• Journal of Korea Technology Innovation Society
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• v.9 no.1
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• pp.84-102
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• 2006

This paper compares four models to value a biotechnology R&D project; option tree model, dynamic discounted cash flow(DCF) model, and option thinking DCF model with general DCF model. Real Options, especially 6-folded option tree model yields boner estimate of value than values using other methods. According to sensitivity analysis, sales of final products, number of investigational new drug developments(INDs) and success rates of each stage are key factors for the value of biotechnology R&D investment.

• IE interfaces
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• v.24 no.4
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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.

• Journal of information and communication convergence engineering
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• v.11 no.3
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• pp.190-198
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• 2013

Recently, one of the most vital advancement in the field of finance is high-performance trading using field-programmable gate array (FPGA). The objective of this paper is to design high-performance Black Scholes option trading system on an FPGA. We implemented an efficient Black Scholes Call Option System IP on an FPGA. The IP may perform 180 million transactions per second after initial latency of 208 clock cycles. The implementation requires the 64-bit IEEE double-precision floatingpoint adder, multiplier, exponent, logarithm, division, and square root IPs. Our experimental results show that the design is highly efficient in terms of frequency and resource utilization, with the maximum frequency of 179 MHz on Altera Stratix V.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1423-1431
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• 2022

Volatility is one of the variables that the Black-Scholes model requires for option pricing. It is an unknown variable at the present time, however, since the option price can be observed in the market, implied volatility can be derived from the price of an option at any given point in time and can represent the market's expectation of future volatility. Although volatility in the Black-Scholes model is constant, when calculating implied volatility, it is common to observe a volatility smile which shows that the implied volatility is different depending on the strike prices. We implement supervised learning to target implied volatility by adding V-KOSPI to ease volatility smile. We examine the estimation performance of KOSPI200 index options' implied volatility using various Machine Learning algorithms such as Linear Regression, Tree, Support Vector Machine, KNN and Deep Neural Network. The training accuracy was the highest(99.9%) in Decision Tree model and test accuracy was the highest(96.9%) in Random Forest model.

• Communications for Statistical Applications and Methods
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• v.31 no.5
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• pp.585-599
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• 2024

Options pricing remains a critical aspect of finance, dominated by traditional models such as Black-Scholes and binomial tree. However, as market dynamics become more complex, numerical methods such as Monte Carlo simulation are accommodating uncertainty and offering promising alternatives. In this paper, we examine how effective different options pricing methods, from traditional models to machine learning algorithms, are at predicting KOSPI200 option prices and maximizing investment returns. Using a dataset of 2023, we compare the performance of models over different time frames and highlight the strengths and limitations of each model. In particular, we find that machine learning models are not as good at predicting prices as traditional models but are adept at identifying undervalued options and producing significant returns. Our findings challenge existing assumptions about the relationship between forecast accuracy and investment profitability and highlight the potential of advanced methods in exploring dynamic financial environments.

• 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.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.1499-1504
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• 2008

This study shows risk-based evaluation results of casualty accidents for passengers, railway staffs and MOP(Member of public) on the national railway in South Korea. To evaluate risk of these accidents, the hazardous events and the hazardous factors were identified by the review of the accident history and engineering interpretation of the accident behavior. A probability evaluation model for each hazardous event which was based on the accident appearance scenario was developed by using the Fault Tree Analysis (FTA) technique. The probability for each hazardous event was evaluated from the historical data and structured expert judgment. In addition, the severity assessment model utilized by the Event Tree Analysis (ETA) technique was composed of the accident progress scenarios. And the severity for the hazardous events was estimated using fatalities and weighted injuries. The risk assessment model developed can be effectively utilized in defining the risk reduction measures in connection with the option analysis.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.179-185
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• 2015

The cost of advanced medical technologies is commonly considered to be a major factor in the overall escalation of expenditures on health. The use of computed tomography (CT) scanning has increased dramatically over the past decade. CT has been rapidly adopted, despite their high cost. The aim of this study is to analysis the increasing factor of the frequency of the CT, using the decision tree model. Finally, we propose the effective policy option of diagnostic imaging technology in Korea.

• Journal of computational fluids engineering
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• v.18 no.1
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• pp.69-76
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• 2013

To reduce the damage from the coastal disaster such as typhoon and tsunami, a possible option is the eco-friendly approach to minimize the destruction of ecological system. One of feasible idea is the forest for damage prevention artificially arranged along the beach. To understand a precise physics on the flow before and after the forest, we use a CFD method. In this paper, a three-dimensional numerical model has been constructed based on tree cases in a real forest located at Byin-myeon, Seocheon-gun, Chungnam. The CFD computation using a commercial code COMSOL multiphysics is performed for the distribution of real spatial coordinate of each tree. Through this investigation, the CFD techniques are shown to be applied to the research of forest composition plan. The physics in the regime from laminar to turbulent flow is qualitatively explained, and the obtained data are compared one another quantitatively.

• Health Policy and Management
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• v.29 no.3
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• pp.357-367
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• 2019

Background: This study evaluated the cost-effectiveness of 21 different national dyslipidemia screening strategies according to total cholesterol (TC) cutoff and screening interval among 40 years or more for the primary prevention of coronary heart disease over a lifetime in Korea, from a societal perspective. Methods: A decision tree was used to estimate disease detection with the 21 different screening strategies, while a Markov model was used to model disease progression until death, quality-adjusted life years (QALYs) and costs from a Korea societal perspective. Results: The results showed that the strategy with TC 200 mg/dL and 4-year interval cost \4,625,446 for 16.65105 QALYs per person and strategy with TC 200 mg/dL and 3-year interval cost \4,691,771 for 16.65164 QALYs compared with \3,061,371 for 16.59877 QALYs for strategy with no screening. The incremental cost-effectiveness ratio of strategy with TC 200 mg/dL and 4-year interval versus strategy with no screening was \29,916,271/QALY. At a Korea willingness-to-pay threshold of \30,500,000/QALY, strategy with TC 200 mg/dL and 4-year interval is cost-effective compared with strategy with no screening. Sensitivity analyses showed that results were robust to reasonable variations in model parameters. Conclusion: In this study, revised national dyslipidemia screening strategy with TC 200 mg/dL and 4-year interval could be a cost-effective option. A better understanding of the Korean dyslipidemia population may be necessary to aid in future efforts to improve dyslipidemia diagnosis and management.