shipping industry. The mathematical and analytical model is started from the first equation about ROE, return of net operating income on equity, with an independent variable, debt ratio. It is constructed with several parameters, ROS(return of operating income on sales), TAT(total assets turnover), and NFCL(net finance cost to liabilities). There could not be a certain relationship between debt ratio and ROS or TAT, while some correlation or causality between debt ratio and NFCL. In other words, most of firms with high debt ratio is likely to burden higher finance cost than others with low one. In this case, there is a linearity relationship between debt ratio and NFCL, so then the second equation considering this relation could be included within the analytical approach of this paper. To be short, if the criteria of adequate debt ratio has to be defined as some level of debt ratio to optimize ROE, the ROE could be illustrated as a quadratic equation to debt ratio from two equations. Next, this research estimated those parameters' numbers through the single regression method with data over 12 years of Korean shipping industry, and identified empirically the fact that optimal debt ratio would be approximately 400%. To conclude, if that industry's sales and operating incomes are stable, the debt ratio could be accepted until twice of 200% had forced in order to guarantee its financial safety in past time.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.301-308
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• 2018

This study explores an analytical mathematical model designed to estimate the optimal debt ratio of the Korean automobile industry, which has a more significant effect on the national economy than that of other industries, and attempts to estimate the optimal debt ratio based on objective data. The analytical model is based on ROA and ROE which uses the debt ratio as an independent variable and employs ROS, TAT, and NFCL as the related parameters. Regarding the NFCL, the optimal debt ratio is usually defined as the debt ratio that maximizes the ROA and ROE and is calculated using analytical procedures, such as by adding an equation that considers the debt ratio and the linearity relationship to the analytical model. This is because the optimal debt ratio can be calculated reliably by making use of an estimated value within a certain range, which is derived from more than two calculations rather than a single estimation starting from one calculation formula. In this study, for the estimation of the optimal debt ratio, the ROA and ROE are expressed as a quadratic equation with the debt ratio as the independent variable. Using this analysis procedure, the optimal debt ratio obtained using the data from the Korean automobile industry over a sixteen year period, which would optimize the profitability of the Korean automobile industry, was found to be 188% of the debt ratio in the ROA and 213% of the debt ratio in the ROE. This result was obtained by overcoming the problem of the reliability of the estimation value in spite of the limitations of the logical theory of this study, and can be interpreted as meaning that maintaining a debt ratio of 188% to 213% can enhance the profitability and reduce the risks in the Korean automobile industry. Furthermore, this indicates that the existing debt ratio of the Korean automobile industry is lower than the optimal value within the estimated range. Consequently, it is necessary for corporations to change their future debt ratio policies, given that the purpose of debt ratio management is to maintain safety and increase profitability, and to take into account the characteristics of the specific industry.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.4
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• pp.135-142
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• 2017

This study employs an analytical mathematical model to estimate the optimal debt ratio of Korean agricultural corporations, more sensitive to the government debt ratio policy compared to other industries, and the estimation of the optimal debt ratio based on objective data. The analytical model utilizes the equation for ROE, with the debt ratio as an independent variable, and related parameters include ROS, TAT, and NFCL. Regarding the NFCL, the optimal debt ratio standard is defined as the debt ratio that maximizes the ROE by analytical procedures such as adding an equation concerning the debt ratio and a linearity relationship to the analytical model, and from these equations, a quadratic equation with the debt ratio as an independent variable describes the ROE. This methodemploys fourteen years of corporate data. Results show that 138% of debt ratio is the optimal debt ratio to increase the ROE of the corporations, which implies that the existing debt ratio of Korean agricultural corporations is higher than optimal. Consequently, it is required for authorities to change future debt ratio policies in view that the purpose of debt ratio management is to maintain safety and increase profitability.Management should emphasize characteristics of the specific industry rather than standardized judgements based on numerical indexes.

• The Journal of the Korea Contents Association
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• v.15 no.5
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• pp.475-487
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• 2015

This study measures the managerial efficiency of Korea's 14 public enterprises using bootstrap DEA in 2013. In addition, it examines the factors that affect on the bootstrap bias-corrected efficiency using truncated regression analysis. The results and implications of this study are as follows. First, using bootstrap DEA model analysis, the results showed that the mean technical efficiency was 0.3182, the mean pure technical efficiency was 0.4994 and the mean scale efficiency was 0.6585. The main cause of technical inefficiency was due to pure technical inefficiency. Second, rank test between technical efficiency of general DEA model and bootstrap DEA model was no significant difference under CRS and VRS assumption. Third, the main cause of the inefficiency in 11 DMUs among 14 DMUs were mainly due to the pure technology and three DMUs were because of the scale efficiency. Finally, in the truncated regression analysis, cost of labor, profit, sales, return of equity, and the number of employees appeared as factors affecting the scale efficiency at the 10% significance level.