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Portfolio Selection Strategy Using Deep Learning
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딥 러닝을 이용한 포트폴리오 구성 전략

논문 기본 정보

Type
Academic journal
Author
Journal
한국엔터프라이즈아키텍처학회 정보기술아키텍처 연구 정보기술아키텍처 연구 제15권 제1호 KCI Accredited Journals
Published
2018.1
Pages
43 - 50 (8page)

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Portfolio Selection Strategy Using Deep Learning
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Abstract· Keywords

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This study proposes a strategy that applies deep learning methodology to portfolioselection problem in finance. While traditional portfolio selection is based on Markowitz theory,it faces a number of practical issues. However, portfolio selection strategy using deep learning isnot only a model-free approach without regard to specific model but also has a strength withrobust problem solving ability. Therefore, this study presents a deep portfolio algorithm consistingof 4-phases: autoencoding, validation, test and verification, and analyzes its efficacy withan ETF listed in KRX(Korea Exchange). The ETF that we target is in the category of Bio/lif science. From the perspective of portfolio diversification, putting together the most communalstocks and the most non-communal stocks in the same portfolio is turned out to make superioroutcome.
#Deep learning #portfolio selection strategy #ETF #autoencoder #artificial intelligence

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References (16)

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김건희 / 2016 / 고성능 딥 러닝 시스템 / 한국정보과학회 34(2):57~62 dbpia 박원기 / 2017 / 딥러닝 소개와 금융업 적용 사례 google schola 박재연 / 2016 / 로보어드바이저를 이용한 포트폴리오 관리 / 정보화연구 13(3):467~476 dbpia 임유진 / 2015 / 시계열 빅데이터 처리 분석을 위한 맵리듀스 메커니즘 연구 / 정보화연구 12(1):91~98 dbpia 유재필 / 2012 / 재고통제기법을 이용한 효율적 포트폴리오 관리 방안 / 산업공학(IE interfaces) 25(2):163~169 google schola

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