• Electronics and Telecommunications Trends
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• v.22 no.6
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• pp.150-157
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• 2007

최근 $3{\sim}4$4년 동안 국내외적으로 RFID 기술에 대한 많은 연구와 시범 사업들이 진행되어 왔다. RFID 기술의 응용 중 하나로 RFID 태그를 유가증권에 장착하여 안전하고 신뢰성 있는 유가증권 시스템을 구축하려는 연구가 진행되기도 하였다. 그러나 RFID 기반 유가증권 보호 시스템의 선도적인 개념을 정리한 연구결과는 있었으나 전제 조건이 현재의 기술 수준과 비용 부담을 넘어서기 때문에 실제 적용하기에는 한계가 있다. 본고에서는 RFID 기반 유가증권 보호 시스템의 구성 요소를 중심으로 현재의 기술 개발 동향을 살펴보고, 각 구성 요소의 기술 수준과 보안 요구사항을 고려하여 RFID 기반 유가증권 보호 시스템에 대해 단계적으로 보안 강도를 높여 나갈 수 있는 보안 대책을 제시한다. 본 고의 분석과 제언은 RFID 기반 유가증권 보호 시스템을 상용화하는 데 참조될 수 있을 것이며, 다양한 변형 구조로 발전할 수 있을 것으로 기대된다.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.5
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• pp.458-463
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• 2005

The total asset management system is used for banks or financial institutions for the management of trusteed assets or own assets and it is divided into three systems: the front-office system, the middle-office system and the back-office system by its business areas and functionalities. This kind of asset management system is a huge and complex system handling large data and various financial products, and requires professional knowledges like accounting, financial product specific knowledge, compliance and regulations, etc. It also performs high level computation for NAV calculation and risk measurement on every day Therefore, it needs absolute stability, extendability and efficiency and should handle the frequent change of regulation and products and connectivity with outdoor institutions. In this paper, we report our successful development of such a system and discuss issues regarding its efficient system design and system construction.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.423-426
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• 2008

한국증권선물거래소의 유가증권 매매체결시스템은 안정적이고 신속한 데이터 처리에 초점을 둔 시스템이다. 인터넷과 HTS(Home Trading System)의 대중화로 인해 대량의 데이터로부터 적시에 정보를 추출하고 분석하고자 하는 요구가 증가하고 있다. 그러나 현재의 통계정보시스템은 이와 같은 요구를 수용하기 어려우며 개발자의 별도 노력이 요구된다. 또한 목표성능에 대한 요구가 매우 높아짐에 따라 시스템 및 어플리케이션의 증설과 개선작업이 빈번하지만 그 효과를 예측하기 어려우며 정량화 된 근거자료의 부재로 의사결정을 지연시킨다. 따라서 이와 같은 요구사항들을 해결하기 위해 기존의 통계정보시스템을 활용하고 추가적인 데이터들을 다양한 차원에서 분석 가능하도록 웨어하우스 데이터베이스를 구축하며 성능예측을 위한 요소들을 추출하고 데이터마이닝을 수행하여 의사결정에 도움을 줄 수 있는 다차원 모델링 기반의 거래분석 시스템을 제안한다. 거래분석 시스템의 구축으로 사용자는 웹상에서 적시에 다차원 분석보고서를 생성할 수 있다. 또한 관리자는 외부적 환경변화에 따른 향후 시스템 성능 감소를 예측할 수 있으며 내부적 요인을 제어하여 이를 상쇄할 수 있는 방안을 찾을 수 있게 된다.

• The Korean Journal of Financial Management
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• v.26 no.3
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• pp.103-138
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• 2009

In this article we examine a unique data set of intraday fair disclosure(FD) releases to shed light on market efficiency within the trading day. Specifically, this paper analyze the response of stock prices on fair disclosure disseminated in real-time through KIND(Korea Investor's Network for Disclosure) on Korea stock exchange during the period from January 2003 to September 2004. We find that the prices of stock experiences a statistically and economically significant increase beginning seconds after the fair disclosure is initially announced and lasting approximately two minutes. The stock price responds more strongly to fair disclosure on smaller firm but the response to fair disclosure on the largest firm stock is more gradual, lasting five minutes. We also examine the profitability of a short-term trading strategy based on dissemination of fair disclosure. After controlling for trading costs we find that trader who execute a trade following initial disclosure generate negative profits, but trader buying stock before initial disclosure realize statistically significant positive profit after two minute of disclosure. Summarizing overall results, our evidence supports that security prices on Korea stock exchange reflects all available information within two minutes and the Korea stock market is semi-strongly efficient enough that a trader cannot generate profits based on widely disseminated news unless he acts almost immediately.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.12
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• pp.889-898
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• 2013

Due to the popularization of high-performance capturing equipments and the emergence of powerful image-editing softwares, it is easy to make high-quality counterfeit money. However, the probability of detecting counterfeit money to the general public is extremely low and the detection device is expensive. In this paper, a counterfeit money detection algorithm using a general purpose scanner and computer system is proposed. First, the printing features of color printers are calculated using morphological operations and gray-level co-occurrence matrix. Then, these features are used to train a support vector machine classifier. This trained classifier is applied for identifying either original or counterfeit money. In the experiment, we measured the detection rate between the original and counterfeit money. Also, the printing source was identified. The proposed algorithm was compared with the algorithm using wiener filter to identify color printing source. The accuracy for identifying counterfeit money was 91.92%. The accuracy for identifying the printing source was over 94.5%. The results support that the proposed algorithm performs better than previous researches.

• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.829-844
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• 2013

This study performed comprehensive tests on the four hypotheses on the capital structures for the firms listed in the KOSPI during the period from 2006 to 2011. It may be of concern to find any financial profiles on firms' leverage across the book- and market-value bases since there was relatively little attention drawn to any financial changing profile of the leverage surrounding the period of the pre-and the post-global financial crises. The findings of this study may also be compared with those of the previous related literature, by which it may be expected to enhance the robustness and consistency of the results across the different classifications on capital markets. It was found that three explanatory variables such as PFT, SIZE, and RISK, were found to be the statistically significant attributes on leverage during the tested period. Moreover, the outcome by the Fisher Exact test showed that a firm belonging to each corresponding industry may possess its reversion tendency towards the industry mean and median leverage ratios.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.147-155
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• 2022

In this study, we developed a system to dynamically balance a daily stock portfolio and performed trading simulations using gradient boosting and genetic algorithms. We collected various stock market data from stocks listed on the KOSPI and KOSDAQ markets, including investor-specific transaction data. Subsequently, we indexed the data as a preprocessing step, and used feature engineering to modify and generate variables for training. First, we experimentally compared the performance of three popular gradient boosting algorithms in terms of accuracy, precision, recall, and F1-score, including XGBoost, LightGBM, and CatBoost. Based on the results, in a second experiment, we used a LightGBM model trained on the collected data along with genetic algorithms to predict and select stocks with a high daily probability of profit. We also conducted simulations of trading during the period of the testing data to analyze the performance of the proposed approach compared with the KOSPI and KOSDAQ indices in terms of the CAGR (Compound Annual Growth Rate), MDD (Maximum Draw Down), Sharpe ratio, and volatility. The results showed that the proposed strategies outperformed those employed by the Korean stock market in terms of all performance metrics. Moreover, our proposed LightGBM model with a genetic algorithm exhibited competitive performance in predicting stock price movements.

• Management & Information Systems Review
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• v.35 no.4
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• pp.1-19
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• 2016

The researchers in strategic management have focused on identifying the effects of CEO's demographic characteristics and experience on the strategic choices and performance of firms. On the other hand, they have failed to identifying the effects of CEO's psychological characteristics on them because of the difficulties over data collection and measurement for variables. To overcome this limitation of prior researches, this study is designed to achieve two specific objectives. The first is to examine the effect of CEO narcissism on diversification strategy and performance of listed corporations on Korean securities market in an economic downturn. The other is to examine the moderating effects of various corporate governance systems that are related to board and/or ownership structures on those relationships. The empirical setting for this study was drawn from a multi-year(2011~2014) sample of large listed corporations in Korean securities market. To achieve the objectives, the hypotheses of research are analyzed by implementing multiple regression analyses in two separate models. The results of these analyses show that CEO narcissism is positively related to the diversification of listed large corporations in Korean securities market. Regrading the moderating effects, the stake of institutional investors weakens the positive relationship between CEO narcissism and firm's diversification. The findings of this research imply that CEO narcissism can intensify the tendency of Korean corporations to adopt high-risk and high return strategy in an economic downturn. Thus, firms might be able to use CEO narcissism to drastically restructure the business portfolio in an economic downturn. However, Korean corporations should be very cautions to maximize the positive effect of CEO narcissism. They might be use the institutional investors as their corporate governance system to monitor and control the opportunism of CEO in the decision for diversification in an economic downturn.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.