• Journal of Chest Surgery
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• v.40 no.3 s.272
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• pp.159-167
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• 2007

Background: Vocabularies originating from Chinese characters constitute an important common factor in the medical terminologies used 3 eastern Asian countries; Korea, China and Japan. This study was performed to comparatively analyze the medical terminologies of these 3 countries in the field of cardiopulmonary bypass (CPB) and; thereby, facilitate further understanding among the 3 medical societies. Material and Method: A total of 129 English terms (core 85 and related 44) in the field of CPB were selected and translated into each country's official terminology, with help from Seoul National University Hospital (Korea), Tokyo Michi Memorial Hospital(Japan), and Yanbian Welfare Hospital and Harbin Children Hospital (China). Dictionaries and CPB textbooks were also cited. In addition to the official terminology used in each country, the frequency of use of English terms in a clinical setting was also analyzed. Result and Conclusion: Among the 129 terms, 28 (21.7%) were identical between the 3 countries, as based on the Chinese characters. 86 terms were identical between only two countries, mostly between Korea and Japan. As a result, the identity rate in CPB terminology between Korea and Japan was 86.8%; whereas, between Korea and China and between Japan and China the rates were both 24.8%. The frequency of use of English terms in clinical practices was much higher in Korea and Japan than in China. Despite some inherent limitations involved in the analysis, this study can be a meaningful foundation in facilitating mutual understanding between the medical societies of these 3 eastern Asian countries.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.948-952
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• 2006

인구의 증가와 경제의 발전으로 인해 한정된 수자원에 대한 수요가 급증하였고 향후 고도의 복지사회 구현과 지방 자치화에 따른 각종 용수 수요에 대한 심각한 물 배분 문제가 대두되고 있다. 특히 심각하게 물 배분 문제가 야기될 때 각 수요지점별로 필요한 용수를 공급하기 위해서 단지 상류에서 하류 단으로 물을 배분한다면 수리권의 공정성 문제가 제기되며 물 관리 원칙의 결여에 따른 곤란한 상황이 발생할 수 있다. 이렇게 갈수 및 가뭄 시와 같이 물 배분 문제가 생길시 에는 우선 하천유역 전체의 가용수량을 파악한 후 각 용수 사용별로 중요성을 감안하여 용수공급 우선순위를 설정하여 전 하천 유역을 통하여 일관된 배분을 실시할 수 있는 수자원 최적화 배분 시스템을 개발 할 필요성이 있다. 이러한 문제를 해결하기 위하여 본 연구에서는 한국수자원공사 수자원연구원과 콜로라도주립대학에서 공동 개발한 유역 네트워크 유량 모델(River Basin Network Flow Model)인 K-MODSIM을 이용하여 유역통합 실시간 일 저수지 운영 모델을 개발하였다. 개발된 유역통합 실시간 일운영 모델은 금강유역에 적용하였으며, 금강유역을 12개의 소유역으로 구분하고, 용담댐과 대청댐을 포함하며, 유역관리는 용수공급, 치수대책, 발전수력 및 하천 유지용수의 공급등의 유역내 수자원 관리 상황을 포함하였다. 이처럼 유역의 매우 자세한 세부사항을 고려함으로서 기존에 개발된 모델이 가지고 있던 단순화의 단점을 보완하고 유역의 특성을 최대한 반영하도록 하였다. 또한 유역통합 실시간 일운영 모델의 장기적인 저수지 운영 문제를 해결하기 위해 암시적 추계학적 동적계획법을 사용하여 도출된 월운영룰을 일운영모델에 적용할 수 있는 방법을 제시 하였다.기능으로 구성되어 있으며, 각 기능을 선택하면 해당 화면으로 GUI가 전환된다. 따라서 다량의 측정자료의 신뢰성을 유지하고 이를 모형의 입력자료로 활용하는 일련의 과정을 시스템화하기 때문에 자료의 이상적 유지 관리가 이루어지며 복잡한 2차원 수질해석 모형을 수월하게 운영할 수 있는 시스템으로 개발하였다.제외하면, 부자측정 방법에 의한 유량산정시 가장 큰 오차원인은 홍수시 측정된 유속측선의 위치와 홍수 전후로 측정된 횡단면상의 위치가 일치하지 않는 점과, 대부분 두 측정 구간의 평균값을 대푯값으로 사용한다는 점이다. 본 연구는 다년간의 유량 측정 및 검증 경험과 자료를 토대로 현장에서 부자를 이용하여 측정된 측정성과를 정확도 높은 유량자료로 산정하는데 있어서의 문제점을 도출하고, 이로 인해 발생하는 오차를 추정하여 그 개선방안을 제시해 보고자한다. 더불어 보다 정확한 유량 산정을 위한 기준과 범주를 제시하고자 한다.리적 특성을 잘 반영하며, 도시지역의 복잡한 배수시스템 해석모형과 지표범람 모형을 통합한 모형 개발로 인해 더욱 정교한 도시지역에서의 홍수 범람 해석을 실시할 수 있을 것으로 판단된다. 본 모형의 개발로 침수상황의 시간별 진행과정을 분석함으로써 도시홍수에 대한 침수위험 지점 파악 및 주민대피지도 구축 등에 활용될 수 있을 것으로 판단된다. 있을 것으로 판단되었다.4일간의 기상변화가 자발성 기흉 발생에 영향을 미친다고 추론할 수 있었다. 향후 본 연구에서 추론된 기상변화와 기흉 발생과의 인과관계를 확인하고 좀 더 구체화하기 위한 연구가 필요할 것이다.게 이루어질 수 있을 것으로 기대된다.는 초과수익률이 상승하지만, 이후로는 감소하므로, 반전거래전략을 활용하는 경우 주식투자기간은 24개월이하의 중단기가 적합함을

• Management & Information Systems Review
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• v.33 no.1
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• pp.45-63
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• 2014

A trading system is a computer trading program that automatically submits trades to an exchange. Mechanical a trading system to execute trade is spreading in the stock market. However, a trading system to trade a single asset might occur instability of the profit because payoff of this system is determined a asset movement. Therefore, it is necessary to develop a trading system that is trade two assets such as a pair trading that is to sell overvalued assets and buy the undervalued ones. The aim of this study is to propose a relative value based trading system designed to yield stable and profitable profits regardless of market conditions. In fact, we propose a procedure for building a trading system that is based on the rough set analysis of indicators derived from a price ratio between two assets. KOSPI 200 index futures and S&P 500 index futures are used as a data for evaluation of the proposed trading system. We intend to examine the usefulness of this model through an empirical study.

• Journal of Ginseng Culture
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• v.3
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• pp.90-118
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• 2021

Gaeseong fostered the conditions necessary for modern capitalism, as huge capital was accumulated through the cultivation and trade of ginseng, which were activities that flourished in the 18th century. During the Japanese colonial era, ginseng merchants were not simply limited to acquiring landowner capital from ginseng trade but actively converted such resource to productive and financial capital, thereby becoming modern entrepreneurs. Ginseng merchants led the joint management and investment of Gaeseong Electric Co., Ltd., Daehan Cheonil Bank, Gaeseong Brewing Co., Ltd., and Songgo Textile Company, founded in the early 20th century. They pursued corporate profits and, as leading individuals of society, spearheaded regional development by supporting educational and cultural projects in Gaeseong. These projects included the establishment of the Gaeseong Commercial School, the publication of Goryeo Times, and the operation of the Gaeseong Jwa Theater. Although liberal economics prioritized shareholder interest, the 21st century witnessed an emphasis on social responsibility among stakeholders asthe major purpose of enterprises. A trend that emerged was ESG (environment, social, governance) management, in which non-financial factors are valued more highly than financial performance. A successful business, which was denoted only by high profits in the past, is now defined by whether a company fulfills its social responsibility. In the early 20th century, the corporate activities of ginseng merchants in Gaeseong reflected entrepreneurship and stakeholder-centered ESG management, which later emerged as essential elements of modern business management. The modern management philosophy ahead of its times stemmed from the regionality of Gaeseong. The political discrimination against Gaeseong residents in the Joseon Dynasty precluded them from becoming government officers, and under a strict social hierarchy, yangban ("noblemen"), the intellectuals of the Joseon Dynasty, were forced to serve as merchants. Son Bong-sang and Kong Seong-hak, aside from being representative ginseng merchants, were both Confucian scholars and writers. The second and third generations of ginseng merchant families who had received higher education abroad returned to Gaeseong to carry on with their family businesses, then established modern companies with capital accrued from the ginseng industry. An analysis of the commercial activities of ginseng merchants in the early 20th century confirmed that these individuals were pioneering entrepreneurs who adopted the ESG management philosophy. In ginseng merchants, one sees a dimension of capitalism with a human face, as with ginseng thatsaves human life.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.237-262
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• 2022

There has been a growing interest in IPOs (Initial Public Offerings) due to the profitable returns that IPO stocks can offer to investors. However, IPOs can be speculative investments that may involve substantial risk as well because shares tend to be volatile, and the supply of IPO shares is often highly limited. Therefore, it is crucially important that IPO investors are well informed of the issuing firms and the market before deciding whether to invest or not. Unlike institutional investors, individual investors are at a disadvantage since there are few opportunities for individuals to obtain information on the IPOs. In this regard, the purpose of this study is to provide individual investors with the information they may consider when making an IPO investment decision. This study presents a model that uses machine learning and text analysis to predict whether an IPO stock price would move up or down after the first 5 trading days. Our sample includes 691 Korean IPOs from June 2009 to December 2020. The input variables for the prediction are three tone variables created from IPO prospectuses and quantitative variables that are either firm-specific, issue-specific, or market-specific. The three prospectus tone variables indicate the percentage of positive, neutral, and negative sentences in a prospectus, respectively. We considered only the sentences in the Risk Factors section of a prospectus for the tone analysis in this study. All sentences were classified into 'positive', 'neutral', and 'negative' via text analysis using TF-IDF (Term Frequency - Inverse Document Frequency). Measuring the tone of each sentence was conducted by machine learning instead of a lexicon-based approach due to the lack of sentiment dictionaries suitable for Korean text analysis in the context of finance. For this reason, the training set was created by randomly selecting 10% of the sentences from each prospectus, and the sentence classification task on the training set was performed after reading each sentence in person. Then, based on the training set, a Support Vector Machine model was utilized to predict the tone of sentences in the test set. Finally, the machine learning model calculated the percentages of positive, neutral, and negative sentences in each prospectus. To predict the price movement of an IPO stock, four different machine learning techniques were applied: Logistic Regression, Random Forest, Support Vector Machine, and Artificial Neural Network. According to the results, models that use quantitative variables using technical analysis and prospectus tone variables together show higher accuracy than models that use only quantitative variables. More specifically, the prediction accuracy was improved by 1.45% points in the Random Forest model, 4.34% points in the Artificial Neural Network model, and 5.07% points in the Support Vector Machine model. After testing the performance of these machine learning techniques, the Artificial Neural Network model using both quantitative variables and prospectus tone variables was the model with the highest prediction accuracy rate, which was 61.59%. The results indicate that the tone of a prospectus is a significant factor in predicting the price movement of an IPO stock. In addition, the McNemar test was used to verify the statistically significant difference between the models. The model using only quantitative variables and the model using both the quantitative variables and the prospectus tone variables were compared, and it was confirmed that the predictive performance improved significantly at a 1% significance level.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.63-83
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• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.

• Journal of Korean Society for Geospatial Information Science
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• v.8 no.1 s.15
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• pp.51-63
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• 2000

Dynamic traffic assignment(DTA) has been a topic of substantial research during the past decade. While DTA is gradually maturing, many aspects of DTA still need improvement, especially regarding its formulation and solution algerian Recently, with its promise for In(Intelligent Transportation System) and GIS(Geographic Information System) applications, DTA have received increasing attention. This potential also implies higher requirement for DTA modeling, especially regarding its solution efficiency for real-time implementation. But DTA have many mathematical difficulties in searching process due to the complexity of spatial and temporal variables. Although many solution algorithms have been studied, conventional methods cannot iud the solution in case that objective function or constraints is not convex. In this paper, the genetic algorithm to find the solution of DTA is applied and the Merchant-Nemhauser model is used as DTA model because it has a nonconvex constraint set. To handle the nonconvex constraint set the GENOCOP III system which is a kind of the genetic algorithm is used in this study. Results for the sample network have been compared with the results of conventional method.