• Journal of Society of Preventive Korean Medicine
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• v.4 no.2
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• pp.119-141
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• 2000

The current industrial health service is shifting to health improvement business with 1st primary prevention-focused service from secondary and tertiary prevention-focused business, and Oriental medicine can provide such primary prevention-focused service due to the characteristics of its science. In particular, the advanced concept of health improvement can match the science of health care of Oriental medicine. Notably, what is most important in health improvement is our lifestyle, This does not underestimate the socio-environmental factors, which have lessened their importance due to modernism. The approach of Oriental medicine weighs more individuals' lifestyle and health care through self-cultivation. This matches the new model of advanced health business. Oriental medicine is less systemized than Western medicine, but it can provide ample contents that enhance health. If we conceive health-improvement program based on the advantages provided by these two medical systems, this will influence workers to the benefit of their health. Also, health Program needs to define factors that determine individual lives, and to provide information and technologies essential to our lives. The Oriental medicine approach puts more stress on a subject's capabilities than it does on the effect his surrounding environment can have. This needs to be supported theoretically by not only defining the relations between an individual's health state and his lifestyle, but also identifying the degree to which an individual in the industrial work place practices health improvement lifestyle . This is the first step toward initiating health-improvement business . In order to do this, this researcher conducted a survey by taking random samplings from workers, and can draw the following conclusions from it. 1 The sampled group is categorized into', by sender, female 6.6%, and male 93.4%, with males dominant; by marriage status , unmarried 43.9% and married 55.6%, with both similar percentage, and, by age, below 30, 48.4%, between 30 and 39, 27.4%, between 40 and 49, 18.2%, and over 50, 6.0%. The group further is categorized into; by education, middle school or under 1.7%, high school 30.5%, and junior college or higher 65.8% with high school and higher dominant: and by income, below 1.7 million won 24.2%, below 2.4 million won 14.8%, and above 2.4 million 6.3% Still, the group by job is categorized into collegians with 23.9%, office worker with 10.3%, and professionals with 65.8% , and this group does not include workers engaged in production that are needed for this research, but mostly office workers . 2. The subjects selected for this survey show their degree of practicing health-improvement lifestyle at an average of 2.63, health management pattern at 2.64, and health-related awareness at 2.62 The sub-divisions of health-improvement lifestyle show social emotion (2.87), food (2.66). favorite food (2.59), and leisure activities (2.52), in this order for higher points. It further shows health awareness (2.47) and safety awareness (2.40), lower points than those in health management pattern . 3. In the area of using leisure time for health-improvement, males, older people, married, and people with higher income earn higher marks. And, in the area of food management, the older and married earn higher marks . In the area of favorite food management, females, lower-income bracket, and lower-educated show higher degree of practice , while in the area of social emotion management, the older. married, and higher-income bracket show higher marks. In addition, in the area of health awareness, the older, married, and people with higher-income show higher degree of practice. 4. To look at correlation by overall and divisional health-improvement practice degree , this researcher has analyzed the data using Person's correlation coefficient. The lifestyle shows significant correlation with its six sub-divisions, and use of leisure time, food, and health awareness all show significant correlation with their sub-divisions. And. the social emotion and safety awareness show significant correlation with all sub-divisions except favorite food management.

• 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.