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Optimal and naive diversification in an emerging market: evidence from China's A‐shares market

Yan, Cheng and Yan, Ji (2021) 'Optimal and naive diversification in an emerging market: evidence from China's A‐shares market.', International journal of finance & economics., 26 (3). pp. 3740-3758.

Abstract

This paper empirically investigates the out‐of‐sample performance of the 1/N naive rule and the Markowitz mean–variance strategies in the largest emerging market (i.e., China's A‐shares market) and provides three new findings. First, we show that some mean–variance optimization strategies can outperform the 1/N rule in China's A‐shares market, while minimum‐variance strategies cannot. Using certainty equivalent return (CER) instead of Sharpe ratios does not change our results qualitatively. Second, we find an obvious advantage of mean–variance optimization when N is large. Third, when transaction costs are taken into account, the profitability of the unconstrained mean–variance optimizations almost vanishes, while the profitability of the mean–variance optimizations with the short‐sale constraint remains. Our results are robust to using a shorter estimation window of about 60 months. These results provide support for the use of optimal diversification strategies in emerging markets.

Item Type:Article
Full text:Publisher-imposed embargo until 08 October 2022.
(AM) Accepted Manuscript
File format - PDF
(450Kb)
Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1002/ijfe.1984
Publisher statement:This is the peer reviewed version of the following article: Yan, Cheng & Yan, Ji (2021). Optimal and naive diversification in an emerging market: Evidence from China's A‐shares market. International Journal of Finance & Economics 26(3): 3740-3758., which has been published in final form at https://doi.org/10.1002/ijfe.1984. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.
Date accepted:18 June 2020
Date deposited:05 November 2020
Date of first online publication:08 October 2020
Date first made open access:08 October 2022

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