효과적인 지식창출을 위한 웹 상의 지식채굴과정 : 주식시장에의 응용

This study proposes the knowledge discovery process for the effective mining of knowlege on the web. The proposed knowledge discovery process uses the prior knowledge base and the prior knowledge management system to reflect tacit knowledge in addition to explicit knowledge. The prior knowledge management system constructs the prior knowledge base using a fuzzy cognitive map, and defines information to be extracted from the web. In addition, it transforms the extracted information into the form being handled in mining process. Experiments using case-based reasoning and neural networks are performed to verify the usefulness of the proposed model. The experimental results are encouraging and prove the usefulness of the proposed model

인공신경망을 이용한 이자율 예측에 관한 연구

학위논문(석사) - 한국과학기술원 : 경영정보공학과, 1996.2, [ vii, 71 p. ]Most previous researches for the prediction models of interest rate have made efforts to test empirically whether interest rate determination theories may be applied to Korean situation rather than to forecast interest rate. For the application of forecasting interest rate, those interest rate determination models are not appropriate. Artificial neural network models were used for forecasting interest rate as a new methodology, which has proven itself successful in financial domain. This research intended to construct artificial neural network models which can maximize the performance of prediction, regarding Corporate Bond Yield(CBY) as interest rate. We combined the fundamental variables derived from interest rate determination theory and the market variables considering the supply and demand of corporate bond for the construction of models. While the models which consist of only time series data for corporate bond yield were developed, the other models generated through conjunction and reorganization of fundamantal variables and market variables were developed. Every models were reconstructed to predict 1, 3, 6, 12 months after and we obtained 16 artificial neural network models for interest rate forecasting. The 132 neural networks were learned for searching optimal model. Multi-layer perceptron networks using backpropagation algorithm showed good performance in the prediction for 1 month after. The RMSE was through 0.238 to 0.593 and artificial neural network models were better performance with 5% significant level by t-test. In the prediction for 3 months after, the combining model with the fundamental and intermarket varibles(NN4) showed significantly the good performance in forecasting the interest rate. The determination of how long period to forecast is very important factor in constructing the interest rate forecast models.한국과학기술원 : 경영정보공학과

인터넷상에서의 정량적, 정성적 정보를 이용한 지식기반 데이터마이닝에 관한 연구

학위논문(박사) - 한국과학기술원 : 경영공학전공, 2002.8, [ viii, 165 p. ]Neural networks have shown considerable success in modeling financial data series. Neural networks have the ability to scan the data for patterns and can be used to construct nonlinear models. However, in financial data series forecasting, most neural network models are constructed on the basis of only quantitative information such as macroeconomic data. We divided the information, which affect the financial data series, into two categories, quantitative and qualitative factors. We proposed a method for integrating cognitive maps and neural networks to gain competitive advantage using qualitative information acquired from news information on the World Wide Web. We investigate ways to apply news information on the internet to the prediction of interest rates. We developed the KBNMiner (Knowledge-Based News Miner), which is designed to represent the knowledge of interest rate experts with cognitive maps (CMs), to search and retrieve news information on the internet according to prior knowledge, and to apply the information, which is retrieved from news information, to a neural network model for the prediction of interest rates. Our study focuses on improving the performance of data mining by using qualitative information. Real-world interest rate prediction data is used to illustrate the performance of the KBNMiner. Our integrated approach, which utilizes CMs and neural networks, has been shown to be effective in experiments. While the 10-fold cross validation is used to test our research model, the experimental results of the paired t-test have been found to be statistically significant. The 10-fold cross validation is used to test our research model and the experimental results of the paired t-test have been found to be statistically significant.한국과학기술원 : 경영공학전공