• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.19-32
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• 2010

Volatility plays a central role in both academic and practical applications, especially in pricing financial derivative products and trading volatility strategies. This study presents a novel mechanism based on generalized autoregressive conditional heteroskedasticity (GARCH) models that is able to enhance the performance of intelligent volatility trading systems by predicting Korean stock market volatility more accurately. In particular, we embedded the concept of the volatility asymmetry documented widely in the literature into our model. The newly developed Korean stock market volatility index of KOSPI 200, VKOSPI, is used as a volatility proxy. It is the price of a linear portfolio of the KOSPI 200 index options and measures the effect of the expectations of dealers and option traders on stock market volatility for 30 calendar days. The KOSPI 200 index options market started in 1997 and has become the most actively traded market in the world. Its trading volume is more than 10 million contracts a day and records the highest of all the stock index option markets. Therefore, analyzing the VKOSPI has great importance in understanding volatility inherent in option prices and can afford some trading ideas for futures and option dealers. Use of the VKOSPI as volatility proxy avoids statistical estimation problems associated with other measures of volatility since the VKOSPI is model-free expected volatility of market participants calculated directly from the transacted option prices. This study estimates the symmetric and asymmetric GARCH models for the KOSPI 200 index from January 2003 to December 2006 by the maximum likelihood procedure. Asymmetric GARCH models include GJR-GARCH model of Glosten, Jagannathan and Runke, exponential GARCH model of Nelson and power autoregressive conditional heteroskedasticity (ARCH) of Ding, Granger and Engle. Symmetric GARCH model indicates basic GARCH (1, 1). Tomorrow's forecasted value and change direction of stock market volatility are obtained by recursive GARCH specifications from January 2007 to December 2009 and are compared with the VKOSPI. Empirical results indicate that negative unanticipated returns increase volatility more than positive return shocks of equal magnitude decrease volatility, indicating the existence of volatility asymmetry in the Korean stock market. The point value and change direction of tomorrow VKOSPI are estimated and forecasted by GARCH models. Volatility trading system is developed using the forecasted change direction of the VKOSPI, that is, if tomorrow VKOSPI is expected to rise, a long straddle or strangle position is established. A short straddle or strangle position is taken if VKOSPI is expected to fall tomorrow. Total profit is calculated as the cumulative sum of the VKOSPI percentage change. If forecasted direction is correct, the absolute value of the VKOSPI percentage changes is added to trading profit. It is subtracted from the trading profit if forecasted direction is not correct. For the in-sample period, the power ARCH model best fits in a statistical metric, Mean Squared Prediction Error (MSPE), and the exponential GARCH model shows the highest Mean Correct Prediction (MCP). The power ARCH model best fits also for the out-of-sample period and provides the highest probability for the VKOSPI change direction tomorrow. Generally, the power ARCH model shows the best fit for the VKOSPI. All the GARCH models provide trading profits for volatility trading system and the exponential GARCH model shows the best performance, annual profit of 197.56%, during the in-sample period. The GARCH models present trading profits during the out-of-sample period except for the exponential GARCH model. During the out-of-sample period, the power ARCH model shows the largest annual trading profit of 38%. The volatility clustering and asymmetry found in this research are the reflection of volatility non-linearity. This further suggests that combining the asymmetric GARCH models and artificial neural networks can significantly enhance the performance of the suggested volatility trading system, since artificial neural networks have been shown to effectively model nonlinear relationships.

• The Korean Journal of Quaternary Research
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• v.20 no.1 s.26
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• pp.51-66
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• 2006

The climate of the last glacial maximum (LGM) in northeast Asia is simulated with an atmospheric general circulation model of NCAR CCM3 at spectral truncation of T170, corresponding to a grid cell size of roughly 75 km. Modern climate is simulated by a prescribed sea surface temperature and sea ice provided from NCAR, and contemporary atmospheric CO2, topography, and orbital parameters, while LGM simulation was forced with the reconstructed CLIMAP sea surface temperatures, sea ice distribution, ice sheet topography, reduced $CO_2$, and orbital parameters. Under LGM conditions, surface temperature is markedly reduced in winter by more than $18^{\circ}C$ in the Korean west sea and continental margin of the Korean east sea, where the ocean exposed to land in the LGM, whereas in these areas surface temperature is warmer than present in summer by up to $2^{\circ}C$. This is due to the difference in heat capacity between ocean and land. Overall, in the LGM surface is cooled by $4{\sim}6^{\circ}C$ in northeast Asia land and by $7.1^{\circ}C$ in the entire area. An analysis of surface heat fluxes show that the surface cooling is due to the increase in outgoing longwave radiation associated with the reduced $CO_2$ concentration. The reduction in surface temperature leads to a weakening of the hydrological cycle. In winter, precipitation decreases largely in the southeastern part of Asia by about $1{\sim}4\;mm/day$, while in summer a larger reduction is found over China. Overall, annual-mean precipitation decreases by about 50% in the LGM. In northeast Asia, evaporation is also overall reduced in the LGM, but the reduction of precipitation is larger, eventually leading to a drier climate. The drier LGM climate simulated in this study is consistent with proxy evidence compiled in other areas. Overall, the high-resolution model captures the climate features reasonably well under global domain.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• The Southeast Asian review
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• v.27 no.2
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• pp.37-76
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• 2017

$Nguy{\tilde{\hat{e}}}n$ Cư Trinh has two faces in the history of territorial expansion of Vietnam into the Mekong delta. One is his heroic contribution to the $Nguy{\tilde{\hat{e}}}n$ family gaining control over the large part of the Mekong delta. The other is his role to make the eyes of readers of Vietnamese history be fixed only to the present territory of Vietnam. To the readers, $Nguy{\tilde{\hat{e}}}n$ Cư Trinh's achievement of territorial expansion was the final stage of the nam $ti{\acute{\hat{e}}n$ of Vietnam. In fact, however, his achievement was partial. This study pays attention to the King $V{\tilde{o}}$ instead of $Nguy{\tilde{\hat{e}}}n$ Cư Trinh in the history of the territorial expansion in the Mekong delta. King's goal was more ambitious. And the ambition was propelled by his dream to build a new world, and its order, in which his new capital, $Ph{\acute{u}}$ $Xu{\hat{a}}n$ was to be the center with his status as an emperor. To improve my assertion, three elements were examined in this article. First is the nature of $V{\tilde{o}}$ Vương's new kingship. Second is the preparation and the background of the military operation in the Mekong Delta. The nature of the new territory is the third element of the discussion. In 1744, six years after this ascending to the throne, $V{\tilde{o}}$ Vương declared he was a king. Author points out this event as the departure of the southern kingdom from the traditional dynasties based on the Red River delta. Besides, the government system, northern custom and way of dressings were abandoned and new southern modes were adopted. $V{\tilde{o}}$ Vương had enough tributary kingdoms such as Cambodia, Champa, Thủy $X{\tilde{a}}$, Hoả $X{\tilde{a}}$, Vạn Tượng, and Nam Chưởng. Compared with the $L{\hat{e}}$ empire, the number of the tributary kingdoms was higher and the number was equivalent to that of the Đại Nam empire of the 19th century. In reality, author claims, the King $V{\tilde{o}}^{\prime}s$ real intention was to become an emperor. Though he failed in using the title of emperor, he distinguished himself by claiming himself as the Heaven King, $Thi{\hat{e}}n$ Vương. Cambodian king's attack on the thousands of Cham ethnics in Cambodian territory was an enough reason to the King $V{\tilde{o}}^{\prime}s$ military intervention. He considered these Cham men and women as his amicable subjects, and he saw them a branch of the Cham communities in his realm. He declared war against Cambodia in 1750. At the same time he sent a lengthy letter to the Siamese king claiming that the Cambodia was his exclusive tributary kingdom. Before he launched a fatal strike on the Mekong delta which had been the southern part of Cambodia, $V{\tilde{o}}$ Vương renovated his capital $Ph{\acute{u}}$ $Xu{\hat{a}}n$ to the level of the new center of power equivalent to that of empire for his sake. Inflation, famine, economic distortion were also the features of this time. But this study pays attention more to the active policy of the King $V{\tilde{o}}$ as an empire builder than to the economic situation that has been told as the main reason for King $V{\tilde{o}}^{\prime}s$ annexation of the large part of the Mekong delta. From the year of 1754, by the initiative of $Nguy{\tilde{\hat{e}}}n$ Cư Trinh, almost whole region of the Mekong delta within the current border line was incorporated into the territory of $V{\tilde{o}}$ Vương within three years, though the intention of the king was to extend his land to the right side of the Mekong Basin beyond the current border such as Kampong Cham, Prey Vieng, and Svai Rieng. The main reason was $V{\tilde{o}}$ Vương's need to expand his territory to be matched with that of his potential empire with the large number of the tributary kingdoms. King $V{\tilde{o}}^{\prime}s$ strategy was the variation of 'silkworm nibbling' and 'to strike barbarians by barbarians.' He ate the land of Lower Cambodia, the region of the Mekong delta step by step as silkworm nibbles mulberry leave(general meaning of $t{\acute{a}}m$ thực), but his final goal was to eat all(another meaning of $t{\acute{a}}m$ thực) the part of the Mekong delta including the three provinces of Cambodia mentioned above. He used Cham to strike Cambodian in the process of getting land from Long An area to $Ch{\hat{a}}u$ Đốc. This is a faithful application of the Dĩ Man $C{\hat{o}}ng$ Man (to strike barbarians by barbarians). In addition he used Chinese refugees led by the Mạc family or their quasi kingdom to gain land in the region of $H{\grave{a}}$ $Ti{\hat{e}}n$ and its environs from the hand of Cambodian king. This is another application of Dĩ Man $C{\hat{o}}ng$ Man. In sum, author claims a new way of looking at the origin of the imperial world order which emerged during the first half of the 19th century. It was not the result of the long history of Đại Việt empires based on the Red River delta, but the succession of the King $V{\tilde{o}}^{\prime}s$ new world based on $Ph{\acute{u}}$ $Xu{\hat{a}}n$. The same ways of Dĩ Man $C{\hat{o}}ng$ Man and $T{\acute{a}}m$ Thực Chi $K{\acute{\hat{e}}}$ were still used by $V{\tilde{o}}^{\prime}s$ descendents. His grandson Gia Long used man such as Thai, Khmer, Lao, Chinese, and European to win another man the '$T{\hat{a}}y$ Sơn bandits' that included many of Chinese pirates, Cham, and other mountain peoples. His great grand son Minh Mạng constructed a splendid empire. At the same time, however, Minh Mạng kept expanding the size of his empire by eating all the part of Cambodia and Cham territories.

• 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.

• Journal of agricultural medicine and community health
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• v.20 no.1
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• pp.1-13
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• 1995

Based on the questionnaires sent to 270 nurses of public health centers in kyungnam during the period of March 19 to April 11 in 1992, this study was written for the of finding out the grade of satisfaction, obstacles in carrying out duties concerned with nursing services and the change of nurses role needed according to the change of the local public health administration. The first-ranking tasks carried by nurses of public health center are believed to have been family planning activities before the 1970's, nursing services during the 1970's, mother-child health activities during the 1980's, and nursing services during the period of 1990 to 1992. As far as the priority order of all the family planning activities is concerned, the counseling of the insertion of intrauterine contraceptive device, the use of oral pill or the distribution of condom was placed emphasis on before 1970, and publicity activities of family planning after that time. The first priority order of mother-child health activities has been put on the registration of pregnant women since 1970, with prenatal examination and vaccination ranking next to it. The priority order for activities against tuberculosis was laid on finding out and registration of new T.B. patients every year, with patients' control, and medication or injection ranking next to it. As for the priority order of nursing services, traveling medical examination and treatment ranked the first-stressed activity before 1970, with medication and injection ranking next to it. The first priority order management activity of communicable diseases was put on vaccination before 1970, with medication and injection. ranking next. And consultation and education ranked second to it during 1990 to 1992. As for the health services of the aged, traveling examination and treatment ranked the order, with the assistance of medical examination ranking next to it. As far as troubles and obstacles shown in case of family planning, the rate of residents' lack understanding was 28.8%, that of lacking budget 13.6%, and the imperfection of public health administration system 11.9%. In the case of tuberculosis control, residents' lacking understanding was 32.5%, the deficiency of public health administration system 18.2%, over-duty(shortage of hands) 15.6%, and the insufficiency skill and know-how 13.0%. In the case of nursing services, the deficiency of public health administration system was 18.2%, each over-duty(the shortage of hands) and the shortage of facilities and equipment 15.6% respective, and residents' lacking understanding 13.0%. The rate of dissatisfaction with the chance or possibility of promotion for his or her career or capability was shown to be 49.2%, and 65.9% of the health nurses expressed their complaints of the deficiency of the chance of the promotion to a professional or expert. when the public health nurses were asked in the questionably whether they were satisfied or not with current state of equipment and facilities needed for public health service, 49.6% of them answered in the negative. The grade of the satisfaction with the current individual position was shown to be low as much as the status of his or her position was now. 37.6% of those asked in the research answered to have the readiness to switch jobs for the reasons of dissatisfaction and so on with lacking promotion chance as well as bad working condition. Significant correlation between the grade of job satisfaction and the current status of the po as found to be in this research, which showed that the lower the status of position was, the lower the grade of job satisfaction was. But little correlation between the grade of job satisfaction and his or her schooling and career was found. In order to carry out primary health care successfully, it can be said that more education and publicity activities to make public health nurses and residents see it in a new light are requested. In addition to it, it is suggested that the improvement of promotion system for public health nurses and the enlargement of job province should also be taken in consideration of the high dissatisfaction with and complaints of the chance of promotion and the system of position. In order words, it is important that considerations for system improvement enough to make nursing services pleasant and satisfactory should be taken into.