DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김우창 | ko |
dc.date.accessioned | 2013-03-12T14:24:59Z | - |
dc.date.available | 2013-03-12T14:24:59Z | - |
dc.date.created | 2013-01-10 | - |
dc.date.created | 2013-01-10 | - |
dc.date.issued | 2012-12 | - |
dc.identifier.citation | 대한산업공학회지, v.38, no.4, pp.258 - 261 | - |
dc.identifier.issn | 1225-0988 | - |
dc.identifier.uri | http://hdl.handle.net/10203/102580 | - |
dc.description.abstract | In this paper, I propose a new asset allocation framework to cope with the dynamic nature of the financial market. The investment performance can be much improved by protecting the capital from the market crashes, and such crashes can be pre-identified with high probabilities by regime detection analysis via a specialized unsupervised machine learning technique | - |
dc.language | Korean | - |
dc.publisher | 대한산업공학회 | - |
dc.title | Dynamic Asset Allocation by Applying Regime Detection Analysis | - |
dc.title.alternative | Regime 탐지 분석을 이용한 동적 자산 배분 기법 | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.citation.volume | 38 | - |
dc.citation.issue | 4 | - |
dc.citation.beginningpage | 258 | - |
dc.citation.endingpage | 261 | - |
dc.citation.publicationname | 대한산업공학회지 | - |
dc.identifier.kciid | ART001714843 | - |
dc.contributor.localauthor | 김우창 | - |
dc.subject.keywordAuthor | Asset Allocation | - |
dc.subject.keywordAuthor | Regime Detection | - |
dc.subject.keywordAuthor | Machine Learning | - |
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