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Default Risk Modeling and Machine Learning : 강건우

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dc.contributor.advisorChoi, Hyeong-In-
dc.contributor.authorJianyuKang-
dc.date.accessioned2017-07-19T09:00:18Z-
dc.date.available2017-07-19T09:00:18Z-
dc.date.issued2014-08-
dc.identifier.other000000022067-
dc.identifier.urihttps://hdl.handle.net/10371/131491-
dc.description학위논문 (석사)-- 서울대학교 대학원 : 수리과학부, 2014. 8. 최형인.-
dc.description.abstractThis paper will be focused on applying machine learning to predict the possibilities for firms to default. The data selected for this modeling are firms from United States between 2008 and 2012. We will use logistic regression and support vector machines, two major classification model from machine learning to forecast the risk of default. The result will be compared when different features are selected. Furthermore, we will discuss the strength of each method by comparing the result.-
dc.description.tableofcontents1. Introduction 1
2. Machine Learning Methods 3
2.1 Logistic Regression 4
2.2 Support Vector Machine 8
3. Data and method apply 13
3.1 Data selection 13
3.2 Feature selection 14
3.3 Measurements 15
4. Results and analysis 17
4.1 General result and analysis 17
4.2 Practical Analysis 19
5. Conclusion 23
Bibliography 24
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dc.formatapplication/pdf-
dc.format.extent838726 bytes-
dc.format.mediumapplication/pdf-
dc.language.isoen-
dc.publisher서울대학교 대학원-
dc.subjectdefault risk prediction-
dc.subjectmachine learning-
dc.subjectlogistic regression-
dc.subjectsupport vector machines-
dc.subjectstock prices-
dc.subject.ddc510-
dc.titleDefault Risk Modeling and Machine Learning-
dc.title.alternative강건우-
dc.typeThesis-
dc.description.degreeMaster-
dc.citation.pages30-
dc.contributor.affiliation자연과학대학 수리과학부-
dc.date.awarded2014-08-
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