• Journal of Korean Classical Literature and Education
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• no.34
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• pp.41-74
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• 2017

This study aimed to investigate the narrative strategy and meaning of fantasy in classic novels and to derive the educational meaning from the fantasy of Sukhyangjeon. This study selected Sukhyangjeon because is considered a suitable literary text that embodies characteristics of fantasy inherent in classical novels through its successful portrayal of the fantasy genre and its high popularity during its time. Specifically, this study observed that the "notice of hardships" which repeatedly appears in the narration of Sukhyangjeon reinforces the fantasy of the novel as it serves as an advance notice of intervention from heaven. Therefore, this study investigated the relationship between the notice of hardships and fantasy by focusing on Sukhyang's life. The way in which the "notice of hardships" is a form of illusion realized it evident in the plot when heaven saves Sukhyang from her hardship even though it was heaven that had granted her those forms of hardship. Firstly, the "notice from heaven" constitutes the macro structure of Sukhyangjeon. Through it, readers realize the enormous power of heaven by seeing how Sukhyang's life has been realized in accordance with heaven's notice with that, it evokes a sense of respect to heaven. In addition, heaven saves Sukhyang from different forms of danger. It enhances a miraculous feeling, which the omnipotent power of heaven shows, against the innocent appearance of Sukhyang during her times of danger. Meanwhile, when heaven notifies Sukhyang and the surrounding people of their fate and subsequently realizes it through dreams, this act creates a mysterious atmosphere and improves the probability of the narrative. If so, the narrative meaning in reinforcing the fantasy of the novel by the use of "notice of hardships" could be revealed through Sukhyang's real life. First, the hardship in which Sukhyang has gone through is so realistic and detailed that the readers' feelings of empathy are evoked with the fear of the coming hardship, wailing together, and trying to resolve the inner anxiety through Sukhyang's happiness. Second, the heavenly beings who are touched by the good behaviors of Sukhyang save her from the dangers of death. This creates the belief in readers that heaven intervenes for the good people. Third, the active attitude that Sukhyang and Leesun show in the process of marriage helps them overcome their earthly hardship and preserve their relationship in heaven. This gives readers hope that they could go to a "higher life" after going through suffering. This fantasy of Sukhyangjeon helps readers overcome their anxieties of reality through fantasy and recognizes the importance of relationships to enhance a sense of unity and solidarity with others. Because of these elements, it is expected that the fantasy of Sukhyangjeon will have a meaningful value to modern readers.

• 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.