• The Journal of the Korea Contents Association
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• v.12 no.4
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• pp.401-406
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• 2012

Shanghai Stock Exchange is the largest stock exchange of emerging markets that there were listed firms 905, listed securities 1,537, listed stocks 949, total number of listed stocks 2 trillion 2000 billion shares. There is more development that is expected to occur in the future. The purpose of this study is to find determinants of capital structure to listed manufacturing firms in Shanghai Stock Exchange using multiple regression. Conclusions of this study are summarized as follows. First, firm size is positively related to debt ratio significantly at 1% significance level.. Second, the profitability is negatively related to debt ratio significantly at 1% significance level. Third, the growth ability is positively related to debt ratio significantly at 1% significance level. fourth, cash flow, the largest shares ownership, negotiable shares ratio are negatively related to debt ratio but they are not significant statistically. The result of this study provides information for investors and can be utilized to improvement of financial structure.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2006.11a
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• pp.55-58
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• 2006

차세대 방송서비스 개발의 일환으로 관심을 모으고 있는 다시점 비디오 부호화(multi-view video coding, MVC) 방식은 인접한 여러 대의 카메라로 동시에 획득한 영상을 효과적으로 압축하는데 그 목적이 있다. 이때, 중간시전 영상을 생성하여 부호화하는 과정의 참조영상으로 이용할 수 있으며, 이를 위해서는 다시점 비디오 특성에 맞는 영상보간 방법이 필요하다. 기존에 제안되었던 영상보간법은 변위의 검색범위를 초기에 설정하여 블록정합을 이용하여 화소 단위로 변위를 측정하기 때문에 카메라 사이의 거리가 크거나 객체의 움직임이 커서 변위의 변동이 심한 영상에서는 안정적인 화질의 영상을 얻기 어렵다. 또한, 고정된 크기의 블록을 이용하여 전체 변위를 측정하므로 객체의 변위차가 큰 영역에서 변위 오류가 많이 발생한다. 본 논문에서는 이와 같은 문제를 해결하여 보다 개선된 화질의 중간시점 영상을 얻기 위한 새로운 영상보간법을 제안한다. 영역분할을 이용한 초기의 변위측정 과정에서, 처음부터 최대 변위의 범위를 설정하는 대신에 블록 단위로 대략적인 변위륵 측정한 후에, 가변 블록을 이용하여 보다 세밀한 변위를 측정한다. 이 방법은 변위차가 큰 객체의 경계 부분에서 보다 정확하게 변위를 측정 할 수 있으므로, 화소 단위로 변위를 측정할 때 이전에 추한 변위 정보를 바탕으로 각 화소별로 검색 범위를 설정한다. 적응적으로 설정된 검색 범위를 이용하여 화소 단위의 변위를 측정하면 보다 개선된 변위를 얻을 수 있다. 추가적으로, 변위측정 과정에서 발생하는 변위의 오류를 최대한 줄이기 위해 각 단계별로 미디언 필터를 이용하여 변위 오류를 수정하였다. 본 논문에서 제안한 방법으로 실험한 결과 기존의 영상보간 방법보다 화질이 약 $1{\sim}4dB$ 정도 개선되었다.필, 투명도 등을 위성원격탐사 자료와 GIS를 이용하여 공간분석을 실시하고, 공간분포도를 작성함으로써 대상해역의 해양환경을 파악하였다. 본 연구결과, 분석된 위성자료가 현장조사에 의한 검증이 이루어지지 않을 경우, 영상자료분석을 통한 표층수온 추출은 대기 중의 수증기와 에어로졸에 의한 계산치의 오차가 반영되기 때문에 실측치 보다 낮게 평가 될 수 있으므로, 반드시 이에 대한 검증이 필요함을 알 수 있었다. 현지관측에 비해 막대한 비용과 시간을 절약할 수 있는 위성영상해석방법을 이용한 방법은 해양수질파악이 가능할 것으로 판단되며, GIS를 이용하여 다양하고 복잡한 자료를 데이터베이스화함으로써 가시화하고, 이를 기초로 공간분석을 실시함으로써 환경요소별 공간분포에 대한 파악을 통해 수치모형실험을 이용한 각종 환경영향의 평가 및 예측을 위한 기초자료로 이용이 가능할 것으로 사료된다.염총량관리 기본계획 시 구축된 모형 매개변수를 바탕으로 분석을 수행하였다. 일차오차분석을 이용하여 수리매개변수와 수질매개변수의 수질항목별 상대적 기여도를 파악해 본 결과, 수리매개변수는 DO, BOD, 유기질소, 유기인 모든 항목에 일정 정도의 상대적 기여도를 가지고 있는 것을 알 수 있었다. 이로부터 수질 모형의 적용 시 수리 매개변수 또한 수질 매개변수의 추정 시와 같이 보다 세심한 주의를 기울여 추정할 필요가 있을 것으로 판단된다.변화와 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 발견하였다. 이상의 행태적 측면과 투자성과측면의 실증결과를 통하여 한국주식시장에 있어서 시장수익률을 평균적으로 초과할 수 있는 거래전략은

• Proceedings of the Technology Innovation Conference
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• 2004.06a
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• pp.214-236
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• 2004

최근 국제 금융시장은 컴퓨터 및 통신분야의 기술진보로 비 은행금융기관에서도 상업은행에 의해서만 독점적으로 제공되었던 거래서비스를 결합한 새로운 금융서비스를 제공할 수 있게 되었고, 이는 곧 은행과 비 은행금융기관의 벽을 무너뜨리는 양자 간 동질화 현상을 유도하였으며 활발한 금융 겸업화 현상으로 금융혁신이라고 부를 만한 서비스분야의 기술혁신 성과를 거두게 되었다. 본 연구는 금융권간 판매채널 통합의 시작이라 평가되는 방카슈랑스를 중심으로 금융기관 간 겸업의 활성화를 통해 산업 간 동질화를 이루는 시점에서 금융혁신이 이루어진다고 설정, 금융서비스의 기술혁신 성과를 예측해 보았다 이를 위해 먼저 우리나라 대표 금융기관 격인 은행과 보험, 증권회사가 금융겸업을 이루는 경우 각 겸업 주체 기관의 혁신적인 성과를 실증 분석하도록 한다. 분석대상은 표본기간 중 국내 주식시장에 연속으로 상장되어 있는 모든 은행, 증권, 손해보험 49개를 대상으로 수행하였으며, 표본자료는 KIS-LINE이 제공하는 재무제표자료와 한국증권거래소에서 제공하는 주가데이터에서 추출하였다. 본 연구의 금융혁신 성과 분석은 두 단계로 이루어진다. 먼저 금융기관 간 가상 합병을 이용, 시뮬레이션 분석을 하고 그 결과 각 금융기관의 ROA와 자기자본비율 및 안전성(위험성지표)을 분석한다 다음 단계로 안전성이 보장되지 않는 경우 적정하게 추정된 안전성 내에서 경영 성과를 이루는 최적 기간에 대해 실물옵션평가(Real Option Valuation) 분석을 한다. 그 결과 금융겸업을 통한 혁신 성과는 은행 주체로 비 은행금융기관과 겸영하는 경우 이상적으로 잘 보여지며, 증권 주체로 보험업을 겸업하는 경우는 ROA 와 자기자본비율 면에서는 혁신적이나 안전성 면에서는 저해된다는 결과를 알아내었다. 그리고 이 같은 안전성의 위험은 증권업을 실행하는데 요구되는 수준의 안전성을 유지하는 경우 3 년간의 투자기간 후에는 혁신사업으로 발전할 수 있는 발판을 마련하게 된다고 추정하였다.

• Journal of the Korea Society of Computer and Information
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• v.16 no.2
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• pp.267-278
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• 2011

This study examines the relationship among stock options, pay equity, organizational commitment. Employees who received stock options tend to perceive their pay more equitable and the tendency shows a positive relationship among the amount of stock options and the equity perception. Also employees who received stock options perceive greater procedural equity, as they recognize stock options are awarded to many employees. However, the perception of stock options was not significantly associated with organizational commitment, turnover intention, and pay satisfaction. In 2003, the study surveyed 115 employees who received stock options in 10 publicly owned Korean firms that introduced stock option plans. The statistical analysis leads to the conclusions as follows. First, as the number of stock options increases, the receiver tends to perceive that pay system is more distributively equitable. Second, as the number of stock option receivers increases, the employees perceive the pay system more procedurally equitable. Third, stock option payments don't ensure that it improves pay satisfaction, turnover intention, and organizational commitment. This study shows a positive relationship that stock options work favorably in terms of pay equity, but the effect doesn't seem to be widely positive. The reason is that the introduction of stock options in domestic firms has been made only recently after the foreign exchange crisis in the late 1990s. More experiments and design issues should be discussed for the future.

• Proceedings of the Korean Society of Embryo Transfer Conference
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• 2007.05a
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• pp.39-50
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• 2007

Sexing from bovine embryos fertilized in vitro implicates a possibility of the sex controlled cattle production. This study was carried out to produce the sex determined cattle through the embryonic sexing by fluorescence in situ hybridization (FISH) technique. FISH was achieved in in vitro fertilized bovine embryos using a bovine Y-specific DNA probe constructed from the btDYZ-1 sequence. Using this probe, a male-specific signal was detected on 100% of Y-chromosome bearing metaphase specimens. The analyzable rate of embryonic sexing by FISH technique was about 93% (365/393) regardless of embryonic stages. As tested single blastomere by FISH and then karyotype with their biopsied embryos, the accuracy of sex determination with FISH was 97.6%. We tried the embryo transfer with sex determined embryos on 15 cattle. Among them, the 5 cattle delivered calf with expected sex last year.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2006.11a
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• pp.279-282
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• 2006

IP망을 이용한 IPTV 방송 서비스가 새로운 수익 모델로 인정받고 현재 국내의 KT, SKT 등이 IPTV 시범서비스를 준비하거나 진행 중에 있다 이 IPTV 서비스는 이전의 단방향 방송과는 달리 사용자와의 인터렉션을 중시하는 양방향 방송을 표방하기 때문에 지금까지의 방송과는 다른 혁신적인 방송서비스가 기대된다. 하지만 IPTV 서비스에 있어서 여러 통신사와 방송사가 참여할 수 있을 것으로 보여지는 것과는 달리 실상은 몇몇 거대 통신기업이 자신들의 망을 이용하는 가입자들을 상대로 한정된 사업을 벌이고 있다. 이는 IPTV 서비스를 위한 인프라가 구축되어 있지 않고 방통융합망의 개념을 만족시키기 위해 서비스 개발자가 알아야 할 프로토콜들이 너무나 많기 때문이다. 따라서 본 논문에서는 이러한 상황을 타개할 수 있는 수단을 Open API로 제안한다. 맞춤형 방송을 위한 시나리오를 TV-Anytime의 벤치마킹과 유저 시나리오를 참고하여 재구성하고 이 시나리오로부터 IPTV 방송 서비스를 위한 방통융합망의 기본적이고 강력한 기능들을 Open API 함수로 정의하였다. 여기에서의 방송 서비스는 NDR, EPG, 개인 맞춤형 광고 서비스를 말하며 각 서비스를 위한 서버는 통합망 위에 존재하고 이 서버들이 개방하는 API들은 다른 응용프로그램에 의해 사용되는 것이기 때문에 가장 기본적인 기능을 정의하게 된다. 또한, 제안한 Open API 함수를 이용하여 개인 맞춤형 방송 응용 서비스를 구현함으로써 서비스 검증을 하였다. Open API는 웹서비스를 통해 공개된 기능들로써 게이트웨이를 통해 다른 망에서 사용할 수 있게 된다. Open API 함수의 정의는 함수 이름, 기능, 입 출력 파라메터로 이루어져 있다. 사용자 맞춤 서비스를 위해 전달되는 사용자 상세 정보와 콘텐츠 상세 정보는 TV-Anytime 포럼에서 정의한 메타데이터 스키마를 이용하여 정의하였다.가능하게 한다. 제안된 방법은 프레임 간 모드 결정을 고속화함으로써 스케일러블 비디오 부호화기의 연산량과 복잡도를 최대 57%감소시킨다. 그러나 연산량 감소에 따른 비트율의 증가나 화질의 열화는 최대 1.74% 비트율 증가 및 0.08dB PSNR 감소로 무시할 정도로 작다., 반드시 이에 대한 검증이 필요함을 알 수 있었다. 현지관측에 비해 막대한 비용과 시간을 절약할 수 있는 위성영상해석방법을 이용한 방법은 해양수질파악이 가능할 것으로 판단되며, GIS를 이용하여 다양하고 복잡한 자료를 데이터베이스화함으로써 가시화하고, 이를 기초로 공간분석을 실시함으로써 환경요소별 공간분포에 대한 파악을 통해 수치모형실험을 이용한 각종 환경영향의 평가 및 예측을 위한 기초자료로 이용이 가능할 것으로 사료된다.염총량관리 기본계획 시 구축된 모형 매개변수를 바탕으로 분석을 수행하였다. 일차오차분석을 이용하여 수리매개변수와 수질매개변수의 수질항목별 상대적 기여도를 파악해 본 결과, 수리매개변수는 DO, BOD, 유기질소, 유기인 모든 항목에 일정 정도의 상대적 기여도를 가지고 있는 것을 알 수 있었다. 이로부터 수질 모형의 적용 시 수리 매개변수 또한 수질 매개변수의 추정 시와 같이 보다 세심한 주의를 기울여 추정할 필요가 있을 것으로 판단된다.변화와 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 발견하였다. 이상의 행태적 측면과 투자성과측면의 실증결과를 통하여 한국주식시장에 있어서 시장수익률을 평균적으로 초과할 수 있는 거래전략은 존재하므로 이러한 전략을 개발 및 활용할 수 있으며, 특히, 한국주식시장에 적합한 거래전략은 반전거래전략이고, 이 전략의 유용성은 투자자가 설정한 투자기간보다

• KDI Journal of Economic Policy
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• v.31 no.1
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• pp.239-276
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• 2009

The paper investigates the possible relationship between earnings prediction by security analysts and special ownership ties that link security companies those analysts belong to and firms under analysis. "Security analysts" are known best for their role as information producers in stock markets where imperfect information is prevalent and transaction costs are high. In such a market, changes in the fundamental value of a company are not spontaneously reflected in the stock price, and the security analysts actively produce and distribute the relevant information crucial for the price mechanism to operate efficiently. Therefore, securing the fairness and accuracy of information they provide is very important for efficiencyof resource allocation as well as protection of investors who are excluded from the special relationship. Evidence of systematic distortion of information by the special tie naturally calls for regulatory intervention, if found. However, one cannot presuppose the existence of distorted information based on the common ownership between the appraiser and the appraisee. Reputation effect is especially cherished by security firms and among analysts as indispensable intangible asset in the industry, and the incentive to maintain good reputation by providing accurate earnings prediction may overweigh the incentive to offer favorable rating or stock recommendation for the firms that are affiliated by common ownership. This study shares the theme of existing literature concerning the effect of conflict of interests on the accuracy of analyst's predictions. This study, however, focuses on the potential conflict of interest situation that may originate from the Korea-specific ownership structure of large conglomerates. Utilizing an extensive database of analysts' reports provided by WiseFn(R) in Korea, we perform empirical analysis of potential relationship between earnings prediction and common ownership. We first analyzed the prediction bias index which tells how optimistic or friendly the analyst's prediction is compared to the realized earnings. It is shown that there exists no statistically significant relationship between the prediction bias and common ownership. This is a rather surprising result since it is observed that the frequency of positive prediction bias is higher with such ownership tie. Next, we analyzed the prediction accuracy index which shows how accurate the analyst's prediction is compared to the realized earnings regardless of its sign. It is also concluded that there is no significant association between the accuracy ofearnings prediction and special relationship. We interpret the results implying that market discipline based on reputation effect is working in Korean stock market in the sense that security companies do not seem to be influenced by an incentive to offer distorted information on affiliated firms. While many of the existing studies confirm the relationship between the ability of the analystand the accuracy of the analyst's prediction, these factors cannot be controlled in the above analysis due to the lack of relevant data. As an indirect way to examine the possibility that such relationship might have distorted the result, we perform an additional but identical analysis based on a sub-sample consisting only of reports by best analysts. The result also confirms the earlier conclusion that the common ownership structure does not affect the accuracy and bias of earnings prediction by the analyst.

• 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.

• The Korean Journal of Financial Management
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• v.6 no.1
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• pp.31-62
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• 1989

By using deterministic dynamic models, we observe the behavior of the foreign exchange rate of a small open economy with rational expectation formation and different restrictions on the international economic integrations. First, an economy connected to the world by purchasing power parity and uncovered interest parity is studied in the next section. In both sections, financial assets available in the economy are domestic money and bonds. Stocks are added as a financial instrument in the next section, and real capital accumulation is also taken into account. Furthermore, the economy concerned there is fairly autonomous, and not directly governed by either purchasing power parity or uncovered interest parity. The expectation formation used throughout the whole paper is complete perfect foresight, which is the certainty version of rational expectation and free from any forecast errors. It is found that upon monetary expansion the short run depreciation of the foreign exchange rate is a fairly robust result regardless of the degree of the international economic integration, while it is not true for fiscal expansion. The expectation on the long run state significantly affects the short run response of the exchange rate. All of our models postulate that the current account should be balanced eventually. As the result, the short run behavior of the exchange rate is affected by the expectation on the long run balance and may well be a blend of the traditional flow view and modem asset view. The initial overshooting of the exchange rate is easily observed even in the fairly autonomous economy Furthermore, the initial overshooting is not reduced over time, but augmented for some time before it is eventually eliminated. As long as we maintain rational expectaion, introducing time delay in the adjustment of the foreign goods price to the foreign exchange rate does not make much difference.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.