• Journal of Information Technology Applications and Management
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• 제26권3호
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• pp.29-41
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• 2019

As users' interests of cryptocurrency has been increased, investment volume of it also increases. In the cryptocurrency market, it cannot always be distributed homogenous information to all investors, similar to the stock market because it reflects the characteristics of a market microstructure. Cryptocurrency traders, thus, like stock investors, can experience the information asymmetry in the market and cannot but help to depend on private information. The purpose of this study is to estimate the trading intensity of informed traders and uninformed traders among cryptocurrency investors around the world based on PIN (Probability of Informed Trading). We have an aim to compare the difference of information asymmetry according to the ten types of cryptocurrency. The results of this study are expected to prevent the continuous increase of suspicious transactions related to cryptocurrency and contribute to the development of a sound cryptocurrency market.

• 정보기술과데이타베이스저널
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• 제8권1호
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• pp.81-94
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• 2001

Providing explanations about recommending actions is one of the most important capabilities of expert systems. In fact, there exist many approaches incorporating this explanation facility into the system. Here we present briefly a new approach to generating these explanations and further attempt to investigate the impact of system explanations on user behaviors toward system-generated recommendations. For this experiment we designed a stock investment decision supporting system which, given a set of market situations, suggests an investment recommendation with explanations about the recommending action. Twenty-nine bank employees evaluated the output of the system in a laboratory setting. The results indicate that explanation facilities can make systems-generated advice more confident to users but cannot increase users'acceptance for the system conclusion.

• 국제지역연구
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• 제12권3호
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• pp.195-216
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• 2008

해외 주요 주식시장을 대상으로 하는 연구에서 단기적으로는 모멘텀 현상이 관찰되고 장기적으로는 반전현상이 관찰되고 있다. 그러나 한국 시장의 경우에는 장단기에 모두 반전현상이 있는 것으로 조사되고 있다. 본 연구에서는 한국 주식시장을 대상을 모멘텀과 반전현상의 관측 여부를 다시 확인하고 외국인투자자와 개인투자자의 투자행태와의 관련성을 분석한다. 그 결과 한국 시장에서는 기존연구와 같이 모멘텀 현상이 없다는 것이 확인되었다. 그러나 외국인투자자들을 대상으로 분석한 결과 외국인투자자들은 국내외에서 일관적으로 단기에는 모멘텀, 장기에는 반전투자전략을 쓰고 있는 것으로 나타났다. 한국에서 모멘텀 현상이 관찰되지 않는 것은 단기에도 반전투자전략을 사용하는 개인투자자들 때문인 것으로 추정된다.

• 유통과학연구
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• 제17권9호
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• pp.117-125
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• 2019

Purpose - We investigate how to increase consumer incentives to buy public offering funds, resulting in activating the public offering fund market. In particular, this study aims to find ways to expand diversity and to improve efficiency of public offering fund. The public fund market of Korea has been stagnant in recent years. However, the public offering fund market plays a very significant role in terms of consumer welfare. Since only a few wealthy investors can participate in the private equity market, the stagnation in the public offering fund market usually reduces the opportunity of consumer's buying funds thus ultimately affecting their future wealth. Research design, data, and methodology - To attain our purpose, the 'factor-based portfolio strategy' has been considered. It is an alternative portfolio strategy, which composites the advantages of the passive management and active management. For our empirical anaylsis, we used global stock distribution market data over the period of 1991 and 2016. Then we constructed portfolios based on firm-size, firm-value, and momentum. Finally, a regression model was set, then hypotheses were tested, analyzing the performances. Results - First, among the 15 factor-based portfolios of global, Europe, Asia-Pacific(ex Japan), US and Japan, in eight portfolios, positive excess returns are observed at 5% significance level. Further, there is another portfolio with positive excess return at 10% significance level. Second, most of the portfolios with significant excess performance show positive relationship with the market portfolio. However, the firm-value based portfolio in Asia-Pacific region shows no relationship, and the firm-value based portfolio in US shows negative relationship. Third, we confirmed that the two firm-value factor portfolios in Asia-Pacific region and US, not having positive relationship with market portfolio, provide significant excess returns. Conclusions - In this paper, we provide empirical evidences supporting that the factor-based portfolios expand the diversity of funds and improve the efficiency of investment performance. However, there is no guarantee that the efficiency will continue in the future. In addition, various constraints and costs must be considered. Nevertheless, our novel findings in the advanced financial market such as US and Asia-Pacific are very interesting and offers important implications.

• 한국정보시스템학회지:정보시스템연구
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• 제12권2호
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• pp.1-20
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• 2003

This paper deals with the application of the genetic algorithm to the technical trading rule of the stock market. MACD(Moving Average Convergence & Divergence) and the Stochastic techniques are widely used technical trading rules in the financial markets. But, it is necessary to determine the parameters of these trading rules in order to use the trading rules. We use the genetic algorithm to obtain the appropriate values of the parameters. We use the daily KOSPI data of eight years during January 1995 and October 2002 as the experimental data. We divide the total experimental period into learning period and testing period. The genetic algorithm determines the values of parameters for the trading rules during the teaming period and we test the performance of the algorithm during the testing period with the determined parameters. Also, we compare the return of the genetic algorithm with the returns of buy-hold strategy and risk-free asset. From the experiment, we can see that the genetic algorithm outperforms the other strategies. Thus, we can conclude that genetic algorithm can be used successfully to the technical trading rule.

• The Journal of Asian Finance, Economics and Business
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• 제8권10호
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• pp.147-158
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• 2021

Due to COVID-19, the risk of price volatility in commodity and equity markets increases. The research and application of hedging is the most effective way to reduce the market risk. Hedging is a risk management strategy employed to offset losses in investments by taking an opposite position in a related asset. We use K-means and hierarchical clustering methods to cluster companies and futures products respectively, and analyze the relationship between the number of hedging firms, regional distribution, nature of firms, capital distribution, company size, profitability, number of local Futures Commission Merchants (FCMs), regional location, and listing time. The study shows that listed companies with large scale and good profitability invest more money in hedging, while state-owned enterprises' participation in hedging is more likely to be affected by the company size and the number of local futures commission merchants, and private enterprises are more likely to be affected by the company profitability and the regional location. Listed companies are more willing to choose long-listed and mature futures products for hedging. We also provide policy advice based on our conclusion. So far, there is no study on the characteristics of hedging. This paper fills the gap. The results provide a basis and guidance for people's investment and risk management. Using clustering analysis in hedging study is another innovation of this paper.

• 경영과학
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• 제31권3호
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• pp.1-12
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• 2014

This study suggests the two types DEA models such as DEA CCR model and Super Efficiency model to evaluate the value of a company and to apply them for the investments. 14 kinds of real data of companies such as EV/EBITDA, EPS growth rate, PCR, PER, dividend yield, PBR, stock price/net current asset, debt ratio, current ratio, ROE, operating margin, inventory turnover, accounts receivable turnover, and sales growth ratio were used as input variables of DEA models. 12 year data from December 30, 2000 up to December 30, 2012 were collected, and the data with negative, missing and 0 values were removed reflecting the characteristics of the DEA. In order to verify the effectiveness of the models, we compared the historical variability and rate of return of both models those of the market. Study results are as follows. First, two DEA models are more stable than market in terms of rate of return because the historical variability of both models are less than that of market. Second, Super Efficiency model is more stable than CCR model. Lastly, the cumulative rate of return of Super Efficiency model (434%) is greater than that of the CCR model (420%) and that of the market (269%).

• 지식경영연구
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• 제22권4호
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• pp.173-187
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• 2021

투입 증대를 통한 경제성장 전략은 급격히 변화하는 경제환경과 맞물리면서 한계를 보이면서 기업의 투입 대비 효율성에 대한 관심이 증가하고 있다. 본 연구는 국내 제조기업 중 연구개발 투자액 상위 938개 기업의 2005~2018년 패널 데이터를 활용하여 기업의 효율성을 측정하고, 효율성에 영향을 주는 요인을 분석하였다. 분석 결과, 기업의 혁신을 위한 투입인 연구개발집중도와 연구개발 성과인 특허가 모두 기업의 효율성을 높이는 것으로 나타났다. 이 밖에 기업의 규모, 부채비율, 수익성 등도 기업의 효율성에 유의미한 영향을 주었다. 분석결과를 통해 본 연구는 기업의 낮은 효율성을 극복하기 위한 지식경영 관점에서의 시사점을 제시하였다.

• Journal of the Korean Data and Information Science Society
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• 제22권1호
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• pp.37-47
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• 2011

주가지수 옵션시장에는 많은 투자전략이 개발되어 있다. 그중 차익거래 전략은 시장이 효율성 유지측면에서 매우 중요한 역할을 하고 있다. 본 연구는 이러한 차익거래 전략 중 박스스프레드 전략을 적용하여 과거 옵션 데이터를 통해 사후 검증하고 러프 집합을 이용해 수익성을 향상시키고자 한다. 옵션 데이터는 2002년 1월부터 2006년 12월까지 실제 증권거래소에서 거래되었던 틱 데이터를 기반으로 하고 있으며 비주얼 베이직을 이용해 9시부터 오후 3시까지의 1분 마다의 종가인 1분봉으로 변형하여 분석을 하였다. 박스스프레드 전략은 낮은 위험, 낮은 이익 구조를 가지고 있다. 기존의 전략을 과거 데이터를 기반으로 백 테스팅 해보고 러프 집합을 이용하여 거래 진입 시점을 제한함으로써, 동일 위험 대비 좀 더 높은 수익구조를 만들어 낼 수 있는 전략을 구사한다면 낮은 위험으로 안정적 수익을 취할 수 있다.

• 한국IT서비스학회지
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• 제19권2호
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• pp.109-124
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• 2020

Facing the 4^th Industrial Revolution era, researches on artificial intelligence have become active and attempts have been made to apply machine learning in various fields. In the field of finance, Robo Advisor service, which analyze the market, make investment decisions and allocate assets instead of people, are rapidly expanding. The stock price prediction using the machine learning that has been carried out to date is mainly based on the prediction of the market index such as KOSPI, and utilizes technical data that is fundamental index or price derivative index using financial statement. However, most researches have proceeded without any explicit verification of the prediction rate of the learning data. In this study, we conducted an experiment to determine the degree of market prediction ability of basic indicators, technical indicators, and system risk indicators (AR) used in stock price prediction. First, we set the core parameters for each financial indicator and define the objective function reflecting the return and volatility. Then, an experiment was performed to extract the sample from the distribution of each parameter by the Markov chain Monte Carlo (MCMC) method and to find the optimum value to maximize the objective function. Since Robo Advisor is a commodity that trades financial instruments such as stocks and funds, it can not be utilized only by forecasting the market index. The sample for this experiment is data of 17 years of 1,500 stocks that have been listed in Korea for more than 5 years after listing. As a result of the experiment, it was possible to establish a meaningful trading strategy that exceeds the market return. This study can be utilized as a basis for the development of Robo Advisor products in that it includes a large proportion of listed stocks in Korea, rather than an experiment on a single index, and verifies market predictability of various financial indicators.