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Cited by 336

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Vast Portfolio Selection With Gross-Exposure Constraints

Open access
Fan, Jianqing, Zhang, Jingjin, Yu, Ke·2012·Journal of the American Statistical Association

Abstract

We introduce the large portfolio selection using gross-exposure constraints. We show that with gross-exposure constraint the empirically selected optimal portfolios based on estimated covariance matrices have similar performance to the theoretical optimal ones and there is no error accumulation effect from estimation of vast covariance matrices. This gives theoretical justification to the empirical results in Jagannathan and Ma (2003). We also show that the no-short-sale portfolio can be improved by allowing some short positions. The applications to portfolio selection, tracking, and improvements are also addressed. The utility of our new approach is illustrated by simulation and empirical studies on the 100 Fama-French industrial portfolios and the 600 stocks randomly selected from Russell 3000.

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Crossref · 10.1080/01621459.2012.682825

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Metadata

Authors
Fan, J., Zhang, J., & Yu, K.
Venue
Journal of the American Statistical Association
Published
2012-06
Volume
107
Issue
498
Pages
592-606
ISSN
  • 0162-1459 (ISSN-L)
  • 1537-274X
OpenAlex
W2134056163
Cited by
336
References
3
FWCI
22.68
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Vast Portfolio Selection With Gross-Exposure Constraints. https://app.cochat.ai/papers/10.1080/01621459.2012.682825
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Fan, J., Zhang, J., & Yu, K. (2012). Vast Portfolio Selection With Gross-Exposure Constraints. Journal of the American Statistical Association. https://doi.org/10.1080/01621459.2012.682825

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.