• Health Policy and Management
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• v.15 no.4
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• pp.81-109
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• 2005

According to many recent studies suggesting that cash flow analysis method tends to be more effective than traditional financial index analysis method to predict corporate bankruptcy, this study applies the cash flow analysis method to hospital business to identify the significant variables which can distinguish between superior hospitals and bankruptcy hospitals. The author analyzed recent 3 years, i.e. from the year of 2000 to the year of 2002, financial statements of 31 bankrupt hospitals In 2003, and the same number of superior hospitals through using Multiple Discriminant Analysis and Logit Analysis. The results are belows; First, the study releases that Logit Analysis is more likely to be effective than Multiple Discriminant Analysis. Second, this research also shows that traditional financial index analysis method is more superior compare to cash flow analysis method for hospital bankruptcy predict model. Finally, this study suggest that the significant variables, which can distinguish superior hospitals from bankrupt hospitals, are Operating/Current Liabilities$(Y_2)$, CFO/Equity$(Y_5)$ for cash flow analysis method and Net Worth to Total Assets Ratio$(X_1)$, Quick Ratio $(X_3)$, Return on Assets$(X_6)$, Growth Rate of Patient Revenues$(X_{16})$ for traditional financial index analysis method.

• Journal of Distribution Science
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• v.16 no.11
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• pp.21-27
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• 2018

Purpose - This study investigates the financial ratio of savings banks and the effect of the ratio having influence upon bankruptcy by quantitative empirical analysis of forecast model to give material of better management and objective evidence of management strategy and way of advancement and risk control. Research design, data, and methodology - The author added two growth indexes, three fluidity indexes, five profitability indexes, and four activity indexes CAMEL rating to not only the balance sheets but also the income statement of thirty savings banks that suspended business from 2011 to 2015 and collected fourteen financial ratio indexes. IBMSPSS VER. 21.0 was used. Results - Variables having influence upon bankruptcy forecast models included total asset increase ratio and operating income increase ratio of growth index and sales to account receivable ratio, and tangible equity ratio and liquidity ratio of liquidity ratio. The study selected total asset operating ratio, and earning and expenditure ratio from profitability index, and receivable turnover ratio of activity index. Conclusions - Financial supervising system should be improved and financial consumers should be protected to develop saving bank and to control risk, and information on financial companies should be strengthened.

• Human Ecology Research
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• v.59 no.1
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• pp.127-141
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• 2021

This study evaluates the financial status of Korean households using Household Financial Composite Index (HFCI) proposed in the preceding study. We analyzed 1,566 households with four persons aged 30-59 using raw data from the Korea Labor Panel Survey in 2016. The analysis results are as follows. First, HFCI was found to be 57.0 out of 100. Growth Index as one of three subindices was 11.1, which was significantly lower with 65.0 points for Status Index and 61.1 points for the Stability Index. Second, for households with male household owners, the overall financial score was 57.0, while that with female owners was almost similar with 57.2. HFCI was similar for the owner's age groups, but for Status Index, 58.5 for 30s, compared with 66.1 for 40s and 67.1 for 50s. The higher the education level of household owners, the better HFCI, with 53.2 high school graduates and 64.8 graduate graduates, showing a high gap of 11.6 points. HFCI for households living in owned housing was highest at 60.0, while that for rented housing was 40.7. Third, after controlling other effects, it was found that HFCI differed according to the level of education and ownership of housing living. Householder's gender was not found as a significant factor on HFCI. Status Index The 40s and 50s was higher than those in their 30s. Fourth, households were divided into three groups based on HFCI, named as risk, average and secure groups. HFCI for the risk group was 26.8, which was lower than 78.6 for the secure group, with a Status Index of 19.3. Households in their 50s and graduate school graduates were significantly included in the list of secure groups than others.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.12
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• pp.19-29
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• 2021

This study aims to measure financial inclusion and examine its impact on income inequality in a panel of 18 Asian countries over the period 1997-2017. Two alternative approaches for developing financial inclusion index are used: one approach following the methodology of Sarma (2008), while the other is the Dynamic Factor Model (DFM)-based index. The impact of individual indicators and index of financial inclusion on inequality in income is analyzed. The Generalized Method of Moment (GMM) approach is used for empirical analysis. The results indicate that micro-level financial inclusion has a weak negative and statistically significant impact on income inequality. Macro-level index and all individual indicators of financial inclusion do not affect income inequality in the selected sample of economies. The income inequality issues have different natures and cannot be fixed by financial inclusion only. It needs holistic structural reforms to enable fair distribution of income and make an equitable financial system. Financial inclusion is a relatively less important intervention tool regarding fixing the issue of income inequality. This is one of the first studies that used the DFM method for financial inclusion indices construction.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.12
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• pp.363-370
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• 2021

This study aims to revisit the issues and misconceptions about financial inclusion (FI) indices. For indices construction, this study uses two approaches: one approach following the methodology of Sarma (2008) which is based on UNDP methodology, while the other is the Dynamic Factor Model (DFM)-based index of Stock and Watson (2002) and Rehman et al. (2021). The data of 18 economies of Asia from 1997 till 2017 is used for indices construction and analysis. The authors constructed macro and micro-level financial inclusion indices based on the different types of financial inclusion indicators. Second, the authors have critically evaluated two different approaches, and the results show that Sarma (2008)-based index show financial inclusion's level, while DFM-based index reveal fluctuation in the current year's financial inclusion level due to the prior variations. For measuring the level of financial inclusion, the Sarma (2008) index is effective, while for forecasting the level of financial inclusion, the DFM approach is more appropriate. Furthermore, the micro and macro aspects of financial inclusion should be reflected in separate indices for better understanding and in-depth insights.

• The Journal of the Korea Contents Association
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• v.15 no.10
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• pp.457-467
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• 2015

Despite the recent phenomena of Chonsei price increase, low interest rate and low growth, the indexes of financial and insurance industry production showed the results contrary to the common belief that the financial industry is sensitive to such financial crises. This is because the index of financial industry has continuously maintained a certain level of increase as opposed to the index of all industry production. Thus, this study aimed to analyze the dynamic correlation between the index of financial industry production and Chonsei price increase. A vector autoregression (VAR) model, which doesn't have a cointegrating relationship, was used to define the Chonsei price index and the indexes of all industry production and financial and insurance industry, which are macro economic variables, and describe the data. The results of the analysis on the time series data of 183 months from January 2000 to May 2015 showed that Chonsei price increase was not directly derived from the index of financial industry, but the finance industrial index affected Chonsei price increase.

• Journal of the Korean Home Economics Association
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• v.50 no.4
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• pp.37-50
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• 2012

This research was conducted for the analysis the middle-old aged empty nest households' debt holdings and their financial status(emergency fund index, liquidity index, debt burden index) considering the level of income and assets. In order to accomplish this study, we made use of the KReIS third beta-version data. The results of the analysis were as follows. First, in all income asset groups there were more non-debts holding houses compared to debt holding houses. Moreover in debts holding houses, compared to other groups high income high assets groups were more. Second, the households that possessed more assets, had more debts. Third, the financial status of the households holding debts were more vulnerable compared to households that had no debts. Moreover, all income asset groups' emergency fund index were low. Households having no debts possessed low real assets and so the liquidity index was higher in holding debts households. In holding debts households, debt burden index was high. And especially these houses suffered from high debt burden when their income and asset were low.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.3
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• pp.79-86
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• 2010

Causing by economic stagnation and financial management difficulty of the domestic small and medium business company, the actual condition which enlarges the plan of politic funds increased from the government. The government authority as the SBC (small and medium business promotion corporation) evaluates the domestic small and medium business company, and directly executes a governmental politic funds. Financial analysis is a important factor of enterprise evaluation among various valuation bases. However because of applying to the whole domestic manufacturing industry on financial evaluation, Current financial evaluation index effectively cannot reflect the feature of a regional industry. A whole manufacturing industry index considers a standard for the comparison evaluation of domestic manufacturing industry and compares with the other industries and the world-wide nation industry change, But, there is a difference from indexes of corresponding industry in specific regions. This paper proposes a model of evaluation index for the efficient execution of politic funds. We considers three manufacturing industries for proposed model which are shipbuilding, machine tool and airline industries in Gyeongsangnam-do region.

• Journal of the Korean Data and Information Science Society
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• v.18 no.1
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• pp.31-40
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• 2007

Many previous researchers tried to analysis relationship between financial index of hospitals such as revenue, expenses, and profit and hospital outcome such as number of inpatient and outpatient or, between that financial index and hospital size including number of hospital beds. However, these studies did not find exact relationship between financial index and hospital efficiency and productivity. Therefore, purpose of the study explores exact relationship between hospital financial outcome and hospital efficiency and productivity using adjusted inpatient days concept from American Hospital Association. Through the empirical analysis, the researchers find that hospital profit has the U-shape quadratic function to operation ratio. 66.9% of operation ratio is changing point and hospitals with 55.8% through 75.0% of operation ration have experience deficit situation. Considering the hospital circumstance, Korean hospitals would be to maintain general hospital type with various specialty departments.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.421-431
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• 2021

This study investigates the empirical linkages between ASEAN countries' institutional quality and financial inclusion using country data from 2008-2019. In this paper, six governance indicators from the World Governance index are used to measure the impact of institutions on financial inclusion. The PCA method's financial inclusion index is constructed from 3 indicators: penetration, access, and usage: penetration, access, and usage with six indices respectively as the number of ATMs per 1000 km², the number of bank branches per 1000 km², the number of ATMs per 100,000 people and the number of bank branches for 100,000 adults, the ratio of credit to private to GDP, and the ratio of deposit to private to GDP. Regression analysis with the Generalized Moments method shows the positive impact of institutions and other control variables like GDP per capita, inflation, bank concentration, and human development index on financial inclusion. Therefore, this study recommends that the government and policymakers in countries pursue the financial inclusion agenda to pay attention to the financial and economic indicators and institutional factors. This is because many savers, borrowers, and investors may not be protected when financial contracts are enforced or breaches occur in an environment where economic, legal, judicial, and political institutions are weak, such as in ASEAN countries.