• International conference on construction engineering and project management
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• 2005.10a
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• pp.1244-1249
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• 2005

Present PII(Private Infrastructure Investment) in Korea has increased up to 11% compared to the year 2003 and is expected to increase in the future. In spite of its rapid increase, we don't have any definite standard or system which distinctly presents the rate of return for domestic PII yet, and practical and scientific research is not sufficient compared to its necessity and importance. Hence, in this study we suggests methods to estimate the rate of return of PII to promote SOC PII to last successfully and present the proper level of rate of return of PII which is appropriate for domestic situations through diverse analysis. Therefore, to present reasonable rate of return, we have used 5 methods: previous research analysis, case study, financial index analysis, analysis of investor's rate of return, and analysis of rate of return in a real estate market. After comparing and analyzing these methods, at the end, we have presented the appropriate level of rate of return of PII, which can be applied in the domestic market.

• Korean Journal of Construction Engineering and Management
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• v.5 no.6 s.22
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• pp.179-190
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• 2004

Present private infrastructure investment in SOC investment has increased up to $11\%$ compared to the year 2003 and it is expected to increase in the future. In spite of its rapid increasement we don't have definite standard or system on distinctly presented rate of return for domestic private infrastructure investment yet, and practical and scientific research is not sufficient, compared to its necessity and importance. Hence, in this study we tried to build theories systematically, which are related to rate of return of private infrastructure investment to promote SOC private infrastructure investment to last successfully and present the proper level of rate of return of private infrastructure we investment appropriate in domestic situations through diverse analysis. Therefore, to present reasonable rate of return, we used 5 methods: existing research analysis, case study, financial index analysis, analysis of investors rate of return, and analysis of rate of return in a real estate market. After comparing and analyzing these methods, Ive presented in the end the appropriate level of rate of return of private infrastructure investment, which can be applied in a domestic market.

• Journal of Korean Institute of Industrial Engineers
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• v.12 no.2
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• pp.89-99
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• 1986

The study attempts to estimate and evaluate the rates of return on graduate degree holders who major in science and engineering. The model of this study adopts the rate of return method considering unemployment as well as mortality rate. The data are collected by questionnaires and interviews. The results show that the social rate of return(SROR) of bachelors is lower than the rate of return on investment in physical capital which is assumed as a decision criterion of public investment in Korea, but the SROR of Ph.D. holders is balanced. The results also show that the private rates of return (PROR) of all the levels in postgraduate science and engineering education are higher than the private discount rate which is a decision criterion of private investment in Korea. It also indicates that the PROR on investment in graduate education is not likely higher than the SROR because an individual bears high share of the educational costs.

• The Journal of Information Systems
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• v.28 no.4
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• pp.333-358
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• 2019

Purpose The study aims to develop a data-based decision model for private bankers when recommending hedge funds to their customers in financial institutions. Design/methodology/approach The independent variables are set in two groups. The independent variables of the first group are aggressive investors, active investors, and risk-neutral type investors. In the second group, variables considered by private bankers include customer propensity to invest, reliability, product subscription experience, professionalism, intimacy, and product understanding. A decision-making variable for a private banker is in recommending a first-rate general private fund composed of foreign and domestic FinTech products. These contain dependent variables that include target return rate(%), fund period (months), safeguard existence, underlying asset, and hedge fund name. Findings Based on the research results, there is a 94.4% accuracy in decision-making when the independent variables (customer rating, reliability, intimacy, product subscription experience, professionalism and product understanding) are used according to the following order of relevant dependent variables: step 1 on safeguard existence, step 2 on target return rate, step 3 on fund period, and step 4 on hedge fund name. Next, a 93.7% accuracy is expected when decision-making uses the following order of dependent variables: step 1 on safeguard existence, step 2 on target return rate, step 3 on underlying asset, and step 4 on fund period. In conclusion, a private banker conducts a decision making stage when recommending hedge funds to their customers. When examining a private banker's recommendations of hedge funds to a customer, independent variables influencing dependent variables are intimacy, product comprehension, and product subscription experience according to a categorical regression model and artificial neural network analysis model.

• KDI Journal of Economic Policy
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• v.27 no.1
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• pp.81-122
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• 2005

The paper aims to establish the characteristics of the Korean firms' R&D investment and to estimate the private rate of return to R&D investment. For the empirical analysis, a balanced panel data is constructed with 695 firms on 8 year observations. The panel data enables to characterize R&D investment of the Korean firms, which is mostly conformed to the 'stylized facts' of R&D investment that found in the previous studies on foreign firms. Klette model was used to estimate the private rate of return on R&D investment and depreciation rate of R&D capital. The paper estimates that the rate of return on R&D investment is 10.5% on average or 16.4% on median for the whole industry whereas manufacturing firms show 10.4% on average or 16.4% on median. The depreciation rate was estimated about 32.9% for whole industry, where it ranges from the minimum 11.6% for metal industry and 49.5% for services. The median estimate of the rate of return for R&D investment of Korean firms is roughly two times higher than real interest rates for the same period, which implies that R&D investment allows sizable rent in addition to the opportunity cost of capital investment.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2000.11a
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• pp.346-356
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• 2000

This paper aims to analyse the relationship between R＆D investment and economic growth in Korea. The analysis result shows 38 percent of average economic growth rate(7.1%) of Korea between 1976 and 1998 was achieved by the growth of Total Factor Productivity (TFP), and the R&D investment during the period contributed in achieving the economic growth rate by as much as 9.86 percent. In the process of the estimate, the rate of return of the R&D investment from both government and private was calculated as 47 percent. The relationship between private R&D investment, government R&D investment and the GDP was also investigated, and it was estimated that the private sector invested 2.0 percent of the GDP in R&D during the period, and was found that 1 won of government R&D investment induced 0.202 won of private sector's R&D investment. However, the time-lag effect, which is naturally believed to exist between the R&D investment and the economic growth, could not be analysed in a mathematical form, because of the lack of the data to establish this relationship. However, this paper believe that the time-lag effect in this relationship was included implicitly by using the data of 23 years.

• Health Policy and Management
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• v.20 no.3
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• pp.123-137
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• 2010

The main purpose of this article is for deriving functions related to the prediction of the closure of the hospitals, and finding out how the discriminant functions affect the closure of the hospitals. Empirical data were collected from 3 years financial statements of 41 private hospitals closed down from 2000 till 2006 and 62 private hospitals in business till now. As a result, the functions related to the prediction of the closure of the private hospital are 4 indices: Return on Assets, Operating Margin, Normal Profit Total Assets, Interest expenses to Total borrowings and bonds payable. From these discriminant functions predicting the closure, I found that the profitability indices - Return on Assets, Operating Margin, Normal Profit Total Assets - are the significant affecting factors. The discriminant functions predicting the closure of the group of the hospitals, 3 years before the closure were Normal Profit to Gross Revenues, Total borrowings and bonds payable to total assets, Total Assets Turnover, Total borrowings and bonds payable to Revenues, Interest expenses to Total borrowings and bonds payable and among them Normal Profit to Gross Revenues, Total borrowings and bonds payable to total assets, Total Assets Turnover, Total borrowings and bonds payable to Revenues are the significant affecting factors. However 2 years before the closure, the discriminant functions predicting the closure of the hospital were Interest expenses to Total borrowings and bonds payable and it was the significant affecting factor. And, one year before the closure, the discriminant functions predicting the closure were Total Assets Turnover, Fixed Assets Turnover, Growth Rate of Total Assets, Growth Rate of Revenues, Interest expenses to Revenues, Interest expenses to Total borrowings and bonds payable. Among them, Total Assets Turnover, Growth Rate of Revenues, Interest expenses to Revenues were the significant affecting factors.

• KDI Journal of Economic Policy
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• v.39 no.2
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• pp.75-98
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• 2017

This paper aims to estimate the social discount rate (SDR) rather than dig into its theoretical foundation. As SDRs can be derived by investigating both the rate of return on investment and the social time preference rate, we estimate the marginal productivity of both private and public capital and the time preference rate based on the Euler equation. In order to provide a single representative SDR, the weighted averages of the marginal productivity and time preference rate, whose weights are determined by the flow of funds data reflecting the social demand of funds, are presented. Based on the empirical results, we argue that the marginal productivity of private capital stands in the middle of the 3% range while that of public capital varies from 4.5% to 8.6%, with the time preference rate showing a decreasing trend from 3.2% in the early 2000s to 1.2% by around 2030. The single representative SDR or the weighted SDR is estimated to be approximately 3.0~4.5% and expected to continue its downward trend for the foreseeable future.

• Journal of the Korean housing association
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• v.14 no.2
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• pp.89-100
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• 2003

This study is identified the characteristics of housing product through housebuilders' strategies. As a methodology for this study, literature survey and questionnaire survey were used. Questionnaires have been done on mail to 232 housebuilders and return rate was 34.1%. In summary, housing product can be characterized long gestation period, periodically building cycle, future uncertainty of market volatility, maximization of land development gain, utilization of public fund for continuous building activity, moral hazard by accidentally bankrupcy. Therefore private housebuilding could be defined speculative industry.

• Journal of Technology Innovation
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• v.5 no.1
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• pp.206-222
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• 1997

The conventional analysis with which justifies government intervention of the private sector's innovation activities is the market failure approach. According to such analysis, fund allocation through autonomous market mechanisms is not optimal in technology financing because of the disparity between the desirable level of investment for society as a whole and that for private firms. To optimize the fund allocation, public policies such as subsidy, preferencial loan and venture capital investment programs are designed for technology development projects performed by private firms. They, however, have not been effective in increasing private investment for such projects. In most cases, it was found that little considerations given to the relationship between uncertainty embodied in technology development projects and each types of financing. With respect to optimizing fund allocation, technology development projects should be financed by different means according to their probability of success and the expected value of technology. Employing various theoretical models on financing decision-making we verify here that technology development projects to be supported by commercial banks or venture capital institutions is limited contingent upon levels of uncertainty adn expected value. Under the assumption that financial institutions are risk averse, loan or investment can be available only if the probability of success of the project is higher than the probability premium and the current market rate of interest. Therefore, the projects that have lower probability of success and/or small expected return are excluded from commercial loan or investment programs. However, the remaining projects, whose probability of success is low but with high expected return, may be applied under government subsidy programs. To achieve optimality of fund allocation and to activate technology financing, we conclude that there should be a systematic division of role among financial institutions including government commercial banks, and venture capital institutions.