• Journal of the Economic Geographical Society of Korea
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• v.15 no.4
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• pp.585-600
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• 2012

The purpose of this study related to the liquidity impact of the housing market variables using vector auto-regressive model(VAR) and empirical analysis is to derive some policy implications. October 2003 until May 2012 using monthly data for liquidity variables mortgage rates, mortgage, financial liquidity, as the composite index and nation, Seoul, Gangnam, Gangbuk, the Apartment sales prices were analyzed. Granger Causality Test Results, mortgage rates and mortgage at a bargain price two regions had a strong causal relationship. Since the impulse response analysis, Geothermal difference there, but housing price housing price itself, the most significant ongoing positive (+) reactions were liquidity-related variables are mortgage loans is large and persistent positive (+), financial liquidity weakly positive (+), mortgage interest rates are negative (-), KOSPI, the negative (-) reacted. Liquidity and housing prices that the rise can be and Gangnam in Gangbuk is greater than the factor that housing investment was confirmed empirically. Government to consider the current economic situation, while maintaining low interest rates and liquidity of the market rather than the real estate industry must ensure that activities can be embedded and local enforcement policies should be differentiated according to the policy will be able to reap significant effect.

• The Korean Journal of Financial Management
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• v.22 no.2
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• pp.165-187
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• 2005

This paper tries to investigate the relationships among stock return volatility, time-varying risk premium and Korea Discount. Using Korean Composite Stock Price Index (KOSPI) return from January 4, 1980 to August 31, 2005, this study finds possible links between time-varying risk premium and Korea Discount. First of all, this study classifies Korean stock returns during the sample period by three regime-switching volatility period that is to say, low-volatile period medium-volatile period and highly-volatile period by estimating Markov-Switching ARCH model. During the highly volatile period of Korean stock return (09/01/1997-05/31/2001), the estimated time-varying unit risk premium from the jump-diffusion GARCH model was 0.3625, where as during the low volatile period (01/04/1980-l1/30/1985), the time-varying unit risk premium was estimated 0.0284 from the jump diffusion GARCH model, which was about thirteen times less than that. This study seems to find the evidence that highly volatile Korean stock market may induce large time-varying risk premium from the investors and this may lead to Korea discount.

• The Korean Journal of Financial Management
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• v.26 no.4
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• pp.191-234
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• 2009

This study examined on the volatility feedback hypothesis through the use of threshold GARCH-in-Mean (GJR-GARCH-M) model developed by Glosten, Jaganathan, and Runkle (1993) in the stock markets of 14 emerging countries during the period of January, 1996 to May, 2009. On this study, I found successful evidences which can support the volatility feedback hypothesis through the following three estimation procedures. First, I found relatively strong positive relationship between the expected market risk premiums and their conditional standard deviations from the GARCH-M model in the basis of daily return on each representative stock market index, which is appropriate to investors' risk-averse preferences. Second, I can also identify the significant asymmetric time-varying volatility originated from the investors' differentiated reactions toward the unexpected market shocks by applying the GJR-GARCH-M model and further find the lasting positive risk aversion coefficient estimators. Third, I derived the negative signs of the regression coefficient of unpredicted volatility on the stock market return by re-applying the GJR-GARCH-M model after I controlled the positive effect of predicted volatility through including the conditional standard deviations from the previous GARCH-M model estimation as an independent explanatory variable in the re-applied new GJR-GARCH-M model. With these consecutive results, the volatility feedback effect was successfully tested to be effective also in the various emerging stock markets, although the leverage hypothesis turned out to be insufficient to be applied to another source of explaining the negative relationship between the unexpected volatility and the ex-post stock market return in the emerging countries in general.

• International Area Studies Review
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• v.20 no.2
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• pp.113-140
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• 2016

This paper first extracts the main basis for the Middle-Income Trap(MIT) to apply these grounds to reality of the Chinese economy. And then confirmed crisis factors of China economy. Also discussed then the economic reforms of China in order to not fall into the MIT. After reviewing previous research extracted six factors the results will correspond to comply with the Chinese economy. Those are 'Over-investment', 'excess capacity' 'reduction of TFP continued,' 'disappearance and the aging of the population bonus', 'excessive debt and structural adjustment and financial instability of the company', 'income unequal expansion', 'low financial and information infrastructure accessibility', and 'low transparency index'. China's policy direction to avoid the MIT generally set properly, but proof that implementation process not easy, was appearing everywhere. After all, China economy should be modified now to a reforms of 'government failure' and promotion of function for ongoing restructuring system in the market. Because of the SDR incorporation from 2015, it is inevitable to face major constraints in the external aspects.

• 한국노년학
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• v.38 no.2
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• pp.271-290
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• 2018

The study aims to analyze the effects of socio-economic deprivation on suicidal ideation. The analysis data were used as a guide for Korea Welfare Panel Study 9. the frequency analysis, mean difference analysis, correlation analysis, and logistic regression were performed by SPSS programs. The results of analysis are as follows. First, The results of frequency analysis by deprivation type showed a high frequency of deprivation in the following order. Experience of not receiving a public pension, experience of being able to work but unemployed, experience of not being able to eat a balanced diet due to financial difficulties, and experience where you had nothing to eat but no more money to buy. Second, the average difference analysis shows that when a person does not have a spouse, the lower the academic background and the income level, the higher the likelihood of suicide. Third, regression analysis shows that the following deprivation patterns have a statistically significant effect on older adults' thoughts of suicide. Experience in which the respondents or their family could not go to hospital because they had no money, experience that move house because is back rent more than 2 months or can not pay rent, experience that they could not afford to buy food and eat well-balanced meals, experience of failing to pay your bills on time, experience of being able to work but not having a job, and experience in which financial difficulties left them short of food and no money to live. Based on such research results, some policy measures, such as the expanding management of medical care benefits cases, the improvement of elderly housing, residential conditions and the diet survey for the elderly, and the expansion of measures to support elderly people's tax rates, were proposed.

• Journal of Digital Convergence
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• v.19 no.1
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• pp.125-139
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• 2021

This study identifies the trend of Korean accounting researches on profit adjustment. We analyzed the abstract of accounting research articles published in Korean Citation Index (KCI) by using text mining technique. Among papers whose themes were profit adjustment, topics were divided into 4 parts: (i) Auditing and audit reports, (ii) corporate taxes and debt ratios, (iii) general management strategy of companies, and (iv) financial statements and accounting principles. Unlike the prediction that financial statements and accounting principles would be the main topic, auditing was analyzed as the most studied area. We analyzed topic trends based on the number of papers by topic, and could figure out the impact of K-IFRS introduction on profit adjustment research. By using Big Data method, this study enabled the division of research themes that have not been available in the past studies. This study enables the policy makers and business managers to learn about additional considerations in addition to accounting principles related to profit adjustment.

• 한국노년학
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• v.28 no.4
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• pp.887-905
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• 2008

This study investigated the effects of education for the elderly on their psychological well-being. Loneliness(negative state of emotion) and life satisfaction(positive state of emotion) were compared between participants and non-participants of educational programs for the elderly in order to learn whether participating educational programs influences their psychological well-being. The subjects of this research were 288(146 participants and 142 non participants) elderly who are 60 years and older, living in Seoul City and Gyeong Gi Do. They were selected by the judgmental sampling method and surveyed using structured questionnaire. Research instruments were consisted of the UCLA Loneliness Scale, Life Satisfaction Index-Z Scale(LSIZ) and several background questions. The result showed that the participants of the educational programs had a lower level of loneliness and a higher level of life satisfaction. The educational program for the elderly was effective for the psychological well-being of the aged. Multiple regression results showed that subjective health played the most important role in explaining the loneliness, followed by education level, elderly education participation, financial states. The results also showed that subjective health played the most important role in explaining the life satisfaction, followed by elderly education participation, religious activity participation, financial status. Implications for policy, practice, and further research were discussed.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Journal of dental hygiene science
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• v.5 no.2
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• pp.71-76
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• 2005

This study tried to understand influence of water fluoridation program on oral health status and get the basic data of water fluoridation program in the future. Sangdang-gu in Cheongju City, fluoridated community and Manan-gu in Anyang City, non-fluoridated community were the surveyed area of the study. And from July 3, 2003 to July 22, 2003, using questionnaire, we surveyed opinions of parents of the fifth and sixth grade students of C elementary school in Sangdang-gu, Cheongju City and A elementary school in Manan-gu, Anyang City about water fluoridation program, and made an oral examination on the fifth and sixth grade students. The results are as follows : 1. DMFT index was lower for Cheongju, fluoridated community with Cheongju 1.69, Anyang 2.11(P = .010). 2. DMFT rate was lower for Cheongju, fluoridated community with Cheongju 6.72%, Anyang 7.94%. 3. Health level of the first molar was higher for Cheongju, fluoridated community with Cheongju 95.54%, Anyang 94.10%(P = .002). This study intends to understand the effects of fluoridation program on oral status by analyzing the effects of water fluoridation program and present basic materials for improving oral health. Improving national oral health is thought to be associated with expense retrenchment of oral health insurance financial. So it may need to extend using tap water to all the area of the country and additionally establish confidence through an active public relations and education of water fluoridation program.

• Korean Journal of Construction Engineering and Management
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• v.16 no.1
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• pp.101-109
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• 2015

The expected economic effect by investment was divided by square of real discount rate annually for change to present value. Thus, the impact of real discount rate on economic analysis is larger than other factors. The existing general method for prediction of real discount rate is application of average data during past certain period. This study proposed prediction method of real discount rate for accuracy improvement. First, the economic variables which impact on interest rate of business loan and consumer price of real discount rate were determined. The variables which impact on interest rate of business loan were selected to call rate and exchange rate. The variable which impact on consumer price index was selected to producer price index. Next, the effect relation was analyzed between real discount rate and selected variables. The significant effect relation were analyzed to exit. Lastly, the real discount rate was predicted from 2008 to 2010 based on related economic variables. The accuracy of prediction result was compared with actual data and average data. The real discount rate based on actual data, predicted data, and average data were analyzed to -1.58%, -0.22%, and 6.06%, respectively. Though the proposed method in this study was not considered special condition such as financial crisis, the prediction accuracy was much higher than result based on average data.