• Journal of Information Technology Applications and Management
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• v.23 no.4
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• pp.25-39
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• 2016

Asset prices decline sharply and stock markets collapse when financial crisis happens. Recently we have encountered more frequent financial crises than ever. 1998 currency crisis and 2008 global financial crisis triggered academic researches on early warning systems that aim to detect the symptom of financial crisis in advance. This study proposes a risk recovery index for detection of good opportunities from financial market instability. We use SVM classifier algorithms to separate recovery period from unstable financial market data. Input variables are KOSPI index and V-KOSPI200 index. Our SVM algorithms show highly accurate forecasting results on testing data as well as training data. Risk recovery index is derived from our SVM-trained outputs. We develop a trading system that utilizes the suggested risk recovery index. The trading result records very high profit, that is, its annual return runs to 121%.

• 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.

• 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.

• 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.

• 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.

• 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 Korean Society of Industrial and Systems Engineering
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• v.32 no.4
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• pp.235-240
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• 2009

This study examines the competitive strategy of service quality in financial industry. Service quality has been suggested as the approach for service organizations to be service and customer oriented. Recently, the issue of internal service quality has been one of topics widely discussed in financial industry. The purpose of the current study is to examine the internal service quality which is believed to increase such organizational performances as customer satisfaction, retention and revenue. Internal service quality index is used to measure service quality in service industry. In this paper, the methodology for measuring the degree of service quality which has been adopted in service quality research area was used. This paper proposes internal service quality index in financial industry. In this paper, we show the application method of internal service quality index.

• 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.

• Proceedings of the Korean Society for Quality Management Conference
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• 2009.10a
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• pp.109-114
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• 2009

Recently, there has been growing interest in the financial and economic effectiveness of service quality and customer satisfaction. It means that the final goal of customer satisfaction is the maximization with firms' financial performance, enterprise could survive through the creation of continuous financial performance. Companies are working in various ways to identify the direct relationship of service quality and customer satisfaction with financial and economic effectiveness in order to justify and validate customer satisfaction management. In this study, the influence of customer satisfaction on the financial performance is examined. Also, we have analyzed customer satisfaction by comparing financial outcome of each industry Customer satisfaction index, the key non-financial performance measure has significant association with firms' financial performance index. Moreover, high customer satisfaction has a competitive advantage, so it can be the key success factor of firm's financial performance improvement.