• Journal of Convergence for Information Technology
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• v.11 no.5
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• pp.94-103
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• 2021

This study aims to propose technical trading rules for Bitcoin futures and empirically analyze investment performance. Investment strategies include standard trading rules such as VMA, TRB, FR, MACD, RSI, BB, using Bitcoin futures daily data from December 18, 2017 to March 31, 2021. The trend-following rules showed higher investment performance than the comparative strategy B&H. Compared to KOSPI200 index futures, Bitcoin futures investment performance was higher. In particular, the investment performance has increased significantly in Sortino Ratio, which reflects downside risk. This study can find academic significance in that it is the first attempt to systematically analyze the investment performance of standard technical trading rules of Bitcoin futures. In future research, it is necessary to improve investment performance through the use of deep learning models or machine learning models to predict the price of Bitcoin futures.

• Knowledge Management Research
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• v.22 no.2
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• pp.77-93
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• 2021

In order to continue to grow in response to the rapidly changing industrial environments, companies must retain technological innovation capabilities and enhance market competitiveness. When competition is intensifying for creating new businesses and developing new products through technology commercialization, creating and utilizing technology convergence performance is an important means to create new competitiveness. However, there has been a lack of effort to systematically understand the level of technology convergence performance of the enterprise and to understand its relationship with management performance. In this paper, we develop a new analytical index by segmenting the technology convergence into patent variety, balance and disparity using patented IPC code information based on the concepts presented in existing diversity studies. In addition, 4,522 patents granted for three years between 2013 and 2015 by 219 KOSDAQ companies belonging to the domestic ICT convergence industry were analyzed to demonstrate that the level of technology convergence performance is positively related to sales growth rate in 2016.

• Journal of Korean Dental Hygiene Science
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• v.1 no.1
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• pp.57-63
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• 2018

The coffee demand market in Korea is growing every year, and the adults drink an average of one cup of coffee every day. In order to investigate the effect of coffee on oral malodor, 15 Korean subjects were recruited. There were no significant differences in the values of oral malodor using BB checker(Taiyo, Japan) in the factors of sex, smoking, drinking, feeling oral malodor by themselves, feeling oral malodor by others, periodic scaling, activity of Streptococcus mutans, bleeding on probing, and coffee drinking habits. Patient hygiene performance index, calculus index, index of coat on the tongue, toothbrushing time, toothbrushing frequency had no correlation with oral malodor. The difference between before and after drinking coffee with syrup(17.93±16.54) was significant, but the difference between before and after drinking coffee without syrup(1.13±14.75) was not significant. We suggested the cause of oral malodor after drinking coffee was syrup, but not coffee.

• Environmental and Resource Economics Review
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• v.14 no.4
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• pp.817-838
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• 2005

In this paper, the resource adequacy as well as the optimum fuel mix is obtained by the following procedures. First, the regulation body, the government agency, determine the reliability index as well as the optimum portfolio of the fuel mix during the planning horizon. Here, the resources with the characteristics of public goods such as demand-side management, renewable resources are assigned in advance. Also, the optimum portfolio is determined by reflecting the economics, environmental characteristics, public acceptance, regional supply and demand, etc. Second, the government announces the required amount of each fuel-type new resources during the planning horizon and the market participants bid to the government based on their own estimated fixed cost. Here, the government announces the winners of the each auction by plant type and the guaranteed fixed cost is determined by the marginal auction price by plant type. Third, the energy market is run and the surplus of each plant except their cost (guaranteed fixed cost and operating cost) is withdrew by the regulatory body. Here, to induce the generators to reduce their operating cost some incentives for each generator is given based on their performance. The performance is determined by the mechanism of the performance-based regulation (PBR). Here the free-riding performance should be subtracted to guarantee the transparent competition. Although the suggested mechanism looks like very regulated one, it provides two mechanism of the competition. That is, one is in the resource construction auction and the other is in the energy spot market. Also the advantages of the proposed method are it guarantee the proper resource adequacy as well as the desired fuel mix. However, this mechanism should be sustained during the transient period of the deregulation only. Therefore, generation resource planning procedure and market mechanisms are suggested to minimize possible stranded costs.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.109-130
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• 2011

One of the major problems in the area of data mining is the size of the data, as most data set has huge volume these days. Streams of data are normally accumulated into data storages or databases. Transactions in internet, mobile devices and ubiquitous environment produce streams of data continuously. Some data set are just buried un-used inside huge data storage due to its huge size. Some data set is quickly lost as soon as it is created as it is not saved due to many reasons. How to use this large size data and to use data on stream efficiently are challenging questions in the study of data mining. Stream data is a data set that is accumulated to the data storage from a data source continuously. The size of this data set, in many cases, becomes increasingly large over time. To mine information from this massive data, it takes too many resources such as storage, money and time. These unique characteristics of the stream data make it difficult and expensive to store all the stream data sets accumulated over time. Otherwise, if one uses only recent or partial of data to mine information or pattern, there can be losses of valuable information, which can be useful. To avoid these problems, this study suggests a method efficiently accumulates information or patterns in the form of rule set over time. A rule set is mined from a data set in stream and this rule set is accumulated into a master rule set storage, which is also a model for real-time decision making. One of the main advantages of this method is that it takes much smaller storage space compared to the traditional method, which saves the whole data set. Another advantage of using this method is that the accumulated rule set is used as a prediction model. Prompt response to the request from users is possible anytime as the rule set is ready anytime to be used to make decisions. This makes real-time decision making possible, which is the greatest advantage of this method. Based on theories of ensemble approaches, combination of many different models can produce better prediction model in performance. The consolidated rule set actually covers all the data set while the traditional sampling approach only covers part of the whole data set. This study uses a stock market data that has a heterogeneous data set as the characteristic of data varies over time. The indexes in stock market data can fluctuate in different situations whenever there is an event influencing the stock market index. Therefore the variance of the values in each variable is large compared to that of the homogeneous data set. Prediction with heterogeneous data set is naturally much more difficult, compared to that of homogeneous data set as it is more difficult to predict in unpredictable situation. This study tests two general mining approaches and compare prediction performances of these two suggested methods with the method we suggest in this study. The first approach is inducing a rule set from the recent data set to predict new data set. The seocnd one is inducing a rule set from all the data which have been accumulated from the beginning every time one has to predict new data set. We found neither of these two is as good as the method of accumulated rule set in its performance. Furthermore, the study shows experiments with different prediction models. The first approach is building a prediction model only with more important rule sets and the second approach is the method using all the rule sets by assigning weights on the rules based on their performance. The second approach shows better performance compared to the first one. The experiments also show that the suggested method in this study can be an efficient approach for mining information and pattern with stream data. This method has a limitation of bounding its application to stock market data. More dynamic real-time steam data set is desirable for the application of this method. There is also another problem in this study. When the number of rules is increasing over time, it has to manage special rules such as redundant rules or conflicting rules efficiently.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Journal of Technology Innovation
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• v.28 no.2
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• pp.1-36
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• 2020

While the policy intervention of each country for the promotion of renewable energy is strengthened, Korea introduced Feed-in Tariff (FIT) in 2002 to directly support the development of renewable energy. But in 2012, the shift of policy instrument that from FIT to Renewable Portfolio Standard (RPS) is occurred. This is a unique background that is currently found only in Korea, and new answers that focus on the outcomes of the shift of policy instruments are needed in addition to the existing discussion of comparison of FIT and RPS. Therefore, this study analyzed the change of policy efficiency after the shift to RPS using Data Envelopment Analysis(DEA) and Malmquist Index. In the result of analysis, a difference in the improvement of policy efficiency after in shift to RPS is found among each renewable energy source. This result is because renewable energy companies voluntarily entered the market only for energy sources that have secured technology or price competitiveness, and this indicates that the performance of renewable energy after the RPS shift has been concentrated on specific energy sources. As a result of this study, considering that the goal of renewable energy policy is to expand distribution and to drive growth engines, multi-faceted analysis is required in consideration of technology and market in selecting policy instruments.

• The Journal of the Korea Contents Association
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• v.11 no.2
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• pp.238-250
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• 2011

In 2006, the number of days of the screen quota (theatrical movie screening days of Korean produced films in a year required by the law) was reduced to 73 days from 146 days per year. Three years after the reduction of screen quota, this paper studies the impact of reduction of screen quota system on Korean produced films. Using Non-parametric statistics, Gini Index and Regression analysis, this study shows that the average number of Korean moviegoer of Korean films which was released last three years(2007-2009) after the cutback of screen quota in 2006 is reduced to 640,109.9123 from 1,107,217.82 for three years(2003-2005) before the cutback. And this is significant in statistics. while Hollywood film gets 76,168,518 more audiences than the total number of audience for 2003-2005, the total numbers of Korean films is cut to 218,917,590 (2007-2009) from 245,802,356 (2003-2005). Gini Index of 2009(0.84) indicates that the inequality of a distribution of box office performance of Korean films is getting worse after the cutback. For now, the reduction of screen quota has negative effects for Korean films. Only the technological advance, education of relates personnel, redesigning of the related infrastructure, market-driven movies, creative production, and the promotion of the independent films can reduced the negative effects.

• 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.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.