Approaching Mean-Variance Efficiency for Large Portfolios

69 Pages Posted: 6 Dec 2015 Last revised: 19 Jul 2018

Mengmeng Ao

Xiamen University - WISE and Department of Finance, School of Economics

Yingying Li

Hong Kong University of Science & Technology (HKUST), Dept of ISOM and Dept of Finance; Hong Kong University of Science & Technology (HKUST) - Department of Information Systems, Business Statistics and Operations Management; Hong Kong University of Science & Technology (HKUST) - Department of Finance

Xinghua Zheng

Hong Kong University of Science & Technology (HKUST) - Department of Information Systems, Business Statistics and Operations Management

Date Written: July 7, 2018

Abstract

This paper introduces a new approach to constructing optimal mean-variance portfolios. The approach relies on a novel unconstrained regression representation of the mean-variance optimization problem combined with high-dimensional sparse-regression methods. Our estimated portfolio, under a mild sparsity assumption, controls the risk and attains the maximum expected return as both the numbers of assets and observations grow. The superior properties of our approach are demonstrated through comprehensive simulation and empirical analysis. Notably, we fi nd that investing in individual stocks in addition to the Fama-French three factor portfolios using our strategy leads to substantially improved performance.

Keywords: Large portfolio selection; Mean-variance portfolio; Sharpe ratio; Unconstrained regression; LASSO

JEL Classification: G11; C10

Suggested Citation

Ao, Mengmeng and Li, Yingying and Zheng, Xinghua, Approaching Mean-Variance Efficiency for Large Portfolios (July 7, 2018). Available at SSRN: https://ssrn.com/abstract=2699157 or http://dx.doi.org/10.2139/ssrn.2699157

Mengmeng Ao (Contact Author)

Xiamen University - WISE and Department of Finance, School of Economics ( email )

Xiamen, Fujian
China

Yingying Li

Hong Kong University of Science & Technology (HKUST), Dept of ISOM and Dept of Finance ( email )

Clear Water Bay, Kowloon
Hong Kong

Hong Kong University of Science & Technology (HKUST) - Department of Information Systems, Business Statistics and Operations Management ( email )

Clear Water Bay
Kowloon
Hong Kong

Hong Kong University of Science & Technology (HKUST) - Department of Finance ( email )

Clear Water Bay, Kowloon
Hong Kong

Xinghua Zheng

Hong Kong University of Science & Technology (HKUST) - Department of Information Systems, Business Statistics and Operations Management ( email )

Clear Water Bay
Kowloon
Hong Kong

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