• 대한안전경영과학회지
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• 제6권2호
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• pp.211-229
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• 2004

The basis of cyber trading has been sufficiently developed with innovative advancement of Internet Technology and the tendency of stock market investment has changed from long-term investment, which estimates the value of enterprises, to short-term investment, which focuses on getting short-term stock trading margin. Hence, this research shows a Short-term Stock Price Forecasting System on Learning Agent System using DTA(Decision Tree Algorithm) ; it collects real-time information of interest and favorite issues using Agent Technology through the Internet, and forms a decision tree, and creates a Rule-Base Database. Through this procedure the Short-term Stock Price Forecasting System provides customers with the prediction of the fluctuation of stock prices for each issue in near future and a point of sales and purchases. A Human being has the limitation of analytic ability and so through taking a look into and analyzing the fluctuation of stock prices, the Agent enables man to trace out the external factors of fluctuation of stock market on real-time. Therefore, we can check out the ups and downs of several issues at the same time and figure out the relationship and interrelation among many issues using the Agent. The SPFA (Stock Price Forecasting System) has such basic four phases as Data Collection, Data Processing, Learning, and Forecasting and Feedback.

• 산업공학
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• 제24권4호
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• pp.408-419
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• 2011

R&D is a foundation for new business chance and productivity improvement leading to enormous expense and a long-term multi-step process. During the R&D process, decision-makers are confused due to the various future uncertainties that influence economic and technical success of the R&D projects. For these reasons, several decision-making models for R&D project investment have been suggested; they are based on traditional methods such as Discounted Cash Flow (DCF), Decision Tree Analysis (DTA) and Real Option Analysis (ROA) or some fusion forms of the traditional methods. However, almost of the models have constraints in practical use owing to limits on application, procedural complexity and incomplete reflection of the uncertainties. In this study, to make the constraints minimized, we propose a new model named Real Option Decision Tree Model which is a conceptual combination form of ROA and DTA. With this model, it is possible for the decision-makers to simulate the project value applying the uncertainties onto the decision making nodes.

• 가족자원경영과 정책
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• 제11권2호
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• pp.1-19
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• 2007

The purpose of this study was to analyze the affecting factors on employed mens' retirement and reemployment decision making. The focus was on the process of employed mens' decision on retirement and their reemployment decision after retirement from present job. The major findings were as follows ; First, the employed men who had a retirement plan were having more household income, more household net asset, more savings and investment for elderly life, and more positive attitude toward retirement. Second, the major factors affecting on having retirement plan or not were employed mens' age, household income, expected income after retirement, savings and investment for elderly life, job, and attitude toward retirement. Third, the major affecting factors on expected retirement age were employed mens' age, health status, job security, and attitude toward retirement. Forth, the employed mens' reemployment decision was affected from their household income, expected income after retirement, pension ownership, and attitude toward retirement. From the findings, it can be concluded that the employed mens' age, economic status, and attitude toward retirement played a important role in the process of retirement and reemployment decision making.

• KDI Journal of Economic Policy
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• 제28권1호
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• pp.117-144
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• 2006

본 연구는 기초재고스톡이 고정투자 수요에 미치는 영향을 반영하는 '실질완충효과(real buffer effect)'와 기말재고스톡과 고정투자를 위한 자금조달 원천 사이의 대체효과를 반영하는 '재무적 완충효과(financial buffer effect)'가 존재하는가를 1990~2003년 기간 동안 361개 상장 제조기업 자료를 이용하여 분석하였다. 또한 재고스톡과 고정투자 간의 관계가 외환위기 전 후에 차이가 있는가를 아울러 살펴보았다. 먼저 재고스톡과 고정투자 간의 이론적인 연관성을 Bo(2004)의 논문을 통해 살펴본 후, Dynamic GMM-SYS 방법을 이용하여 재고스톡과 고정투자 사이의 구조적 관계를 검정하였다. 전체 기간 및 기간 구분에 따른 추정결과는 기말재고스톡이 고정투자와 음의 관계를 나타내고 있고, 또한 기초재고스톡이 고정투자와 통계적으로 유의한 음의 관계를 보이고 있음도 확인하였다. 이러한 결과는 두 가지 측면에서 해석이 가능하다. 기업은 예상치 못한 높은 수요에 직면했을 때 재고스톡을 완충장치(buffer)로 이용하고 있을 뿐만 아니라 재고투자를 고정투자를 위한 재원의 대비책으로 보유한다는 것이다. 이러한 결과를 통해서 알 수 있는 것은 기업의 의사결정은 생산 및 재고스톡 조정 그리고 고정투자에 대한 의사결정과 재무적 의사결정이 상호 연관되어 있다는 것이다.

• 산업경영시스템학회지
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• 제16권27호
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• pp.113-119
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• 1993

The purpose of this study is to develop a analytic procedure of the alternatives for the FMS that consist of high-priced facilities. A procedure to analysis the alternatives are as follows. Stage one is procedure to appraise investment alternatives of FMS and devide factors into strategic, analytic, economic viewpoint and draw upon decision making matrix through normalization Stage two is appraise to normalized alternatives using TOPSIS model among multiple attributes decision making models.

• 정보보호학회논문지
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• 제27권4호
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• pp.855-861
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• 2017

Gordon과 Loeb[1]은 기업의 정보보호 투자 의사결정은 정보보호 투자를 통한 한계편익(MB)과 정보보호 투자의 한계비용(MC)이 일치하는 곳에서 최적의 투자수준이 결정된다고 한다. 그러나, 정보보호 사고를 당하고 있는 많은 기업의 경우, 정보보호 사고를 당하고 있다는 것을 인지하지 못하고 유출되는 피해가 얼마인지 측정하지 못하고 있다. 본 연구에서는 불완전한 정보 상황에서 정보보호 투자 의사결정을 수행하는 모델을 Gordon과 Loeb[1]의 모델을 수정하여 제시하고, 투자수준의 차이를 비교하였다. 불완전정보 하에서 정보보호 투자를 통한 기대수익은 실제 발생하는 정보 보호 사고에 비해 낮게 인식되는 경향을 띄게 되고, 정보보호 투자도 적게 되었다. 이는 정부와 같은 제3의 기관이 정보 보호 사고발생률, 피해액 규모 등 정확한 정보를 알려주면 기업 스스로 정보보호 투자를 확대 할 수 있음을 보여준다.

• 한국중재학회지:중재연구
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• 제17권2호
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• pp.141-165
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• 2007

This study aims to make a contribution to the promotion of trade and economic development of South Korea, and, at the same time, call attention to the increasing trend of investment agreements concluded within Free Trade Agreements (FTA) by examining theoretically FTAs and dispute resolution and investigating systematically the conclusion procedure of agreements, and the system, institutions, and jurisdiction of dispute resolution, and presenting these findings to the government and investors involved. The most problematic aspect in the legal process of arbitration involving disputes over investment is that of arguments concerning the right of jurisdiction. When a dispute arises, even though an investor files for arbitration at an ICSID institution, the parties become involved in another energy-consuming argument even before proceeding to the hearing and decision of the original plan in cases in which the respondent of the dispute files an objection to the decision rights of the arbitral tribunal. As the main basis for this type of plea, the point of non-existence of jurisdiction is first raised where the applicable dispute does not fall under the range of investments defined in individual investment contracts or investment agreements such as a Bilateral Investment Treaty (BIT). To avoid an open-ended definition of investment for the range of investments, articles concerning investments in the FTA and NAFTA between Canada and the USA adopt the limited closed-list method. Article 96 of the FTA between Japan and Mexico applied the same abovementioned method of limited form of definition regarding range of investments and concluded BITs between member countries of APEC applied a similar method as well. Instead of employing the previously used inclusive definition, the BITs concluded between countries of Latin America and the USA are equipped with limited characteristics of an investment. Furthermore, to correspond with this necessary condition the three following requirements are needed : 1) fixed investment funding; 2) expected profits resulting from such investments; 3) and the existence of fixed risk bearing.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제1권3호
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• pp.229-242
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• 2012

다수의 기업은 정보보호투자에 대한 필요성은 인식하고 있으나, 정보보호 투자로 인한 효과가 가시적으로 파악하기 힘들고, 침해사고로 인한 피해규모 또한 산정하기 매우 힘들다. 그렇기 때문에 기업은 정보보호에 관한 투자의사결정을 하기도 쉽지 않고 투자규모 또한 산정하기 쉽지 않은 실정이다. 물론 기업의 투자의사결정을 위한 전통적인 투자기법들은 많지만, 정보보호투자는 다른 실물자산에 대한 투자에 비하여 투자효과의 불확실성이 매우 높기 때문에 전통적인 투자분석 기법으로는 한계를 가지고 있다. 본 연구에서는 기업이 투자 의사결정을 함에 있어서 전통적으로 사용한 투자분석기법들에 대해 기술하고, 미래에 대한 불확실성이 큰 금융 선물(先物), 옵션(Option)의 평가기법에서 발전한 실물옵션(Real Option) 분석기법을 정보보호투자 분석에 활용하는 방법을 제시하고자 한다.

• Asia pacific journal of information systems
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• 제9권4호
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• pp.125-142
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• 1999

The increased investment in technological innovations makes the investigation of factors affecting technology adoption more interesting. Several perspectives have been proposed to explain the determinants of information technology adoption. While the traditional innovation diffusion research streams try to explain and predict adoption behavior with the adopter's perceptions about the characteristics of the innovation itself, critical mass theorists argue that adoption behavior as a collective action is based on what their business partners are doing and whether there exists enough critical mass to justify the investment. Drawing on theses two perspectives, this study investigates the decision criteria in the adoption of information technology as innovation and factors affecting the decision criteria. The survey results reveal that the adoption behavior is affected both by innovation characteristics and by critical mass's activity. Correlation analysis, t-test, and stepwise regression models also show that as the environmental uncertainty is getting higher, adoption decision is affected more by what others are doing, and that highly competitive organizations seem to play the role of critical mass.

• 무역학회지
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• 제46권2호
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• pp.281-299
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• 2021

This paper analyzes the feasibility of using machine learning methods to forecast the employment. The machine learning methods, such as decision tree, artificial neural network, and ensemble models such as random forest and gradient boosting regression tree were used to forecast the employment in Busan regional economy. The following were the main findings of the comparison of their predictive abilities. First, the forecasting power of machine learning methods can predict the employment well. Second, the forecasting values for the employment by decision tree models appeared somewhat differently according to the depth of decision trees. Third, the predictive power of artificial neural network model, however, does not show the high predictive power. Fourth, the ensemble models such as random forest and gradient boosting regression tree model show the higher predictive power. Thus, since the machine learning method can accurately predict the employment, we need to improve the accuracy of forecasting employment with the use of machine learning methods.