• 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 Environmental Impact Assessment
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• v.28 no.3
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• pp.183-200
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• 2019

In this study, the characteristics of seasonal $PM_{2.5}$ behavior in South Korea and other Northeast Asian regions were analyzed by using the $PM_{2.5}$ ground measurement data, weather data, WRF and CMAQ models. Analysis of seasonal $PM_{2.5}$ behavior in Northeast Asia showed that $PM_{2.5}$ concentration at 6 IMS sites in South Korea was increased by long-distance transport and atmospheric congestion, or decreased by clean air inflow due to seasonal weather characteristics. As a result of analysis by applying BFM to air quality model, the contribution from foreign countries dominantly influenced the $PM_{2.5}$ concentrations of Baengnyeongdo due to the low self-emission and geographical location. In the case of urban areas with high self-emissions such as Seoul and Ulsan, the $PM_{2.5}$ contribution from overseas was relatively low compared to other regions, but the standard deviation of the season was relatively high. This study is expected to improve the understanding of the air pollutant phenomenon by analyzing the characteristics of $PM_{2.5}$ behavior in Northeast Asia according to the seasonal weather condition change. At the same time, this study can be used to establish the air quality policy in the future, knowing that the contribution of $PM_{2.5}$ concentration to the domestic and overseas can be different depending on the regional emission characteristics.

• Korean Journal of Psychosomatic Medicine
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• v.10 no.2
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• pp.110-119
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• 2002

Objectives : Numbers of patients who have chronic pain seem to be increasing in the psychiatric practice. Many investigators have used models of stress and coping to help explain the differences in adjustment found among persons who experience chronic pain. Coping strategies appear to be associated with adjustment in chronic pain patients. The objectives of this study were to develop a self-report questionnaire which is the most widely used measures of pain coping strategies, Coping Strategies Questionnaire (CSQ) into Korean version and to study the different coping strategies with which chronic pain patients frequently use when their pain reaches a moderate or greater level of intensity. Methods : One hundred twenty-eight individuals with chronic pain conditions and two hundred fifty-two normal controls were administered the Korean version-Coping Strategies Questionnaire(KCSQ) to assess the frequency of use and perceived effectiveness of a variety of cognitive and behavioral pain coping strategies. We also obtained their clinical features in chronic pain patients. Reliability of the questionnaire were analyzed and evaluated differences of coping strategies between two groups. Results : Data analysis revealed that the questionnaire was internally reliable. Chronic pain patients reported frequent use of a variety of pain coping strategies, such as coping self-statements, praying and hoping, catastrophizing, and increase behavior scales which were higher compared to the normal controls. Conclusion: K-CSQ revealed to be a reliable self-report questionnaire which is useful for the assessment of coping strategies in clinical setting on chronic pain. And analysis of pain coping strategies may be helpful in understanding pain for chronic pain patients. The individual K-CSQ may have greater utility in terms of examining coping, appraisals, and pain adjustment. A consideration of pain coping strategies may allow one to design pain coping skills training interventions so as to fit the individual chronic pain patient. Further research is needed to determine whether cognitive-behavioral intervention designed to decrease maladaptive coping strategies can reduce pain and improve the physical and psycho-social functioning of chronic patients.