• 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 Korean Society of Coastal and Ocean Engineers
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• v.25 no.5
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• pp.335-347
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• 2013

In February 2008, high storm waves due to a developed atmospheric low pressure system propagating from the west off Hokkaido, Japan, to the south and southwest throughout the East Sea (ES) caused extensive damages along the central coast of Japan and along the east coast of Korea. This study consists of two parts. In the first part, we estimate extreme storm wave characteristics in the Toyama Bay where heavy coastal damages occurred, using a non-hydrostatic meteorological model and a spectral wave model by considering the extreme conditions for two factors for wind wave growth, such as wind intensity and duration. The estimated extreme significant wave height and corresponding wave period were 6.78 m and 18.28 sec, respectively, at the Fushiki Toyama. In the second part, we perform numerical experiments on wave-structure interaction in the Fushiki Port, Toyama Bay, where the long North-Breakwater was heavily damaged by the storm waves in February 2008. The experiments are conducted using a non-linear shallow-water equation model with adaptive mesh refinement (AMR) and wet-dry scheme. The estimated extreme storm waves of 6.78 m and 18.28 sec are used for incident wave profile. The results show that the Fushiki Port would be overtopped and flooded by extreme storm waves if the North-Breakwater does not function properly after being damaged. Also the storm waves would overtop seawalls and sidewalls of the Manyou Pier behind the North-Breakwater. The results also depict that refined meshes by AMR method with wet-dry scheme applied capture the coastline and coastal structure well while keeping the computational load efficiently.

• Journal of the Korean Society of Food Science and Nutrition
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• v.26 no.4
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• pp.704-713
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• 1997

This study was conducted to describe qualitatively the protein metabolism of pigs during growth depending on the feed protein quality and to describe quantitatively amino acids requirements, using a simulation model. The used model has a non-linear structure. In the used model, the protein utilization system of a pig, which is in the non-steady-state, is described with 15 flux equations and 11 differential equations and is composed with two compartments. Protein deposition(g/day) of pigs on the 30th, 60th, 90th, and 120th day of feeding duration with three-quality protein, beginning with body weight 20kg, were calculated according to the empirical model, PAF(the product of amino acid functions) of Menke, and was used as object function for the simulation. The mean of relative difference between the simulated protein deposition and PAF calculated values, lied in a range of 8.8%. The simulated protein deposition showed different behavior according to feed protein quality. In the high-quality protein, it showed paraboloidal form with extending growth simulation up to 150eh day. So the maximum of protein deposition was acquired on the 105th day of simulate growth time and then it decreased fast. In the low-quality protein, this form of protein deposition in the course of simulated growth did not appear until 150th day. The simulated protein mass also showed a difference in accordance with feed protein quality. The difference was small on the 30th day of simulated growth, but with duration of the simulated growth it was larger. On the 150th day the simulated protein deposition of high quality protein was 1.5 times higher as compared to the low-quality protein. The simulated protein synthesis and break-down rates(g/day) in the whole body showed a parallel behavior in the course of growth, according to feed protein quality. It was found that the improvement of feed protein quality increased protein deposition in the whole body through a increase of both protein synthesis and breakdown during growth. Also protein deposition efficiency, which was calculated from simulated protein deposition and protein synthesis, showed a difference in dependence on the protein qualify of feed protein. The protein deposition efficiency was higher in pigs fed with high quality protein, especially at the simulation time 30th day. But this phenomena disappeared with growth, so on the 150th day of growth, the protein deposition of the high feed protein quality was lowest among the three different quality of feed protein. The simulated total requirement of the 10 essential amino acids for the growth of pigs was 28.1(g/100g protein), similar to NRC. The requirement of lysine was 4.2(g/100g protein).

• Nuclear Medicine and Molecular Imaging
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• v.40 no.4
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• pp.218-227
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• 2006

Purpose: The HSV1-tk reporter gene system is the most widely used system because of its advantage that direct monitoring is possible without the introduction of a separate reporter gene in case of HSV1-tk suicide gene therapy. In this study, we investigate the usefulness of the reporter probe (substrate), $9-(4-[^{18}F]Fluoro-3-hydroxymethylbutyl)$guanine ($[^{18}F]FHBG$) for non-invasive reporter gene imaging using PET in HSV1-tk expressing hepatoma model. Materials and Methods: Radiolabeled FHBG was prepared in 8 steps from a commercially available triester. The labeling reaction was carried out by NCA nucleophilic substitution with $K[^{18}F]/K2.2.2.$ in acetonitrile using N2-monomethoxytrityl-9-14-(tosyl)-3-monomethoxytritylmethylbutyl]guanine as a precursor, followed by deprotection with 1 N HCl. Preliminary biological properties of the probe were evaluated with MCA cells and MCA-tk cells transduced with HSV1-tk reporter gene. In vitro uptake and release-out studies of $[^{18}F]FHBG$ were performed, and was analyzed correlation between $[^{18}F]FHBG$ uptake ratio according to increasing numeric count of MCA-tk cells and degree of gene expression. MicroPET scan image was obtained with MCA and MCA-tk tumor bearing Balb/c-nude mouse model. Results: $[^{18}F]FHBG$ was purified by reverse phase semi-HPLC system and collected at around 16-18 min. Radiothemical yield was about 20-25%) (corrected for decay), radiochemical purity was >95% and specific activity was around >55.5 $GBq/{\mu}\;mol$. Specific accumulation of $[^{18}F]FHBG$ was observed in HSV1-tk gene transduced MCA-tk cells but not in MCA cells, and consecutive 1 hour release-out results showed more than 86% of uptaked $[^{18}F]FHBG$ was retained inside of cells. The uptake of $[^{18}F]FHBG$ was showed a highly significant linear correlation ($R^2=0.995$) with increasing percentage of MCA-tk numeric cell count. In microPET scan images, remarkable difference of accumulation was observed for the two type of tumors. Conclusion: $[^{18}F]FHBG$ appears to be a useful as non-invasive PET imaging substrate in HSV1-tk expressing hepatoma model.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.

• Journal of the Korean Geotechnical Society
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• v.26 no.7
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• pp.49-58
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• 2010

This paper presents experimental results of a series of 1-g shaking table model tests performed on end-bearing single piles and pile groups to investigate the effect of particle size on the dynamic behavior of soil-pile systems. Two soil-pile models were tested twice: first using Jumoonjin sand, and second using Australian Fine sand. In the case of single-pile models, the lateral displacement was almost within 1% of pile diameter which corresponds to the elastic range of the pile. The back-calculated p-y curves show that the subgrade reaction of the Jumoonjin-sand-model ground was larger than that of the Australian Fine-sand-model ground at the same displacement. This phenomenon means that the stress-strain behavior of Jumoonjin sand was initially stiffer than that of Australian Fine sand. This difference was also confirmed by resonant column tests and compression triaxial tests. And the single pile p-y backbone curves of the Australian fine sand were constructed and compared with those of the Jumoonjin sand. As a result, the stiffness of the p-y backbone curves of Jumunjin sand was larger than those of Australian fine sand. Therefore, using the same p-y curves regardless of particle size can lead to inaccurate results when evaluating dynamic behavior of soil-pile system. In the case of the group-pile models, the lateral displacement was much larger than the elastic range of pile movement at the same test conditions in the single-pile models. The back-calculated p-y curves in the case of group pile models were very similar in both sands because the stiffness difference between the Jumoonjin-sand-model ground and the Australian Fine-sand-model ground was not significantly large at a large strain level, where both sands showed non-linear behavior. According to a series of single pile and group pile test results, the evaluation group pile effect using the p-multiplier can lead to inaccurate results on dynamic behavior of soil-pile system.

• Korean Journal of Psychosomatic Medicine
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• v.31 no.2
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• pp.108-117
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• 2023

Objectives : Somatic symptom disorder (SSD) is characterized by the manifestation of a variety of physical symptoms, but little is known about differences in autonomic nervous system activity according to symptom severity, especially within patient groups. In this study, we examined differences in heart rate variability (HRV) across symptom severity in a group of SSD patients to analyze a representative marker of autonomic nervous system changes by symptoms severity. Methods : Medical records were retrospectively reviewed for patients who were diagnosed with SSD based on DSM-5 from September 18, 2020 to October 29, 2021. We applied inverse probability of treatment weighting (IPTW) methods to generate more homogeneous comparisons in HRV parameters by correcting for selection biases due to sociodemographic and clinical characteristic differences between groups. Results : There were statistically significant correlations between the somatic symptom severity and LF (nu), HF (nu), LF/HF, as well as SD1/SD2 and Alpha1/Alpha2. After IPTW estimation, the mild to moderate group was corrected to 27 (53.0%) and the severe group to 24 (47.0%), and homogeneity was achieved as the differences in demographic and clinical characteristics were not significant. The analysis of inverse probability weighted regression adjustment model showed that the severe group was associated with significantly lower RMSSD (β=-0.70, p=0.003) and pNN20 (β=-1.04, p=0.019) in the time domain and higher LF (nu) (β=0.29, p<0.001), lower HF (nu) (β=-0.29, p<0.001), higher LF/HF (β=1.41, p=0.001), and in the nonlinear domain, significant differences were tested for SampEn15 (β=-0.35, p=0.014), SD1/SD2 (β=-0.68, p<0.001), and Alpha1/Alpha2 (ß=0.43, p=0.001). Conclusions : These results suggest that differences in HRV parameters by SSD severity were showed in the time, frequency and nonlinear domains, specific parameters demonstrating significantly higher sympathetic nerve activity and reduced ability of the parasympathetic nervous system in SSD patients with severe symptoms.

• Korean Journal of Ecology and Environment
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• v.43 no.1
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• pp.82-90
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• 2010

The ecological characteristics of the Korean Aucha perch, Coreoperca herzi, were determined in order to estimate stock of the mid-upper system of the Seomjin River. The age was determined by counting the otolith annuli. The oldest fish observed in this study was 5 years old. Relationships between body length (BL) and body weight (BW) were $BW=0.0195BL^{3.08}$ ($R^2=0.966$) (p<0.01). Relationships between the otolith radius (R) and body length (BL) were BL=3.882R+1.66 ($R^2=0.944$). The von Bertalanffy growth parameters estimated from a non-linear regression method were $L_{\infty}=19.68\;cm$, $W_{\infty}=188.64\;g$, $K=0.17\;year^{-1}$ and $t_0=-1.46$ year. Therefore, growth in length of the fish was expressed by the von Bertalanffy's growth equation as $L_t=19.68$ ($1-e^{-0.17(t+1.46)}$) ($R^2=0.997$). The annual survival rate (S) was estimated to be $0.666\;year^{-1}$. The instantaneous coefficient of natural mortality (M) of estimated from the Zhang and Megrey method was $0.346\;year^{-1}$, and instantaneous coefficient of fishing mortality (F) was calculated $0.061\;year^{-1}$. From the estimates of survival rate (S), the instantaneous coefficient of total mortality(Z) was estimated to be $0.407\;year^{-1}$.

• Korean Journal of Environment and Ecology
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• v.25 no.3
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• pp.318-326
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• 2011

The population ecological characteristics of the Crucian carp, Carassius auratus, were determined in order to estimate stock of the mid-upper system of the Seomjin River. The fish ranged in size from 95 to 288mm total length. The age was determined by counting the scale annulus. The scales displayed clear annulus that were used to estimate the age. The oldest fish observed in this study was 5 years old. Age-2 fishes were the most numerous in the sample(n=38), followed in frequency be age-3(n=22). Marginal index analysis validated the formation of a single annulus per year. The relationship between body length and body weight was BW = $0.0038BL^{3.73}$($R^2$=0.96) (p<0.01). The relationship between the scale radius and body length was BL = 2.362R+2.76($R^2$=0.89). The von Bertalanffy growth parameters estimated from a non-linear regression method were $L_{\infty}$=33.2 cm, $W_{\infty}$=1,798.4 g, $K=0.20year^{-1}$ and $t_0$=-0.51year. Therefore, Growth in length of the fish was expressed by the von Bertalanffy's growth equation as $L_t=33.23$($1-e^{-0.20(t+0.51)}$)($R^2$=0.98). The annual survival rate was estimated to be 0.427year$^{-1}$. The instantaneous coefficient of natural mortality of estimated from the Zhang and Megrey method was $0.784year^{-1}$, and instantaneous coefficient of fishing mortality was calculated $0.067year^{-1}$. From the estimates of survival rate, the instantaneous coefficient of total mortality was estimated to be $0.851year^{-1}$.

• 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.