• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• The korean journal of orthodontics
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• v.22 no.2 s.37
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• pp.413-426
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• 1992

In order to analyze the relationship between teeth and basal bone for the maintainance of the good occlusion, the mesiodistal width of teeth, the basal arch width and the basal arch length were measured on the study model of the normal occlusion group and Angle's class I malocclusion group (non-extraction group, extraction group) The Maximum tooth material, the percentage of basal arch width to maximum tooth material, the percentage of basal arch length to maximum tooth material and the percentage of basal arch width plus basal arch length to maximum tooth material were caculated, and then statistical analysis was done. From thie study, the obtained results were as follows; 1. In maxilla, the percentage of basal arch width to maximum tooth material was $46.9{\pm}2.6\%$ in normal occlusion group, $49.4{\pm}3.9\%$ in non-extraction group, and $42.5{\pm}3.3\%$ in extraction group. In mandible, that was $46.6{\pm}2.4\%$ in normal occlusion group, $47.5{\pm}4.0\%$ in non-extraction group, and $42.6{\pm}2.6\%$ in extraction group. 2. In maxilla, the percentage of basal arch length to maximum tooth material was $33.4{\pm}1.9\%$ in normal occlusion group, $33.9{\pm}1.8\%$ in non-extraction group, and $28.7{\pm}2.5\%$ in extraction group. In mandible, that was $34.4{\pm}4.3\%$ in normal occlusion group, $36.5{\pm}1.9\%$ in non-extraction group, and $31.5{\pm}2.5\%$ in extraction group. 3. In maxilla, the percentage of basal arch width plus basal arch length to maximum tooth material was $80.3{\pm}3.4\%$ in normal occlusion group, $83.3{\pm}4.8\%$ in non-extraction group, and $71.2{\pm}4.3\%$ in extraction group. In mandible, that was $81.0{\pm}5.2\%$ in normal occlusion group, $84.0{\pm}5.4\%$ in non-extraction group, and $74.1{\pm}4.1\%$ in extraction group. 4. In Maxilla, the $95\%$ confidence interval of the percentage of basal arch width to maximum tooth material was $46.3-47.5\%$ in normal occlusion group, $48.1-50.7\%$ in non-extraction group, and $41.7-47.2\%$ in extraction group. In mandible, that was $46.1-47.2\%$ in normal occlusion group, $46.1-48.8\%$ in non-extraction group, and $42.0-43.3\%$ in extraction group. 5. In maxilla, the $95\%$ confidence interval of the percentage of basal arch length to maximum tooth material was $32.9-33.9\%$ in normal occlusion group, $33.3-34.5\%$ in non-extraction group, and $28.1-29.2\%$ in extraction group. In mandible, that was $33.4-3.4\%$ in noraml occlusion group, $35.8-37.2\%$ in non-extraction group, and $30.9-33.1\%$ in extraction group. 6. In maxilla, the $95\%$ confidence interval of thepercentage of basel arch width plus basal arch length to maximum tooth material was $79.5-81.0\%$ in normal occlusion group, $81.6-84.9\%$ in non-extraction group, and $70.1-72.2\%$ in extraction group. In mandible, that was $79.8-82.2\%$ in normal occlusion group, $82.1-85.5\%$ in non-extraction group, and $73.1-75.1\%$ in extraction group. 7. There was correlation between maxilla and mandible in the maximum tooth material, the basal arch width, the basal arch length, the percentage of basal arch width to maximum tooth material, the percentage of basal arch length to maximum tooth material and the percentage of basal arch width plus basal arch length to maximum tooth material, but not in the basal arch length of male of the extraction group. * A thesis submitted to the Council of the Graduate School of Kyungpook national University in partial fulfillment of the requirements for the degree of Master of Dental Science in December, 1991.

• The Journal of Korean Society for Radiation Therapy
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• v.24 no.2
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• pp.67-75
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• 2012

Purpose: The study is to check the specialty of radiotherapists working in the department of radiation oncology and find job satisfaction, organizational commitment and job commitment having an effect on professional parts. After making analysis of the mutual relation, it is to provide radiotechnologists with making progress in the future. Materials and Methods: From March 2 to March 30, we had carried out a survey with email. It is possible to have 272 questionnaires answered in the survey. We make use of SPSS 13.0 for Windows to analyze the data collected for study. Frequency and a percentage are meant to show general characteristics, and t-test and ANOVA to do the difference between general properties and professionalism. Pearson's correlation coefficient also is meant to do the correlation of professionalism, organizational job commitment and job satisfaction, and multiple regression analysis to do the factor for a relevant variable to affect professionalism. Results: There are subdivisions in the professionalism informing us of the self-regulation $17.74{\pm}2.32/3.55{\pm}.46$, a sense of calling $17.58{\pm}2.63/3.52{\pm}.53$, reference of the professional $17.14{\pm}2.39/3.43{\pm}.48$, service to the public $15.97{\pm}2.48/3.19{\pm}50$, and autonomy $15.68{\pm}2.28/3.14{\pm}46$. Grand mean turns out to be $83.89{\pm}7.63$(Summation of items)/$3.37{\pm}0.49$ (Numbers of items). When it comes to a statistical relation between general characteristics and professionalism, the statistics have it that these come within age (P<.001), period of employment (P<.001), education status (P<.05), a monthly income (P<.001), radiotherapists who get a special license (P<.001), the position (P<.001), and an opportunity for developing (P<.001). As a result of organizational commitment, job commitment, and job satisfaction, grand mean in organizational commitment proves to be $80.10{\pm}8.15/3.34{\pm}.34$. There are subvisions showing affective commitment $28.64{\pm}4.61$/3.58, continuance commitment $27.54{\pm}4.22/3.44{\pm}.53$, and normative commitment $23.95{\pm}2.94/2.99{\pm}.37$ in order of precedence. The average grade in job commitment is $32.47{\pm}5.77/3.30{\pm}.60$ and that in job satisfaction is $63.39{\pm}10.16/3.17{\pm}.51$, respectively. We find the positive relationship between professionalism and organizational commitment (r=.522, P<.05), between professionalism and job commitment (r=.444, P<.05), and between professionalism and job satisfaction (r=.507, P<.05). And we also get the positive relationship between organizational commitment and job commitment (r=.549, P<.05), between organizational commitment and job satisfaction (r=.433, P<.05), and between job commitment and job satisfaction (r=.462, P<.05). To catch the factors influencing the professionalism of radiotherapists, we used multiple regression analysis. According to the final model, it appears affective commitment (B=.755, P<.05), normative commitment (B=.305, P<.05), job satisfaction (B=.092, P<.05), an opportunity for developing (B=-1.505, P<.05), and the position (B=-1.155, P<.05) in order of precedence. It seems that explaining influece on $R^2$ is 0.504. Conclusion: The results of the factors that influence professionalism working as radiotherapists in the department of radiation oncology have it that the more affective commitment, normative commitment, and job satisfaction we feel, the more professionalism we recognize. We think that the focus of professionalism is increased if getting the chances for radiotherapists to have little to do with developing opportunities given.

• Journal of the Korean association of regional geographers
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• v.6 no.2
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• pp.1-19
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• 2000

This study is to investigate and analyze regional patterns of aging in Taejeon Metropolitan city-the overpopulated area of Choong-Cheong Province-by cohort analysis method. According to the population structure transition caused by rapid social and economic changes, Korea has made a rapid progress in population aging since 1970. This trend is so rapid that we should prepare for and cope with aging society. It is not only slow to cope with it in our society, but also there are few studies on population aging of the geographical field in Korea. The data of this study are the reports of Population and Housing Censuses in 1975 and 1985 and General Population and Housing Censuses with 10% sample survey in 1995 taken by National Statistical Office. The research method is to sample as the aging district the area with high aged population rate where the populations over 60 reside among total population during the years of 1975, 1985, 1995 and to sample the special districts of decreasing population where the population decreases very much and the special districts of increasing population in which the population increases greatly, presuming that the reason why aged population rate increases is that non-elderly population high in mobility moves out. It is then verified and ascertained whether it is true or not with cohort analysis method by age. Finally regional patterns in the city are found through the classification and modeling by type based on the aging district, the special districts of decreasing population, and the special districts of increasing population. The characteristics of the regional patterns show that there is social population transition and that non-elderly population moves out. The aging district with the high aged population rate is divided into high-level keeping-up type, relative falling type below the average of Taejeon city in aging progress, and relative rising type above the average of the city. This district can be found at both the central area of the city and the suburbs because Taejeon city has the characteristic of over-bounded city. But it cannot be found at the new built-up area with the in-migration of large population. The special districts of decreasing population where the population continues to decrease can be said to be the population doughnuts found at the CBD and its neighboring inner area. On the other hand, the special districts of increasing population where the population continues to increase are located at the new built-up area of the northern part in Taejeon city. The special districts of decreasing population are overlapping with the aging district and higher in aged population rate by the out-migration of non-elderly population. The special districts of increasing population are not overlapping with the aging district and lower in aged population rate by the in-migration of non-elderly population. To clarify the distribution map of the aging district, the special districts of decreasing and increasing population and the aging district are divided into four groups such as the special districts of decreasing population group-the same one as the aging district, the special districts of decreasing population group, the special districts of increasing population group, and the other district. With the cohort analysis method by age used to investigate the definite increase and decrease of aging population through population transition of each group, it is found that the progress of population aging is closely related to the social population fluctuation, especially that aged population rate is higher with the out-migration of non-elderly population. This is to explain each model of CBD, inner area, and the suburbs after modeling the aging district, the special districts of decreasing population, and the special districts of increasing population in Taejeon city. On the assumption that the city area is a concentric circle, it is possible to divide it into three areas such as CBD(A), the inner area(B), and the suburbs(C). The special districts of increasing and decreasing population in the city are divided into three districts-the special districts of decreasing population(a), the special districts of increasing population(b), and the others(c). The aging district of this city is divided into the aging district($\alpha$) and the others($\beta$). And then modeling these districts, it is probable to find regional patterns in the city. $Aa{\alpha}$ and $Ac{\beta}$ patterns are found in the CBD, in which $Aa{\alpha}$ is the special district of decreasing population and is higher in aged population rate because of aged population low in mobility staying behind and out-migration of non-elderly population. $Ba{\alpha}$, $Ba{\beta}$, $Bb{\beta}$, and $Bc{\beta}$ patterns are found in the inner area, in which neighboring area $Ba{\alpha}$ pattern is located. $Bb{\beta}$ pattern is located at the new developing area of newly built apartment complex. $Cb{\beta}$, $Cc{\alpha}$, and $Cc{\beta}$ patterns are found in the suburbs, among which $Cc{\alpha}$ pattern is highest in population aging. It is likely that the $Cc{\beta}$ under housing land readjustment on a large scale will be the $Cb{\beta}$ pattern. As analyzed above, marriage and out-migration of new family, non-elderly population, with house purchase are main factors in accelerating population aging in the central area of the city. Population aging is responsible for the great increase of aged population with longer life expectancy by the low death rate, the out-migration of non-elderly population, and the age group of new aged population in the suburbs. It is necessary to investigate and analyze the regional patterns of population aging at the time when population problems caused by aging as well as longer life expectancy are now on the increase. I hope that this will help the future study on population aging of the geographical field in Korea. As in the future population aging will be a major problem in our society, local autonomy should make a plan for the problem to the extent that population aging progresses by regional groups and inevitably prepare for it.

• Journal of Distribution Research
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• v.15 no.3
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• pp.71-110
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• 2010

1. Introduction Today Internet is recognized as an important way for the transaction of products and services. According to the data surveyed by the National Statistical Office, the on-line transaction in 2007 for a year, 15.7656 trillion, shows a 17.1%(2.3060 trillion won) increase over last year, of these, the amount of B2C has been increased 12.0%(10.2258 trillion won). Like this, because the entry barrier of on-line market of Korea is low, many retailers could easily enter into the market. So the bigger its scale is, but on the other hand, the tougher its competition is. Particularly due to the Internet and innovation of IT, the existing market has been changed into the perfect competitive market(Srinivasan, Rolph & Kishore, 2002). In the early years of on-line business, they think that the main reason for success is a moderate price, they are awakened to its importance of on-line service quality with tough competition. If it's not sure whether customers can be provided with what they want, they can use the Web sites, perhaps they can trust their products that had been already bought or not, they have a doubt its viability(Parasuraman, Zeithaml & Malhotra, 2005). Customers can directly reserve and issue their air tickets irrespective of place and time at the Web sites of travel agencies or airlines, but its empirical studies about these Web sites for reserving and issuing air tickets are insufficient. Therefore this study goes on for following specific objects. First object is to measure service quality and service recovery of Web sites for reserving and issuing air tickets. Second is to look into whether above on-line service quality and on-line service recovery have an impact on overall service quality. Third is to seek for the relation with overall service quality and customer satisfaction, then this customer satisfaction and loyalty intention. 2. Theoretical Background 2.1 On-line Service Quality Barnes & Vidgen(2000; 2001a; 2001b; 2002) had invented the tool to measure Web sites' quality four times(called WebQual). The WebQual 1.0, Step one invented a measuring item for information quality based on QFD, and this had been verified by students of UK business school. The Web Qual 2.0, Step two invented for interaction quality, and had been judged by customers of on-line bookshop. The WebQual 3.0, Step three invented by consolidating the WebQual 1.0 for information quality and the WebQual2.0 for interactionquality. It includes 3-quality-dimension, information quality, interaction quality, site design, and had been assessed and confirmed by auction sites(e-bay, Amazon, QXL). Furtheron, through the former empirical studies, the authors changed sites quality into usability by judging that usability is a concept how customers interact with or perceive Web sites and It is used widely for accessing Web sites. By this process, WebQual 4.0 was invented, and is consist of 3-quality-dimension; information quality, interaction quality, usability, 22 items. However, because WebQual 4.0 is focusing on technical part, it's usable at the Website's design part, on the other hand, it's not usable at the Web site's pleasant experience part. Parasuraman, Zeithaml & Malhorta(2002; 2005) had invented the measure for measuring on-line service quality in 2002 and 2005. The study in 2002 divided on-line service quality into 5 dimensions. But these were not well-organized, so there needed to be studied again totally. So Parasuraman, Zeithaml & Malhorta(2005) re-worked out the study about on-line service quality measure base on 2002's study and invented E-S-QUAL. After they invented preliminary measure for on-line service quality, they made up a question for customers who had purchased at amazon.com and walmart.com and reassessed this measure. And they perfected an invention of E-S-QUAL consists of 4 dimensions, 22 items of efficiency, system availability, fulfillment, privacy. Efficiency measures assess to sites and usability and others, system availability measures accurate technical function of sites and others, fulfillment measures promptness of delivering products and sufficient goods and others and privacy measures the degree of protection of data about their customers and so on. 2.2 Service Recovery Service industries tend to minimize the losses by coping with service failure promptly. This responses of service providers to service failure mean service recovery(Kelly & Davis, 1994). Bitner(1990) went on his study from customers' view about service providers' behavior for customers to recognize their satisfaction/dissatisfaction at service point. According to them, to manage service failure successfully, exact recognition of service problem, an apology, sufficient description about service failure and some tangible compensation are important. Parasuraman, Zeithaml & Malhorta(2005) approached the service recovery from how to measure, rather than how to manage, and moved to on-line market not to off-line, then invented E-RecS-QUAL which is a measuring tool about on-line service recovery. 2.3 Customer Satisfaction The definition of customer satisfaction can be divided into two points of view. First, they approached customer satisfaction from outcome of comsumer. Howard & Sheth(1969) defined satisfaction as 'a cognitive condition feeling being rewarded properly or improperly for their sacrifice.' and Westbrook & Reilly(1983) also defined customer satisfaction/dissatisfaction as 'a psychological reaction to the behavior pattern of shopping and purchasing, the display condition of retail store, outcome of purchased goods and service as well as whole market.' Second, they approached customer satisfaction from process. Engel & Blackwell(1982) defined satisfaction as 'an assessment of a consistency in chosen alternative proposal and their belief they had with them.' Tse & Wilton(1988) defined customer satisfaction as 'a customers' reaction to discordance between advance expectation and ex post facto outcome.' That is, this point of view that customer satisfaction is process is the important factor that comparing and assessing process what they expect and outcome of consumer. Unlike outcome-oriented approach, process-oriented approach has many advantages. As process-oriented approach deals with customers' whole expenditure experience, it checks up main process by measuring one by one each factor which is essential role at each step. And this approach enables us to check perceptual/psychological process formed customer satisfaction. Because of these advantages, now many studies are adopting this process-oriented approach(Yi, 1995). 2.4 Loyalty Intention Loyalty has been studied by dividing into behavioral approaches, attitudinal approaches and complex approaches(Dekimpe et al., 1997). In the early years of study, they defined loyalty focusing on behavioral concept, behavioral approaches regard customer loyalty as "a tendency to purchase periodically within a certain period of time at specific retail store." But the loyalty of behavioral approaches focuses on only outcome of customer behavior, so there are someone to point the limits that customers' decision-making situation or process were neglected(Enis & Paul, 1970; Raj, 1982; Lee, 2002). So the attitudinal approaches were suggested. The attitudinal approaches consider loyalty contains all the cognitive, emotional, voluntary factors(Oliver, 1997), define the customer loyalty as "friendly behaviors for specific retail stores." However these attitudinal approaches can explain that how the customer loyalty form and change, but cannot say positively whether it is moved to real purchasing in the future or not. This is a kind of shortcoming(Oh, 1995). 3. Research Design 3.1 Research Model Based on the objects of this study, the research model derived is