• Korean Journal of Psychosomatic Medicine
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• v.17 no.1
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• pp.8-14
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• 2009

Objectives : The purpose of this study was to investigate the various aspects of stress in patients with chronic prostatitis. Methods : Thirty two chronic prostatitis patients meeting the criteria of NIH-category III were compared with sixty four controls. Data pertaining to the source of, response to, and coping with stress, as well as chronic prostatitis symptom index were collected using a self-report questionnaire. The questionnaire consisted of a Global Assessment of Recent Stress(GARS), Stress Response Inventory(SRI), Ways of Coping Checklist(WCC), in addition to the NIH-CPSI. Results : From the results of GARS subscales, the scores of changes in relationship, sickness or injury and financial were significantly higher in patients with chronic prostatitis than normal controls. Chronic prostatitis patients had greater amplification of somatization, depression and anger in SRI and significantly lower score in ways of coping checklist compared with controls. In the NIH-CPSI of chronic prostatitis patients, the degree of symptom played a role in depression as stress response item and significant negative correlationship between the symptom point and problem focused, seeking social support coping strategy was observed. Conclusion : These results show that patients with chronic prostatitis have greater stress, higher stress response and insufficient coping strategy. Therefore, these efforts should be considered that enhancing healthier coping strategy and evaluation and management of stress experienced by chronic protatitis patients

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.8
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• pp.215-225
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• 2016

This study seeks to find an alternative to the requirement for proving the competitiveness of Korean performing arts before entering into the overseas market by deriving the factors causing entry into the market to be delayed based on an analysis of the current status. Between 2007 to 2014, the overseas revenues from Korean performing arts increased by 16.4% compared to the total amount of financial support, as the number of free performances given overseas and the average guaranteed number of performances overseas are both at a standstill. Also, the size of the audience increased by a mere 3.3 times, which is an even lower growth rate than that for the number of performances, 3.8 times, during the same period. Furthermore, the audience size per unit is suffering from long-term stagnation. The main causes are as follows: 1) applying one-dimensional methods to performing arts exchanges and expanding the overseas market, 2) the existing confused concepts between profit and non-profit contents, 3) the weaknesses of the market expansion strategy, because of programming practices focusing on providers, such as presenters and producers, rather than consumers. As a result, the necessary basic research, including consumer surveys, has not been done yet. In order to understand the implications of this analysis and solve the problem of the delayed overseas expansion of Korean performing arts, the Korean wave industry was examined as a representative example. Consumer surveys for the performing arts, possibly benchmarked to the "Korean Wave Consumer Survey Index (KWCSI)", are expected to be done in the near future. In addition, through the development of a specific consumer index of the performing arts, customized marketing strategies by continent and country need to be established. This empirical study of the overseas expansion of performing arts can be utilized as a bridge between the academic and real worlds. This work may also enable a variety of strategies to be established for the overseas expansion of the performing arts.

• Korean Journal of Agricultural Science
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• v.26 no.2
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• pp.77-93
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• 1999

This study is aiming at identifying the foreward and backward lingkage effects of the farm land base development project. Korean Government has continuously carried out farmland base development projets including the integrated agricultural development projects. large and medium scale irrigation projects and the comprehensive development of the four big river basin including tidal land reclamation and estuary dam construction for the all weather farming since 1962. the starting year of the five year economic development plans. Consequently the irrigation rate of paddy fields in Korea reached to 75% in 1998 and to escalate the irrigation rate, the Government had procured heavy investment fund from IBRD. IMF and OECF etc. To cope with the agricultural problems like trade liberalization in accordance with WTO policy, the government has tried to solve such problems as new farmland base development policy, preservation of the farmland and expansion of farmland to meet self-sufficiency of foods in the future. Especially, farmland base development projects have been challanged to environmental and ecological problems in evaluating economic benefits and costs where the value of non-market goods have not been included in those. Up to data, in evaluating benefits and costs of the projects, farmland base development projects have been confined to direct incremental value of farm products and it's related costs. Therefore the projects'efficiency as a decision making criteria has shown the low level of economic efficiencies. In estimating economic efficiencies including Leontiefs input-output analysis of the projects could not be founded in Korea at present. Accordingly this study is aimed at achieving and identifying the following objectives. (1) To identify the problems related to the financial supports of the Government in implementing the proposed projects. (2) To estimated backward and foreward linkage effects of the proposed project from the view point of national economy as a whole. To achieve the objectives, Hwangrak benefited area with reservoir which is located in Seosan-haemi Disticts, Chungnam Province were selected as a case study. The main results of the study are summarized as follows : a. The present value of investment and O & M cost were amounted to 3,510million won and the present value of the value added in related industries was estimated at 5.913million won for the period of economic life of 70 years. b. The total discounted value of farm products in the concerned industries derived by the project was estimated at 10,495million won and the foreward and backward linkage effects of the project were amounted to 6,760 and 5,126million won respectively. c. The total number of employment opportunities derived from the related industries for the period of project life were 3,136 man/year. d. Farmland base development projects were showed that the backward linkage effects estimated by index of the sensitivity dispersion were larger than the forward linkage effect estimated by index of the power of dispersion. On the other hand, the forward linkage effect of rice production value during project life was larger than the backward linkage effect e. The rate of creation of new job opportunity by means of implementing civil engineering works were shown high in itself rather than any other fields. and the linkage effects of production of the project investment were mainly derived from the metal and non-metal fields. f. According to the industrial linkage effect analysis, farmland base development projects were identified economically feasible from the view point of national economy as a whole even though the economic efficiencies of the project was outstandingly decreased owing to delaying construction period and increasing project costs.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.195-220
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• 2018

In this study, we conducted an empirical analysis of the factors that affect the change of Bitcoin Closing Price. Previous studies have focused on the security of the block chain system, the economic ripple effects caused by the cryptocurrency, legal implications and the acceptance to consumer about cryptocurrency. In various area, cryptocurrency was studied and many researcher and people including government, regardless of country, try to utilize cryptocurrency and applicate to its technology. Despite of rapid and dramatic change of cryptocurrencies' price and growth of its effects, empirical study of the factors affecting the price change of cryptocurrency was lack. There were only a few limited studies, business reports and short working paper. Therefore, it is necessary to determine what factors effect on the change of closing Bitcoin price. For analysis, hypotheses were constructed from three dimensions of consumer, industry, and macroeconomics for analysis, and time series data were collected for variables of each dimension. Consumer variables consist of search traffic of Bitcoin, search traffic of bitcoin ban, search traffic of ransomware and search traffic of war. Industry variables were composed GPU vendors' stock price and memory vendors' stock price. Macro-economy variables were contemplated such as U.S. dollar index futures, FOMC policy interest rates, WTI crude oil price. Using above variables, we did times series regression analysis to find relationship between those variables and change of Bitcoin Closing Price. Before the regression analysis to confirm the relationship between change of Bitcoin Closing Price and the other variables, we performed the Unit-root test to verifying the stationary of time series data to avoid spurious regression. Then, using a stationary data, we did the regression analysis. As a result of the analysis, we found that the change of Bitcoin Closing Price has negative effects with search traffic of 'Bitcoin Ban' and US dollar index futures, while change of GPU vendors' stock price and change of WTI crude oil price showed positive effects. In case of 'Bitcoin Ban', it is directly determining the maintenance or abolition of Bitcoin trade, that's why consumer reacted sensitively and effected on change of Bitcoin Closing Price. GPU is raw material of Bitcoin mining. Generally, increasing of companies' stock price means the growth of the sales of those companies' products and services. GPU's demands increases are indirectly reflected to the GPU vendors' stock price. Making an interpretation, a rise in prices of GPU has put a crimp on the mining of Bitcoin. Consequently, GPU vendors' stock price effects on change of Bitcoin Closing Price. And we confirmed U.S. dollar index futures moved in the opposite direction with change of Bitcoin Closing Price. It moved like Gold. Gold was considered as a safe asset to consumers and it means consumer think that Bitcoin is a safe asset. On the other hand, WTI oil price went Bitcoin Closing Price's way. It implies that Bitcoin are regarded to investment asset like raw materials market's product. The variables that were not significant in the analysis were search traffic of bitcoin, search traffic of ransomware, search traffic of war, memory vendor's stock price, FOMC policy interest rates. In search traffic of bitcoin, we judged that interest in Bitcoin did not lead to purchase of Bitcoin. It means search traffic of Bitcoin didn't reflect all of Bitcoin's demand. So, it implies there are some factors that regulate and mediate the Bitcoin purchase. In search traffic of ransomware, it is hard to say concern of ransomware determined the whole Bitcoin demand. Because only a few people damaged by ransomware and the percentage of hackers requiring Bitcoins was low. Also, its information security problem is events not continuous issues. Search traffic of war was not significant. Like stock market, generally it has negative in relation to war, but exceptional case like Gulf war, it moves stakeholders' profits and environment. We think that this is the same case. In memory vendor stock price, this is because memory vendors' flagship products were not VRAM which is essential for Bitcoin supply. In FOMC policy interest rates, when the interest rate is low, the surplus capital is invested in securities such as stocks. But Bitcoin' price fluctuation was large so it is not recognized as an attractive commodity to the consumers. In addition, unlike the stock market, Bitcoin doesn't have any safety policy such as Circuit breakers and Sidecar. Through this study, we verified what factors effect on change of Bitcoin Closing Price, and interpreted why such change happened. In addition, establishing the characteristics of Bitcoin as a safe asset and investment asset, we provide a guide how consumer, financial institution and government organization approach to the cryptocurrency. Moreover, corroborating the factors affecting change of Bitcoin Closing Price, researcher will get some clue and qualification which factors have to be considered in hereafter cryptocurrency study.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.

• 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 Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.233-253
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• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.

• Management & Information Systems Review
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• v.36 no.4
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• pp.21-32
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• 2017

Motivated by the recent cases of negligent social responsibility as manifested by foreign luxury fashion brands in Korea, this study investigates whether agency costs depend on the sustainability of different types of corporate governance. Agency costs refer either to vertical costs arising from the relationship between stockholders and managers, or to horizontal costs associated with the potential conflicts between majority and minority stockholders. The firms with luxury fashion brand could spend large sums of money on maintenance of magnificent brand image, thereby increasing the agency cost. On the contrary, the firms may hold down wasteful spending to report a gaudily financial achievement. This results in mitigation of the agency cost. Agency costs are measured by the value of the principal component. First, three ratios are constructed: asset turnover, operating expense to sales, and earnings before interest, tax, and depreciation. Then, the scores of each of these ratios for individual firms in the sample are differenced from the ratios for the benchmark firm of S-OIL. S-OIL was designated as the best superior governance model firm for 2013 by CGS. We perform regression analysis of each agency cost index, luxury fashion brand dummy and a set of control variables. The regression results indicate that the agency costs of the firms with luxury fashion brand exceed those of control group in the fashion industry in the part of operating expenses, but the agency cost falls short of those of control group in the part of EBITD, thus the aggregate agency costs are not differential of those of the control group. In sensitivity test, the results are same that the agency cost of the firms are higher than those of the matching control group with PSM(propensity matching method). These results are corroborated by an additional analysis comparing the group of the companies with the best brands with the control group. The results raise doubts about the effectiveness of management of the firms with luxury fashion brand. This study has a limitation that the research has performed only for 2013 and this paper suggests that there is room for improvement in the current research methodology.

• The Korean Journal of Elementary Counseling
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• v.5 no.1
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• pp.227-253
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• 2006

It seems common for students living at a small county and islands to experience psychological conflicts and be unaccustomed in the peer society because they are not familiar with peer interaction and social skills. This is a case study of L (hereinafter called L) who was grown up in the sheltered school at a small county. L was psychologically disturbed because he couldn't get along well in the transferred school. It is the reason why he had lived in the sheltered school at a small county, so he had not enough exposure to interact with peer and social skills. Sometimes he was obstinate irrationally and when he had trouble with friends, he threw something out or went out of school and tricked juniors dangerously. The fact of disperse with families, parent's indifference, and hate of older brother made L to have ill feeling against family. He had low motivation and low self confident in learning because of short attention time and accumulated poor learning progress. In this study, he was evaluated at various area, such as, intelligent, affective, personal and inter-personal, before counselling. To evaluated the effect of the counselling, K-WISC-III, KPRC, sentence filling test, social adaptation ability test, etc, were administered right after the counselling was over and 8 weeks later. For specific information gathering and analysing, observation diary and deepen counselling were accomplished by homeroom teacher, his mother, and his peers. To correct his problematic behaviors, 13 counseling sessions were accomplished for 6 months and those counselling sessions were recorded and analysed definitely. Followings are the result of this case study. First, he was recovered from the anxiety of inter-personal interaction and he started to interact with peers. The result of sac scale score of KPRC profile was lower than before as much as average student after counseling and 8 weeks later. This reveals that the distress against interpersonal relation have settled. Especially, through the result of sentence filing test, he seemed to feel attachment to peers and be positive, active in the relation of peer. For instance, he was active in the open class lesson and interacted well with peers. It could be said that he overcame the psychological distress comparing with previous time. Second, he could apologize to his peer and juniors for his fault. His attitude were well shown in the letter from an old friend at the sheltered school, average KPRC profiling score comparing with previous counseling time, and remarkable decrease of attack scale score of teacher and peer. Third, his view toward family turn out positive. He recognized his situation that he lived apart from family and even worried about his parent's financial difficulty. Through solving the confliction with his older brother, he could acquire the feeling of family reunion. Fourth, his learning motivation and self-confidence were increased. He confirmed his future positively and he might be judged more attentive because his intelligence index was higher than before as much as average student. With the main goal of this study, verification for effectiveness of counseling. understanding and helping problematic students such as L who lives at a small county and island through investigation of their real situation and problems with the method of counseling and socio-cultural analysis is worthwhile. Identification of ideal relationship with peer is related with positive self-conception, harmonic social adaptation and development of child. It is time to investigate easy adaptive in classroom and well-organised program to acquire general social skills for sheltered school students at a small county and islands.