• 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.

• Tuberculosis and Respiratory Diseases
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• v.42 no.6
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• pp.846-854
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• 1995

Background: Cytokeratin 19 is a subunit of cytokeratin intermediate filament expressed in simple epithelia such as respiratory epithelial cells and their malignant counterparts. An immunoradiometric assay is available to detect a fragment of the cytokeratin, referred to as Cyfra 21-1 in the serum. This study was conducted to evaluate the clinical utility of this new marker in the diagnosis of lung cancer compared with established markers of squamous cell carcinoma antigen (SCC Ag) and carcino-embryonic antigen(CEA). In addition, we compared the diagnostic sensitivity and specificity of Cyfra 21-1 with those of SCC Ag in squamous cell carcinoma of the lung. We also measured the level of Cyfra 21-1 in the different stages of squamous cell carcinoma of the lung. Method: We measured Cyfra 21-1(ELSA-CYFRA 21-1), SCC Ag(ABBOTT SCC RIABEAD) and CEA(ELSA2-CEA) in 79 patients with primary lung cancer and in 78 persons as a comparison group including 32 patients with pulmonary tuberculosis, 23 patients with benign lung disease and 23 cases with healthy individual. Cyfra 21-1 is measured by a solid-phase immunoradiometric assay(CIS Bio International, France) based on the two-site sandwich method. SCC Ag is measured by a radioimmunoassay(Abbott Laboratories, USA). CEA is measured by a immunoradiometric assay(CIS Bio International, France). All data were expressed as the mean$\pm$standard deviation. Results: 1) The mean value of Cyfra 21-1 was $18.38{\pm}3.65\;ng/mL$ in the lung cancer and $1.l6{\pm}0.53\;ng/mL$ in the comparison group(p<0.0001). SCC Ag was $3.53{\pm}6.06\;ng/mL$ in the lung cancer and $1.19{\pm}0.5\;ng/mL$ in the comparison group(p<0.01). CEA was $35.03{\pm}13.9\;ng/mL$ in the lung cancer and $2.89{\pm}1.01\;ng/mL$ in the comparison group(p<0.0001). 2) Cyfra 21-1 level in squamous cell carcinoma($31.52{\pm}40.13\;ng/mL$) was higher than that in adenocarcinoma($2.41{\pm}1.34\;ng/mL$)(p<0.0001) and small cell carcinoma($2.15{\pm}2.05\;ng/mL$)(p=0.007). SCC Ag level in squamous cell carcinoma($5.1{\pm}7.68\;ng/mL$) was higher than that in adenocarcinoma($1.36{\pm}0.69\;ng/mL$)(p=0.009) and small cell carcinoma($1.1{\pm}0.24\;ng/mL$) (p=0.024). 3) The level of Cyfra 21-1 was not correlated with the progression of stage in squamous cell carcinoma of the lung. 4) Using the cut-off value of 3.3ng/mL, the diagnostic sensitivity of Cyfra 21-1 was 83% in squamous cell carcinoma, 22% in adenocarcinoma and 17% in small cell carcinoma. The sensitivity of SCC Ag and CEA were 39% and 20%, respectively in squamous cell carcinoma, 11% and 39% in adenocarcinoma, and 0% and 33% in small cell carcinoma. 5) Comparison of the receiver operating characteristics curves(ROC curve) for Cyfra 21-1, SCC Ag and CEA revealed that Cyfra 21-1 showed highest diagnostic sensitivity among them in the diagnosis of lung cancer. Conclusion: Cyfra 21-1 is thought to be a better tumor marker for the diagnosis of lung cancer than SCC Ag and CEA, especially in squamous cell carcinoma of the lung.

• Journal of Korean Home Economics Education Association
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• v.24 no.2
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• pp.117-134
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• 2012

The purpose of this study was to identify the stage of concern, the level of use, and the innovation configuration of Home Economics teachers regarding creativity and personality education in Home Economics(HE) classes. The survey questionnaires were sent through mails and e-mails to middle-school HE teachers in the whole country selected by systematic sampling and convenience sampling. Questionnaires of the stages of concern and the levels of use developed by Hall(1987) were used in this study. 187 data were used for the final analysis by using SPSS/window(12.0) program. The results of the study were as following: First, for the stage of concerns of HE teachers on creativity and personality education, the information stage of concerns(85.51) was the one with the highest response rate and the next high in the following order: the management stage of concerns(81.88), the awareness stage of concerns(82.15), the refocusing stage of concerns(68.80), the collaboration stage of concerns(61.97), and the consequence stage of concerns(59.76). Second, the levels of use of HE teachers on creativity and personality education was highest with the mechanical levels(level 3; 21.4%) and the next high in the following order: the orientation levels of use(level 1; 20.9%), the refinement levels(level 5; 17.1%), the non-use levels(level 0; 15.0%), the preparation levels(level 2; 10.2%), the integration levels(level 6; 5.9%), the renewal levels(level 7; 4.8%), the routine levels(level 4; 4.8%). Third, for the innovation configuration of HE teachers on creativity and personality education, more than half of the HE teachers(56.1%) mainly focused on personality education in their HE classes; 31.0% of the HE teachers performed both creativity and personality education; a small number of teachers(6.4%) focused on creativity education; the same number of teachers(6.4%) responded that they do not focus on neither of the two. Examining the level and type of performance HE teachers applied, the average score on the performance of creativity and personality education was 3.76 out of 5.00 and the mean of creativity component was 3.59 and of personality component was 3.94, higher than standard. For the creativity education, openness/sensitivity(3.97) education was performed most and the next most in the following order: problem-solving skill(3.79), curiosity/interest(3.73), critical thinking(3.63), problem-finding skill(3.61), originality(3.57), analogy(3.47), fluency/adaptability(3.46), precision(3.46), imagination(3.37), and focus/sympathy(3.37). For the personality education, the following components were performed in order from most to least: power of execution(4.07), cooperation/consideration/just(4.06), self-management skill(4.04), civic consciousness(4.04), career development ability(4.03), environment adaptability(3.95), responsibility/ownership(3.94), decision making(3.89), trust/honesty/promise(3.88), autonomy(3.86), and global competency(3.55). Regarding what makes performing creativity and personality education difficult, most HE teachers(64.71%) chose the lack of instructional materials and 40.11% of participants chose the lack of seminar and workshop opportunity. 38.5% chose the difficulty of developing an evaluation criteria or an evaluation tool while 25.67% responded that they do not know any means of performing creativity and personality education. Regarding the better way to support for creativity and personality education, the HE teachers chose in order from most to least: 'expansion of hands-on activities for students related to education on creativity and personality'(4.34), 'development of HE classroom culture putting emphasis on creativity and personality'(4.29), 'a proper curriculum on creativity and personality education that goes along with students' developmental stages'(4.27), 'securing enough human resource and number of professors who will conduct creativity and personality education'(4.21), 'establishment of the concept and value of the education on creativity and personality'(4.09), and 'educational promotion on creativity and personality education supported by local communities and companies'(3.94).