• Health Policy and Management
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• v.20 no.3
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• pp.123-137
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• 2010

The main purpose of this article is for deriving functions related to the prediction of the closure of the hospitals, and finding out how the discriminant functions affect the closure of the hospitals. Empirical data were collected from 3 years financial statements of 41 private hospitals closed down from 2000 till 2006 and 62 private hospitals in business till now. As a result, the functions related to the prediction of the closure of the private hospital are 4 indices: Return on Assets, Operating Margin, Normal Profit Total Assets, Interest expenses to Total borrowings and bonds payable. From these discriminant functions predicting the closure, I found that the profitability indices - Return on Assets, Operating Margin, Normal Profit Total Assets - are the significant affecting factors. The discriminant functions predicting the closure of the group of the hospitals, 3 years before the closure were Normal Profit to Gross Revenues, Total borrowings and bonds payable to total assets, Total Assets Turnover, Total borrowings and bonds payable to Revenues, Interest expenses to Total borrowings and bonds payable and among them Normal Profit to Gross Revenues, Total borrowings and bonds payable to total assets, Total Assets Turnover, Total borrowings and bonds payable to Revenues are the significant affecting factors. However 2 years before the closure, the discriminant functions predicting the closure of the hospital were Interest expenses to Total borrowings and bonds payable and it was the significant affecting factor. And, one year before the closure, the discriminant functions predicting the closure were Total Assets Turnover, Fixed Assets Turnover, Growth Rate of Total Assets, Growth Rate of Revenues, Interest expenses to Revenues, Interest expenses to Total borrowings and bonds payable. Among them, Total Assets Turnover, Growth Rate of Revenues, Interest expenses to Revenues were the significant affecting factors.

• Korea Journal of Hospital Management
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• v.7 no.3
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• pp.25-43
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• 2002

This study focuses on the factors that make the financing decision of private hospitals in Korea. Data used in this study were collected from 98 hospitals with complete general data of current status as well as financial statements. They were chosen from the 138 hospitals that passed the accreditation process by the Korean Hospital Association from 1996 to 2000 for the purpose of accrediting training hospitals. The dependent variables in this study consist of total liabilities to total assets, borrowings to total assets. The independent variables are ownership, hospital type, teaching status, location, bed size, period of establishment, asset structure, profitability, growth, tax shields, volatility of profit, competition(market concentration), and other factors. The major findings of this study are as follows. The factors found to have significant effect on liabilities to total assets are teaching status(-), asset structure(-), profitability(-), tax shields(+), and business risk(-). University hospitals have less liabilities than the non-university hospitals. It was also confirmed that high profitability, high fixed asset, high volatility of profit and low tax shields results in decrease in liabilities. The factors that significantly affect on borrowings to total assets are teaching status(-), period of establishment(-), volatility of profit(-) and competition(+).

• Health Policy and Management
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• v.12 no.2
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• pp.43-75
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• 2002

This study focuses on the factors that make the financial structure of hospitals in Korea different, and on recommended courses of action that could be very helpful to hospitals in maintaining a sound financial structure. Data used in this study were collected from 132 hospitals with complete general data of present conditions as well as financial statements. They were chosen from the 174 hospitals that passed the standardization audit undertaken by the Korean Hospital Association from 1996 to 2000 for the purpose of accrediting training hospitals. The dependent variable in this study is financial structure. It consists of liabilities as against total assets (total liabilities to total assets, short-term liabilities to total assets, long-term liabilities to total assets, short-term borrowings to total assets, long-term borrowings to total assets). The independent variables are ownership type, hospital type, location, whether or not a representative is a director of the hospital, the possibility of changing a hospital director, bed size, period of establishment, asset structure, profitability, growth, tax shields, business risk, competition. The factors that appear to have the strongest impact on the liabilities to total assets of all the hospitals sampled are ownership type, hospital type, profitability, tax shields, and business risk. It was found that not-for-profit private hospitals and for-profit private hospitals have more liabilities than public hospitals, and tertiary medical institutions have less liabilities than the secondary general hospitals. Moreover, hospitals earning more at the expense of high business risk have a distinct tendency to lower liabilities. Concerning the current ratio, it was found that factors such as ownership type, hospital type, period of establishment, asset structure, and business risk are the more significant variables. The current ratio of public hospitals is higher than that of both not-for-profit private hospitals and for-profit private hospitals, and the current ratio of tertiary medical institutions is higher than that of general hospitals. As business risk is higher in hospitals compared to other businesses, the current ratio becomes higher; this is because it is assumed that for fear of bankruptcy, hospitals lessen liabilities to total assets. On the other hand, as hospitals become older, the fixed assets to total assets become lower. It is remarkable that in hospitals, the factors affecting liabilities to total assets have an opposite regression coefficient sign against factors affecting current ratio. It brings out the same results borne out by the old financial theories and researches, in which a lot of the liabilities of hospitals are considered as the cause of worsening liquidity. Therefore, it is very important for hospitals to maintain a sound financial structure in order to survive using the rational acquisition and maintenance of capital.

• The Korean Journal of Health Service Management
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• v.7 no.3
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• pp.161-176
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• 2013

This research analyzed Raise Capital type and Investment Efficiency for non-profit hospitals in Korea. 152 cases of financial information from 2007 to 2010 were utilized for analysis. As a result of analysis, Raise Capital for Borrowings to total assets was primarily used, taking around 40% on average, and the method of Raise Capital with significant difference among Medical Institutions was Liabilities in Excluded Borrowings to Total Assets and Capital Stock & Capital Reserves to Total Assets. Besides, the relation between Invested capital and Investment efficiency was opposite each other in the non-profit hospitals, and Region was an important element influencing over Productivity per Value Added. In addition, in the investment activity of non-profit hospitals in the light of Investment Efficiency, only hospitals among Medical Institution types had a character of Capital Intensive, and General Hospital and Geriatric & Long-term Medical Care Hospital among Medical Institution types showed a character of Labor Intensive in the light of Performance.

• Health Policy and Management
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• v.12 no.4
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• pp.1-17
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• 2002

This study was attempted to identify the liquidity trends and determinants of private hospitals in Korea different. Data used in this study were collected from 98 hospitals with complete general data of present conditions as well as financial statements(balance sheets, income statements). They were chosen from hospitals that passed the standardization audit undertaken by the Korean Hospital Association from 1996 to 2000 for the purpose of accrediting training hospitals. The dependent variables in this study were used current ration and quick ratio as a proxy indicator for liquidity. The independent variables were ownership type, hospital type, location, bed size, period of establishment, short-term liabilities to total assets, long-term liabilities to total assets, borrowings to total assets, fixed asset ration, net profit to total assets, operating margin to gross revenue, growth rate of net worth to total assets, total asset turnover, and business risk(volatility of profit). The major findings of this study were as follows. Trends of liquidity(current ratio, quick ratio) had been continuously decreased. Especially, There were very distinct decreasing trends of personal hospitals and less than 300beds, which weakened liquidity. The factors had significant effect on current ratio were short-term debt to total assets(-), fixed asset ratio(-), business risk(+). High short-term debt to total assets, high fixed asset ratio and high business risk significantly decreased in liquidity. The factors that significantly affected on quick ratio were short-term debt to total assets(-), borrowings to total assets(+), fixed asset ratio(-), business risk(+).

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.7 no.2
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• pp.35-44
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• 2012

This paper provides information for decision making of the managers and the staffs of oriental medicine hospitals through the analyzing financial statement. The oriental medicine hospitals decreased total assets, total gross revenues, and increased debt. Comparison of years 2008 and 2010, oriental medicine hospital's total assets decreased, liabilities increased, total revenue decreased, and showed a continuing deficit. On the other hand, the rate of net worth of the oriental medicine hospitals were high and lower dependence on the borrowings. So the management performance of the oriental medicine hospitals as a whole were good. However some the oriental medicine hospitals were experiencing serious financial difficulties. In order to the hospitals overcome its financial difficulties, they had to rely on short-term borrowings. In consideration of the reserve fund for essential business, the transfers and the net profit ratio to total assets of the operating profit ratio to total assets were the level of commercial interest rates. But the operating profit ratio to the total assets were significantly different according to the hospitals. And 10 hospitals of the operating profit ratio to gross operating revenues were (-), they had problems with profitability. Meanwhile the total amount of capital and the equity capital of reduced hospitals increased, there were significant differences even between hospitals.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.10
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• pp.520-526
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• 2016

This study aims to develop a distress prediction model, in order to evaluate the distress prediction power for first-class hotels and to calculate the average financial ratio in the Seoul area by using the financial ratios of hotels in 2015. The sample data was collected from 19 first-class hotels in Seoul and the financial ratios extracted from 14 of these 19 hotels. The results show firstly that the seven financial ratios, viz. the current ratio, total borrowings and bonds payable to total assets, interest coverage ratio to operating income, operating income to sales, net income to stockholders' equity, ratio of cash flows from operating activities to sales and total assets turnover, enable the top-level corporations to be discriminated from the failed corporations and, secondly, by using these seven financial ratios, a discriminant function which classifies the corporations into top-level and failed ones is estimated by linear multiple discriminant analysis. The accuracy of prediction of this discriminant capability turned out to be 87.9%. The accuracy of the estimates obtained by discriminant analysis indicates that the distress prediction model's distress prediction power is 78.95%. According to the analysis results, hotel management groups which administrate low level corporations need to focus on the classification of these seven financial ratios. Furthermore, hotel corporations have very different financial structures and failure prediction indicators from other industries. In accordance with this finding, for the development of credit evaluation systems for such hotel corporations, there is a need for systems to be developed that reflect hotel corporations' financial features.

• The Journal of the Korea Contents Association
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• v.17 no.11
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• pp.515-524
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• 2017

This study is focused on finding out how Mers affects tourism industry. To achieve the study purpose we analyzed the differences of management performance of hotels, travel agencies and airlines before and after Mers occurrence. As the result of analysis, there were significant differences in the variables of profit margin ratio before income taxes and operating profit ratio as a profit index before and after Mers occurrence in the hotel firms. And in case of travel agencies, there were significant differences in the variables of debt to equity ratio and borrowings dependence ratio as a stability index before and after Mers occurrence. Lastly there were significant differences in the variables of operating profit ratio as a profit index and total assets turnover ratio as an activity index before and after Mers occurrence in the airlines. Therefore the managers should find an way to reduce the negative image of plague through the effective marketing strategy.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Journal of Venture Innovation
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• v.1 no.2
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• pp.35-46
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• 2018

The research on the efficiency analysis of domestic auto parts companies is mostly based on the calculation of the magnitude of the performance creation such as the sales amount compared to the input assets. However, the performance indicators such as sales, operating profit, and net profit are directly related to the cost structure and This cost structure is affected by changes in the firm's asset and capital structure. As a result, it is considered that efficient capital procurement needs to be done at the same time to create efficient management performance through proper investment. This study focuses on this point and attempts to measure the efficiency of procurement capital relative to the sales and other performance indicators generated by the first 33 suppliers who supply parts to Hyundai Kia Motors. Among the methods of evaluating efficiency, the DEA model based on the linear programming method is most widely used as a nonparametric method but The efficient frontier-based DEA model has the limitation that it can not use the variables that have a downward influence on the efficiency. This is inadequate to directly consider variables such as borrowings and total liabilities related to capital procurement. In this study, the efficiency of capital procurement was measured using Worst Practice DEA and the improvement direction of the capital procurement aspect of domestic auto parts companies was suggested