Earticle

This research aims to examine the predictive power of multiple online stock message boards, namely, NAVER Finance and PAXNET, which are the most popular stock message boards in South Korea, in stock market activities. If predictive power exists, we then compare the predictive power of multiple online stock message boards. To accomplish the research purpose, we constructed a panel data set with close price, volatility, Spell out acronyms at first mention.PER, and number of posts in 40 companies in three months, and conducted a panel vector auto-regression analysis. The analysis results showed that the number of posts could predict stock market activities. In NAVER Finance, previous number of posts positively influenced volatility on the day. In PAXNET, previous number of posts positively influenced close price, volatility, and PER on the day. Second, we confirmed a difference in the prediction power for stock market activities between multiple online stock message boards. This research is limited by the fact that it only considered 40 companies and three stock market activities. Nevertheless, we found correlation between online stock message board and stock market activities and provided practical implications. We suggest that investors need to focus on specific online message boards to find interesting stock market activities.

부정맥 분류를 위한 기존 연구들은 분류의 정확성을 높이기 위해 신경망, 퍼지, 시계열 주파수 분석, 비선형 분석법 등이 연구되어 왔다. 이러한 방법들은 분류율를 향상시키기 위해 정확한 특징점과 많은 양의 신호를 처리해야 하기 때문에 데이터의 가공 및 연산이 복잡하며, 다양한 부정맥을 분류하는데 어려움이 있다. 본 연구에서는 AR(Auto Regressive) 모델링 기반의 특징점 추출과 SVM(Support Vector Machine)을 통한 조기수축 부정맥 분류 방법을 제안한다. 이를 위해 잡음을 제거한 ECG 신호에서 R파를 검출하고 QRS와 RR 간격의 특정 파형 구간을 모델링하였다. 이후 최적 세그먼트 길이(n1, n2), 최적 차수( p1, p2)의 4가지 AR 모델링 변수를 추출하고 SVM을 통해 Normal, PVC, PAC를 분류하였다. 연구의 타당성을 입증하기 위해 MIT-BIH 부정맥 데이터베이스를 대상으로 한 R파의 평균 검출 성능은 99.77%, Normal, PVC, PAC 부정맥은 각각 99.23%, 97.28, 96.62의 평균 분류율을 나타내었다.

Legacy study for detecting arrhythmia have mostly used nonlinear method to increase classification accuracy. Most methods are complex to process and manipulate data and have difficulties in classifying various arrhythmias. Therefore it is necessary to classify various arrhythmia based on short-term data. In this study, we propose a feature extraction based on auto regressive modeling and an premature contraction arrhythmia classification method using SVM., For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. Also, we classified Normal, PVC, PAC through SVM in realtime by extracting four optimal segment length and AR order. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 99.23%, 97.28%, 96.62% in Normal, PVC, PAC classification.

교통량이 지속적으로 증가함에 따라 교통량을 정확히 예측하는 것이 중요한 과제로 떠오르고 있다. 기존 교통량 예측 모형으로 신경망, 회귀분석, 시계열 등이 많이 연구되었다. 본 논문에서는 벡터자기회귀(vector auto-regressive; VAR)를 적용하여 교통량 예측 모형을 개발하였다. 한국도로공사에서 제공하는 2004년 1월부터 2012년 12월까지의 신갈-수원, 신갈-마성, 신갈-동수원, 신갈-서울, 수원-기흥 구간의 교통량 자료를 사례로 이용하였다. 특히, 신갈-마성 구간을 중심으로 다른 구간들과의 관계를 찾고자 하였다. VAR을 통해 예측모형을 설정하고, 단변량 시계열모형과 예측 성능을 비교하였다. 충격반응함수를 통해 신갈-마성 구간에 대한 VAR모형 내 다른 구간들의 영향을 살펴보고, 분산분해를 통해 개별 구간들의 신갈-마성 구간 예측에 대한 상대적 중요성을 알아보았다. 관심의 대상인 신갈-마성 구간의 교통량에 대한 주변 구간들과의 수리적 모형을 구축하였고 주변 구간들의 영향력을 수치적으로 시각적으로 확인하였다. 신갈-마성 구간에 대한 인접 구간들의 영향력을 파악함으로써 차후 도로 건설 및 도로 관리에 도움이 될 것으로 사료된다.

With the continuous increasing in the volume of the traffic, the exact forecasting is emerging as one of the principal challenges these days. Even though neural network, regression analysis and time series analysis have already been studied as traffic forecasting model, in this study, we am going to find out the model using vector auto-regressive. Vector auto-regressive is the way of combination regression analysis with time series analysis. It expands the traditional ARMA to multivariate model. In this study, we have referred to the traffic data of Singal-Suwon, Singal-Masung, Singal-Dongsuwon, Singal-Seoul, Suwon-Kiheung sections from January 2004 to December 2012 from the materials of Korea Expressway Corporation. For the exact analysis, we did log transform after eliminating seasonal factors of materials. We set up the forecasting model through VAR, compared the performances with univariate time series. In addition, we examined the influence of variable in the VAR with using impulse response analysis and the relative importance of individual explanatory variables using variance decomposition analysis as well. As the result of the analysis, VAR has a better predictive power than univariate time series to a certain degree. It also shows the relative importance of individual explanatory variables.

This research aims to identify and analyze factors impacting on the prepayment risk involved in MBS(Mortgage-Backed Security). To do so, Some independent variables likely to affect prepayment in MBS and 143-month time series MBS(issued by the Korea Housing Finance Corporation) data from January 2006 to November 2017 are selected and applied to a VAR(Vector Auto-Regression) model using Eviews program. Finally, the results drawn from this study is that new loan interest rate, apartment price, and unemployment rate have significant statistical impact on prepayment risk.