Sparse and robust portfolio selection via semi-definite relaxation = 반 확정 완화 기법을 통한 희소-강건 포트폴리오 최적화

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For portfolio management in the real-world, it is required that a portfolio has a manageable number of assets and stable performance. However, much research has pointed out that the Markowitz model, which is a classical model in portfolio theory, forms a portfolio with many different assets that may have unstable performance. Therefore, in this paper, we focus on developing a portfolio selection model which constructs a sparse and robust optimal portfolio. In order to achieve our research goal, we introduce two kinds of optimization problems. The first one is a $L_2$ -norm regularized cardinality constraint portfolio and the second one is cardinality constrained robust optimization portfolio with ellipsoidal uncertainty set. Moreover, we formulate a convex optimization problem for these proposed models using semi-definite relaxation. The outcomes of our empirical tests show that portfolios obtained by our model have smaller cardinalities and better out-of-sample performances than those of cardinality constrained Markowitz optimal portfolios. A large part of financial business is now being automated. Our portfolios give the investors new opportunity to obtain the desired properties; sparsity and robustness.
Advisors
Kim, Woo Changresearcher김우창researcher
Description
한국과학기술원 :산업및시스템공학과,
Publisher
한국과학기술원
Issue Date
2016
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 산업및시스템공학과, 2016.8 ,[iv, 32 p. :]

Keywords

Portfolio selection; Sparse portfolio; Robust optimization; $L_2$ -norm regularization; Semi-definite relaxation; 자산선택; 희소 포트폴리오; 로버스트 최적화; 놈 정규화; 반 확정 완화 기법

URI
http://hdl.handle.net/10203/221450
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=663367&flag=dissertation
Appears in Collection
IE-Theses_Master(석사논문)
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