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논문 기본 정보

자료유형
학술저널
저자정보
Namhyoung Kim (Gachon University) Suvrit Sra (Max Planck Institute for Intelligent Systems, Tübingen, Germany)
저널정보
대한산업공학회 Industrial Engineering & Management Systems Industrial Engineering & Management Systems 제13권 제4호
발행연도
2014.12
수록면
442 - 448 (7page)

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초록· 키워드

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Portfolio optimization in the presence of estimation error can be stabilized by incorporating norm-constraints; this result was shown by DeMiguel et al. (A generalized approach to portfolio optimization: improving performance by constraining portfolio norms, Management Science, 5, 798-812, 2009), who reported empirical performance better than numerous competing approaches. We extend the idea of norm-constraints by introducing a powerful enhancement, grouped selection for portfolio optimization. Here, instead of merely penalizing norms of the assets being selected, we penalize groups, where within a group assets are treated alike, but across groups, the penalization may differ. The idea of groupwise selection is grounded in statistics, but to our knowledge, it is novel in the context of portfolio optimization. Novelty aside, the real benefits of groupwise selection are substantiated by experiments; our results show that groupwise asset selection leads to strategies with lower variance, higher Sharpe ratios, and even higher expected returns than the ordinary norm-constrained formulations.

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ABSTRACT
1. INTRODUCTION
2. PORTFOLIO OPTIMIZATION
3. PROPOSED APPROACH: CONSTRAINING GROUP NORM
4. EMPIRICAL RESULTS
5. CONCLUSION
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