• 대한산업공학회지
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• 제40권2호
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• pp.195-204
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• 2014

The transition from fossil to renewable energy is inevitable due to fossil depletion. So, Renewable energy is very important for energy security and economic growth although it's R&D is long-term and high risky project. We propose new valuation method which combined system dynamics and compound real option method for long-term and high risk projects such as renewable energy. This method can show dynamic valuation results for the complex causal interaction and be easy for Monte-Carlo simulation to estimate volatility. And it can reflect the value of flexible decision for uncertainty. We applied the empirical analysis for Korea's photovoltaic industry by using this method. As results by empirical analysis, photovoltaic's R&D has high valuation using this method compared by traditional valuation methods such as DCF.

• 한국기술혁신학회:학술대회논문집
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• 한국기술혁신학회 2001년도 추계학술대회
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• pp.307-337
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• 2001

In this paper, we propose the valuation frame of the IT(Information Technology) ventures using ROV(Real Options Valuation) model. Generally, ROV can comprises the traditional valuation method such as DCF(Discounted Cash Flow), which can measure only the tangible value of a firm from the expected future earnings, in that ROV can additionally measure the intangible value such as the strategic value of a firm in the uncertain environment. We set up the hypothetic IT venture future investment plan and assume that there are a growth option and a switching option consequently along the investment time horizon, which are caused by each characteristics of ventures and IT technologies, especially modularity. In the case that there are several embedded real options in the firm's investment plan in a row, we should apply the compound option pricing model as a real option valuation model in order to consider the value interaction between real options. In an addition, we present the results of optimal investment timing analysis using real options approach and compare them. with those of the original assumed investment timing.

• 대한산업공학회지
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• 제32권3호
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• pp.180-190
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• 2006

We evaluate the economic value and the optimal investment timing of IMT-2000 in Korea, in the perspective of a service provider who owns the business license for IMT-2000, by using the real options analysis. The result clearly shows the project value with options is positive and delaying the investment is more favorable to the provider. Binomial lattice approach, in which we try to describe American call option and sequential compound option, and sensitivity analysis present the optimal decisions according to future states and enable the management to make decision strategically and proactively.

• 기술혁신학회지
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• 제5권2호
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• pp.141-151
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• 2002

Many technology investment projects can be considered as set of sequential options. A compound real option can be used for evaluating sequential technology investment decisions under significant uncertainty and measuring its value. In this paper, the formula developed by Geske and Johnson(1984) and Buraschi and Dumas(2001) was applied to evaluate the technology investment with related double real option. Also double real option was com-pared with net present value method and multiple linear regression model was used to assess the partial effects of risk free rate and log-term volatility on its value.

• Asian Journal of Innovation and Policy
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• 제5권2호
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• pp.197-207
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• 2016

This paper seeks to measure the monetary value of technical development in the deep seabed manganese nodule mining by applying the compound option model (COM). The COM is appropriate for the project in terms of its decision-making structure and embedded uncertainty. The estimation results show that the deep seabed mining project has more economic potential than shown by the previously obtained results from the discounted cash flow (DCF) analysis. In addition, it is reasonable to invest in the project taking the various uncertainty factors into consideration, because the ratio of the value to the cost of the project is far higher than one. This information can be utilized in national ocean policy decision-making.

• 지능정보연구
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• 제22권4호
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• pp.177-192
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• 2016

기계학습(Machine Learning)은 인공 지능의 한 분야로, 데이터를 이용하여 기계를 학습시켜 기계 스스로가 데이터 분석 및 예측을 하게 만드는 것과 관련한 컴퓨터 과학의 한 영역을 일컫는다. 그중에서 SVM(Support Vector Machines)은 주로 분류와 회귀 분석을 목적으로 사용되는 모델이다. 어느 두 집단에 속한 데이터들에 대한 정보를 얻었을 때, SVM 모델은 주어진 데이터 집합을 바탕으로 하여 새로운 데이터가 어느 집단에 속할지를 판단해준다. 최근 들어서 많은 금융전문가는 기계학습과 막대한 데이터가 존재하는 금융 분야와의 접목 가능성을 보며 기계학습에 집중하고 있다. 그러면서 각 금융사는 고도화된 알고리즘과 빅데이터를 통해 여러 금융업무 수행이 가능한 로봇(Robot)과 투자전문가(Advisor)의 합성어인 로보어드바이저(Robo-Advisor) 서비스를 발 빠르게 제공하기 시작했다. 따라서 현재의 금융 동향을 고려하여 본 연구에서는 기계학습 방법의 하나인 SVM을 활용하여 매매성과를 올리는 방법에 대해 제안하고자 한다. SVM을 통한 예측대상은 한국형 변동성지수인 VKOSPI이다. VKOSPI는 금융파생상품의 한 종류인 옵션의 가격에 영향을 미친다. VKOSPI는 흔히 말하는 변동성과 같고 VKOSPI 값은 옵션의 종류와 관계없이 옵션 가격과 정비례하는 특성이 있다. 그러므로 VKOSPI의 정확한 예측은 옵션 매매에서의 수익을 낼 수 있는 중요한 요소 중 하나이다. 지금까지 기계학습을 기반으로 한 VKOSPI의 예측을 다룬 연구는 없었다. 본 연구에서는 SVM을 통해 일 중의 VKOSPI를 예측하였고, 예측 내용을 바탕으로 옵션 매매에 대한 적용 가능 여부를 실험하였으며 실제로 향상된 매매 성과가 나타남을 증명하였다.