• The Journal of Society for e-Business Studies
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• v.9 no.2
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• pp.33-50
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• 2004

This paper presents the evaluation model which is included in a quantitative evaluation methodology for evaluation processes of validity and effectiveness of information budget investment and improving conformance of ROI(Return On Investment). That is, the right efficiency targets of information investment for the development project to GDOC (Government electronic Document Distribution Center), and KPls(Key Performance Indexes) which enable to evaluate objectively effectiveness in the preliminary and post evaluation phases are developed, and then the results were come out by practical investigation study using quantitative evaluation methodology.

• Journal of Multimedia Information System
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• v.8 no.3
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• pp.183-190
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• 2021

In this paper, we implement a system for a fund recommendation based on the investment propensity and for a future fund price prediction. The investment propensity is classified by scoring user responses to series of questions. The proposed system recommends the funds with a suitable risk rating to the investment propensity of the user. The future fund prices are predicted by Prophet model which is one of the machine learning methods for time series data prediction. Prophet model predicts future fund prices by learning the parameters related to trend changes. The prediction by Prophet model is simple and fast because the temporal dependency for predicting the time-series data can be removed. We implement web pages for the fund recommendation and for the future fund price prediction.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.330-334
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• 2011

Build-operate-transfer (BOT) projects are privatized infrastructure undertakings that face long-term investment risks and uncertainties. To ensure these projects can be completed on time and operated according to performance specifications, governments usually require BOT concessionaires to furnish performance bonds as a security. However, in order to attract investment, governments often provide abandonment rights for concessionaires to deal with investment risks and uncertainties. In the context of real options, these abandonment rights will increase project value, but the furnish of performance bonds will reduce this value. Currently in the BOT context, there is no real option model that can handle explicitly the impact of performance bonds on project value. In this paper, a real option valuation model is derived to deal with this important issue. The Taiwan high-speed rail project is used as a case study to show the applicability of the proposed model.

• Journal of the Korean Operations Research and Management Science Society
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• v.1 no.1
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• pp.151-170
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• 1976

There are many cases of production processes which intermittently produce several different kinds of products for stock through one set of physical facility. In this case, an important question is what size of production run should be prduced once we do set-up for a product in order to minimize the total cost, that is, the sum of the set-up, carrying, and stock-out costs. This problem is used to be called scheduling of multiple products through a single facility in the production management field. Despite the very common occurrence of this type of production process, no one has yet devised a method for determining the optimal production schedule. The purpose of this study is to develop quantitative analytical models which can be used practically and give us rational production schedules. The study is to show improved models with application to a can-manufacturing plant. In this thesis the economic production quantity (EPQ) model was used as a basic model to develop quantitative analytical models for this scheduling problem and two cases, one with stock-out cost, the other without stock-out cost, were taken into consideration. The first analytical model was developed for the scheduling of products through a single facility. In this model we calculate No, the optimal number of production runs per year, minimizing the total annual cost above all. Next we calculate No$_{i}$ is significantly different from No, some manipulation of the schedule can be made by trial and error in order to try to fit the product into the basic (No schedule either more or less frequently as dictated by) No$_{i}$, But this trial and error schedule is thought of inefficient. The second analytical model was developed by reinterpretation by reinterpretation of the calculating process of the economic production quantity model. In this model we obtained two relationships, one of which is the relationship between optimal number of set-ups for the ith item and optimal total number of set-ups, the other is the relationship between optimal average inventory investment for the ith item and optimal total average inventory investment. From these relationships we can determine how much average inventory investment per year would be required if a rational policy based on m No set-ups per year for m products were followed and, alternatively, how many set-ups per year would be required if a rational policy were followed which required an established total average inventory inventory investment. We also learned the relationship between the number of set-ups and the average inventory investment takes the form of a hyperbola. But, there is no reason to say that the first analytical model is superior to the second analytical model. It can be said that the first model is useful for a basic production schedule. On the other hand, the second model is efficient to get an improved production schedule, in a sense of reducing the total cost. Another merit of the second model is that, unlike the first model where we have to know all the inventory costs for each product, we can obtain an improved production schedule with unknown inventory costs. The application of these quantitative analytical models to PoHang can-manufacturing plants shows this point.int.

• Journal of the Korea Society of Computer and Information
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• v.9 no.1
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• pp.111-121
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• 2004

This paper presents the methods of evaluating validity and effectiveness of informatization budget investment for e-Government, through the preliminary evaluation. the progress evaluation and the Post evaluation of the investment, and a quantitative evaluation model which improves conformance of IT ROI (Return On Investment) using the evaluation results. the right evaluation targets of informatization investment for the development project to GDOC(Government electronic Document Distribution Center), and KPIs(Key Performance Indexes) which is enable to evaluate objectively effectiveness in the preliminary and the post evaluation phases are developed, and according to using the quantitative evaluation model . improved effectiveness evaluation results for each index in the point of view of usage and innovation are come out.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.1
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• pp.1-10
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• 2008

The enormous budget of government and manpower are invested to the government funded institutes every year. The R&D investment focused on input has to be turned toward the investment based on the effectiveness of R&D activities. Measuring the efficiency of research activities is required in order to evaluate the effectiveness of R&D investment in these institutes. The purpose of this paper is to evaluate the relative efficiency of research activities performed in 19 government funded research institutes. CCR/BCC model and DEA/AR model were applied to get the relative efficiency of 19 institutes. Assurance regions for the weight of output attributes were obtained by using the underlined concept of the analytic hierarchy process (AHP). We used input and output data items describing research activity of 19 government funded research institutes. The results of this study are expected to become a basis of the R&D investment decision of the government.

• International Commerce and Information Review
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• v.12 no.4
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• pp.171-192
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• 2010

In general, a stock index and its individual stocks are assumed to follow a random walk. A stock index is an important source of information and one that is seen by people everyday, regardless of their investment intentions. This paper examines the correlation between the KOSPI-the index that best reflects the Korean stock market and the macro - economic variables that have been found to influence the index by previous studies. The sample period considers the years after 2000 when the Korean stock market matured as restrictions on foreign investors were removed. For this purpose, a Vector Error Correction Model (VECM) and KOSPI equation with a general pacific approach were used. This paper aims at verifying the factors that determined the KOSPI after 2000 and at examining whether there was structural change in the investment environment. It also investigates changes in the factors determining the KOSPI's performance as a result of structural changes in the investment environment. The V AR (Vector Autoregressive) model including the nine variables was selected as a baseline model whose stability was tested using the unit root test. The results from the VECM and the structural changes in the investment environment can be summarized by the following Inner story points.

• Knowledge Management Research
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• v.19 no.1
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• pp.79-95
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• 2018

The purpose of this case study is to analyze the B2C security information service model using the robo-advisor, to develop various service models and to urge new companies to enter. Overseas robo-advisor service market is growing rapidly with the launch of various B2C service models beyond B2B. On the other hand, as the domestic market is dominated by B2B services and serviced just index portfolio which is nascent, it lacks products which are used for active asset management. Recently as the government announced the approval of online investment advisory service, the B2C market of domestic asset management has entered a growth phase, centered on generations familiar with IT. We propose to extend the concept of Robo-Advisor service in accordance with the financial market change. By that model, we will study the case of the algorithm of the investment masters' philosophy and contribute to the expansion of the B2C service market.

• Knowledge Management Research
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• v.19 no.1
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• pp.97-118
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• 2018

The finance-investment industry is currently focusing on research related to artificial intelligence and big data, moving beyond conventional theories of financial engineering. However, the case of equity optimization portfolio by using an artificial intelligence, big data, and its performance is rarely realized in practice. Thus, the purpose of this study is to propose process improvements in equity selection, information analysis, and portfolio composition, and lastly an improvement in portfolio returns, with the case of an equity optimization model based on quantitative research by an artificial intelligence. This paper is an empirical study of the portfolio based on an artificial intelligence technology of "D" asset management, which is the largest domestic active-quant-fiduciary management in accordance with the purpose of this paper. This study will apply artificial intelligence to finance, analyzing financial and demand-supply information and automating factor-selection and weight of equity through machine learning based on the artificial neural network. Also, the learning the process for the composition of portfolio optimization and its performance by applying genetic algorithms to models will be documented. This study posits a model that the asset management industry can achieve, with continuous and stable excess performance, low costs and high efficiency in the process of investment.

• Journal of Technology Innovation
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• v.6 no.2
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• pp.159-177
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• 1998

Although each company is trying to develop an economic analysis model with its own particular style or format, the appropriate method is not yet developed because there are many problems to be solved such as uncertainity of outcomes and intangible benefits of technology. The purpose of tris paper therefore is to suggest an economic analysis methodology, which reflects the complexity and the risk of R&D investment, through a case study on the development of a superconductor fault current limiter. A self-developed Monte Carlo simulation program utilized as a main tool in this paper was very useful for risk analysis of R&D investment which could not be solved in the previous DCF(Discounted Cash Flow) model. We also introduce learning effect to consider the intangible benefits such as Know-How obtained from R&D execution. The expected value and its probability distribution for R&D investment can be obtained by combining the Monte Carlo method with the decision tree approach. This result is helpful in judging the priority and the resource-allocation of R&D projects. It is however necessary to develop more precise model for quantifying the technology stock and the simulation program using the continuous probability distribution in expected values to improve the reliability of economic analysis on R&D projects.