• Journal of Korean Institute of Industrial Engineers
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• v.40 no.2
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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.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2001.11a
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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.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.3
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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.

• Journal of Korea Technology Innovation Society
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• v.5 no.2
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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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• v.5 no.2
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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.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.