DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Kim, Steven H. | - |
dc.contributor.advisor | 김형관 | - |
dc.contributor.author | Kim, Kyung-Myun | - |
dc.contributor.author | 김경면 | - |
dc.date.accessioned | 2011-12-27T04:40:17Z | - |
dc.date.available | 2011-12-27T04:40:17Z | - |
dc.date.issued | 1996 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=109116&flag=dissertation | - |
dc.identifier.uri | http://hdl.handle.net/10203/53771 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 테크노경영대학원, 1996.8, [ vi, 55 p. ] | - |
dc.description.abstract | Statistical techniques and software learning methods have been used extensively to understand financial market behavior. Numerous studies reveal that each of these techniques can provide good results. However, an integrated methodology combining several multivariate techniques may offer even better performance. To explore this conjecture, a variety of integrated strategies and predictive methods were considered in forecasting 10-year Treasury notes. The preprocessing methods were as follows: straightforward standardization of input data, reduction of input variables by stepwise regression and factor analysis, selection of input variables`` lags by diagonal correlation analysis. The results in this study show that the neural network is not significantly more accurate than the linear regression for the data sets employed. The model using differenced data has significantly better results than other models. Among neural network models, the model using differenced data is significantly better than one which simply employs standardized data. The model using factored data also performs somewhat better than that using standardized data, although the difference is not statistically significant. Moreover, the neural network model employing the integrated procedure outperforms the neural network working in isolation. | eng |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Neural networks | - |
dc.subject | Multivariate statistics | - |
dc.subject | Interest rate prediction | - |
dc.subject | 이자율 예측 | - |
dc.subject | 인공 신경망 | - |
dc.subject | 다변량 통계 분석법 | - |
dc.title | Integrating multivariate statistics and neural networks for interest rate prediction | - |
dc.title.alternative | 이자율 예측을 위한 다변량 통계 분석법과 인공 신경망의 결합 : treasury notes를 대상으로 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 109116/325007 | - |
dc.description.department | 한국과학기술원 : 테크노경영대학원, | - |
dc.identifier.uid | 000947088 | - |
dc.contributor.localauthor | Kim, Steven H. | - |
dc.contributor.localauthor | 김형관 | - |
dc.title.subtitle | case study in 10-year treasury note | - |
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