• 지능정보연구
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• 제24권2호
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• pp.1-19
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• 2018

주가 예측은 학문적으로나 실용적으로나 중요한 문제이기에, 주가 예측에 관련된 연구가 활발히 진행되었다. 빅 데이터 시대에 도입하면서, 빅 데이터를 결합한 주가 예측 연구도 활발히 진행되고 있다. 다수의 데이터를 기반으로 기계 학습을 이용한 연구가 주를 이룬다. 특히 언론의 효과를 접목한 연구 방법들이 주목을 받고 있는데, 그중 온라인 뉴스를 분석하여 주가 예측에 활용하는 연구가 주를 이루고 있다. 기존 연구들은 온라인 뉴스가 개별 회사에 대한 미치는 영향을 주로 살펴보았다. 또한, 관련성이 높은 기업끼리 서로 영향을 주는 것을 고려하는 방법도 최근에 연구되고 있다. 이는 동질성을 가지는 산업군에 대한 효과를 살펴본 것인데, 기존 연구에서 동질성을 가지는 산업군은 국제 산업 분류 표준에 따른다. 즉, 기존 연구들은 국제 산업 분류 표준으로 나뉜 산업군이 동질성을 가진다는 가정하에서 분석을 시행하였다. 하지만 기존 연구들은 영향력을 가지는 회사를 고려하지 못한 채 예측하였거나 산업군 내에서 이질성이 존재하는 점을 반영하지 못했다는 한계점을 가진다. 본 연구는 산업군 내에 이질성이 존재함을 밝히고, 이질성을 반영하지 못한 기존 연구의 한계점을 K-평균 군집 분석을 적용하여, 주가에 영향을 미치는 산업군의 동질적인 효과를 반영할 수 있는 방법론을 제안하였다. 방법론이 적합하다는 것을 증명하기 위해 3년간의 온라인 뉴스와 주가를 통해 실험한 결과, 다수의 경우에서 본 논문에서 제시한 방법이 좋은 결과를 나타냄을 확인할 수 있었으며, 국제 산업 분류 표준 산업군 내에서 이질성이 클수록 본 논문에서 제시한 방법이 좋은 효과를 보인다는 것을 확인할 수 있었다. 본 연구는 국제 산업 분류 표준으로 나누어진 기업들이 높은 동질성을 가지지 않는 다는것을 밝히고 이를 반영한 예측 모형의 효율성을 입증하였다는 점에서 의의를 가진다.

• 재무관리연구
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• 제27권2호
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• pp.85-114
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• 2010

본 연구에서는 성과연동형 스톡옵션을 가장 활발하게 부여한 국내 금융업을 대상으로 경영자에 대한 성과연동형 스톡옵션 부여가 과연 기업가치를 유효하게 증가시켰는지를 실증분석을 통해 검증하였다. 2002~2005년 동안 스톡옵션을 부여한 금융기관을 대상으로 실증분석을 수행하여 다음과 같은 결과를 얻었다. 첫째, 스톡옵션 부여공시에 따른 단기 주가반응을 분석한 결과, 고정형 스톡옵션 부여공시는 기업가치에 별다른 영향을 미치지 않았다. 반면, 성과연동형 스톡옵션을 부여한 경우 기대와 달리 공시일 전후 유의한 음(-)의 초과수익률이 발견되었다. 금융기관별로는 은행의 성과 연동형 스톡옵션 부여공시가 강하고 유의한 음(-)의 초과수익률을 산출하였는데, 이는 선행연구에서 제시한 것처럼 은행처럼 규제가 많은 산업에서는 경영자가 경영의사결정을 내릴 때 재량권이 제한적이므로 투자자들이 스톡옵션 부여에 따른 유효성을 낮게 평가하기 때문으로 해석된다. 둘째, 스톡옵션 부여 이후 기업의 장기성과를 검증한 결과, 스톡옵션 부여가 기업가치를 증가시켰다는 증거를 발견하지 못했으며 이는 선행연구와 동일한 결과이다. 성과연동형 스톡옵션을 부여한 금융기관에서도 장기성과가 개선되었다는 결과를 얻지 못했으나, 고정형 스톡옵션 부여와 비교하여 볼 때, 스톡옵션 부여 후 1~24개월 및 1~36개월의 초과수익률이 상대적으로 높게 나타났다. 이는 향후 성과연동형 스톡옵션제도가 보완되고 보다 정교하게 설계되어 실행된다면 기업가치 개선에 기여할 여지가 있음을 시사한다.

• 유통과학연구
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• 제11권10호
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• pp.63-72
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• 2013

Purpose - This study analyzes the relationship and dynamic interactions between stock price index, interest rate, price index, and housing price indices using Korean monthly data from 2000 to 2013, based on a VAR model. This study also examines Granger causal relationships among these variables in order to determine whether the time series of one is useful in forecasting another, or to infer certain types of causal dependency between stochastic variables. Research design, data, and methodology - We used Korean monthly data for all variables from 2000: M1 to 2013: M3. First, we checked the correlations among different variables. Second, we conducted the Augmented Dickey-Fuller (ADF) test and the co-integration test using the VAR model. Third, we employed Granger Causality tests to quantify the causal effect from time series observations. Fourth, we used the impulse response function and variance decomposition based on the VAR model to examine the dynamic relationships among the variables. Results - First, stock price Granger affects interest rate and all housing price indices. Price index Granger, in turn, affects the stock price and six metropolitan housing price indices. However, none of the Granger variables affect the price index. Therefore, it is the stock markets (and not the housing market) that affects the housing prices. Second, the impulse response tests show that maximum influence on stock price is its own, and though it is influenced a little by interest rate, price index affects it negatively. One standard deviation (S.D.) shock to stock price increases the housing price by 0.08 units after two months, whereas an impulse shock to the interest rate negatively impacts the housing price. Third, the variance decomposition results report that the shock to the stock price accounts for 96% of the variation in the stock price, and the shock to the price index accounts for 2.8% after two periods. In contrast, the shock to the interest rate accounts for 80% of the variation in the interest rate after ten periods; the shock to the stock price accounts for 19% of the variation; however, shock to the price index does not affect the interest rate. The housing price index in 10 periods is explained up to 96.7% by itself, 2.62% by stock price, 0.68% by price index, and 0.04% by interest rate. Therefore, the housing market is explained most by its own variation, whereas the interest rate has little impact on housing price. Conclusions - The results of the study elucidate the relationship and dynamic interactions among stock price index, interest rate, price index, and housing price indices using VAR model. This study could help form the basis for more appropriate economic policies in the future. As the housing market is very important in Korean economy, any changes in house price affect the other markets, thereby resulting in a shock to the entire economy. Therefore, the analysis on the dynamic relationships between the housing market and economic variables will help with the decision making regarding the housing market policy.

• 한국콘텐츠학회논문지
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• 제12권2호
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• pp.472-482
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• 2012

주식시장에는 많은 투자자들이 참여하고 있으며 점점 더 많은 사람이 주식투자에 관심을 가지고 있다. 주식시장에서 위험을 회피하고 수익을 얻기 위해서는 다양한 정보를 바탕으로 정확한 의사결정을 해야한다. 즉 수익을 얻을 수 있는 종목 선택, 적절한 매수-매도 가격의 결정, 그리고 적절한 보유기간 등을 결정해야 한다. 본 논문에서는 개인 주식 투자자의 의사결정 지원을 위한 데이터마이닝 도구를 제안한다. 즉, 개인 투자자가 직접 기계학습 방법을 적용하여 주가예측 모델을 생성할 수 있게 하고, 적절한 매수-매도 가격과 보유기간 등을 결정하는 것을 도와주는 도구를 제안한다. 제안하는 도구는 과거 데이터를 이용하여 투자자 자신의 성향에 맞는 투자에서의 의사결정을 할 수 있도록 지원하는 도구로서 주가데이터 관리, 기계학습 적용을 통한 주가예측 모델 생성, 투자 시뮬레이션 등의 기능을 제공한다. 사용자는 스스로 주가에 영향을 미칠 수 있다고 판단하는 기술적 지표를 선정하고 이를 이용하여 주가예측 모델을 만들고 테스트 할 수 있으며, 적절한 예측모델을 적용하여 시뮬레이션을 수행해 봄으로써 실제로 어느 정도 수익을 얻을 수 있는지 평가하고 적절한 매매 정책을 수립할 수 있다. 제안하는 도구를 이용하여 주식 투자자는 기존의 감정적 판단에 의한 투자가 아닌 객관적 데이터에 의해 검증을 거친 주가예측 모델과 매매정책에 따라 주식투자를 할 수 있어 이전 보다 나은 수익을 기대할 수 있다.

• 한국컴퓨터정보학회논문지
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• 제21권6호
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• pp.9-19
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• 2016

In finance literature, stock liquidity showing how stocks can be cashed out in the market has received rich attentions from both academicians and practitioners. The reasons are plenty. First, it is known that stock liquidity affects significantly asset pricing. Second, macroeconomic announcements influence liquidity in the stock market. Therefore, stock liquidity itself affects investors' decision and managers' decision as well. Though there exist a great deal of literature about stock liquidity in finance literature, it is quite clear that there are no studies attempting to investigate the stock liquidity issue as one of decision making problems. In finance literature, most of stock liquidity studies had dealt with limited views such as how much it influences stock price, which variables are associated with describing the stock liquidity significantly, etc. However, this paper posits that stock liquidity issue may become a serious decision-making problem, and then be handled by using data mining techniques to estimate its future extent with statistical validity. In this sense, we collected financial data set from a number of manufacturing companies listed in KRX (Korea Exchange) during the period of 2010 to 2013. The reason why we selected dataset from 2010 was to avoid the after-shocks of financial crisis that occurred in 2008. We used Fn-GuidPro system to gather total 5,700 financial data set. Stock liquidity measure was computed by the procedures proposed by Amihud (2002) which is known to show best metrics for showing relationship with daily return. We applied five data mining techniques (or classifiers) such as Bayesian network, support vector machine (SVM), decision tree, neural network, and ensemble method. Bayesian networks include GBN (General Bayesian Network), NBN (Naive BN), TAN (Tree Augmented NBN). Decision tree uses CART and C4.5. Regression result was used as a benchmarking performance. Ensemble method uses two types-integration of two classifiers, and three classifiers. Ensemble method is based on voting for the sake of integrating classifiers. Among the single classifiers, CART showed best performance with 48.2%, compared with 37.18% by regression. Among the ensemble methods, the result from integrating TAN, CART, and SVM was best with 49.25%. Through the additional analysis in individual industries, those relatively stabilized industries like electronic appliances, wholesale & retailing, woods, leather-bags-shoes showed better performance over 50%.

• 한국시스템다이내믹스연구
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• 제8권2호
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• pp.175-190
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• 2007

Stock market volatility largely depends on firms' value and growth opportunities. However, with the globalization of world economy, the effect of the synchronization in major countries is gaining its importance. Also, domestically, the business cycle and cash market of the country are additional factors needed to be considered. The main purpose of this research is to attest the application and usefulness of System Dynamics as a general stock market forecasting tool. Throughout this research, System Dynamics suggests a conceptual model for forecasting a KOSPI(Korea Composite Stock Price Index), taking the factors of the composite stock price indexes in traditional researches. In conclusion of this research, System Dynamics was proved to bean appropriate model for forecasting the volatility and direction of a stock market as a whole. With its timely adaptability, System Dynamic overcomes the limit of traditional statistic models.

• 동아시아식생활학회지
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• 제13권6호
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• pp.615-623
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• 2003

This study has been conducted to develop brown stock with the high pressure cooking(HPC) method. The sterilization methods, package film and storage methods, and quality maintenance during storage were investigated in this study. The packaging quality of NY/PP was inferior to that of PE/AL/PP since NY/PP facilitated the ventilation and moisture absorption. The maximum duration of the safe storage was found to be 50 days at 25$^{\circ}C$, 30 days at 35$^{\circ}C$ for NY/PP package film, and 60 days at 25$^{\circ}C$, 40 days at 35$^{\circ}C$ for PE/AL/PP one. These results showed that the overall quality of brown stock by the HPC method was not different significantly from that of brown stock by the traditional approach. Furthermore, the HPC approach might improve the productivity by saving the labour cost, food cost, and cooking time. Therefore, the traditional method might well be substituted by this newly developed method.

• 산업경영시스템학회지
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• 제20권41호
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• pp.79-86
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• 1997

This study is tested the information effects of convertible bond(CB). In orter to examine the abnormal stock returns of convertable day of CB, this study were selected 134 samples for the period from Jan.1988 to Dec.1994. There are some empirical studies which pesent evidents that CB are converted day of CB. The results of empirical study are summarized as follows. As in korea stock market, abnormal stock returns of CB have influenced on convertable day of CB. The day has some affirmative influences but it takes away stock price pressures, the amount of stock and dilution effects. As the results, related corporate stock price falled in preference to market abnormal returns.

• 재무관리논총
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• 제9권1호
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• pp.221-244
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• 2003

This study is aimed to investigate the effects of foreign exchange rates on stock market returns. For the United States, the United Kingdom, Germany, Japan and Korea, the cross-correlation precedence of foreign exchange rate on stock market is found in the case of Germany and Korea. But that of stock market is not observed in any case. We performed three kinds of causality and exogeneity test of Granger test, Sims test and Geweke-Meese-Dent test. The analyses on the full period show the time-lag causal, exogeneous relation of foreign exchange rates with Granger, Sims and GMD test for Korea. The United Kingdom presents the significance with Granger and Sims test while Germany reveals the time-lag relation with Granger and GMD test. When we divide the period into two parts with the Louvre Accord, the first part give the less degree of time-lag relation. But in the second period the three kinds of causality and exogeneity test propose consistent time-lag relation with foreign exchange rates on stock markets for the United Kingdom and Korea with the three test methods. And Granger's test prove German foreign exchange market have a time-lag relation on stock market.

• Communications for Statistical Applications and Methods
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• 제8권2호
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• pp.543-556
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• 2001

This study suggests integrated neural network modes for he stock price index forecasting using change-point detection. The basic concept of this proposed model is to obtain significant intervals occurred by change points, identify them as change-point groups, and reflect them in stock price index forecasting. The model is composed of three phases. The first phase is to detect successive structural changes in stock price index dataset. The second phase is to forecast change-point group with various data mining classifiers. The final phase is to forecast the stock price index with backpropagation neural networks. The proposed model is applied to the stock price index forecasting. This study then examines the predictability of integrated neural network models and compares the performance of data mining classifiers.