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Default Risk Modeling and Machine Learning
강건우

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Authors
JianyuKang
Advisor
Choi, Hyeong-In
Major
자연과학대학 수리과학부
Issue Date
2014-08
Publisher
서울대학교 대학원
Keywords
default risk predictionmachine learninglogistic regressionsupport vector machinesstock prices
Description
학위논문 (석사)-- 서울대학교 대학원 : 수리과학부, 2014. 8. 최형인.
Abstract
This 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.
Language
English
URI
http://hdl.handle.net/10371/131491
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College of Natural Sciences (자연과학대학)Dept. of Mathematical Sciences (수리과학부)Theses (Master's Degree_수리과학부)
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