Maguire, Phil and Kelly, Stephen and Miller, Robert and Moser, Philippe and Hyland, Philip and Maguire, Phil (2017) Further evidence in support of a low-volatility anomaly: Optimizing buy-and-hold portfolios by minimizing historical aggregate volatility. Journal of Asset Management, 18 (4). pp. 326-339. ISSN 1479-179X

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Abstract

The ‘low-volatility anomaly’ is the counter-intuitive observation that portfolios of low-volatility stocks tend to yield higher risk-adjusted returns than portfolios of high-volatility stocks. In this article, we investigate if the anomaly holds, not only for portfolios consisting of individual low-volatility stocks, but for portfolios that have been optimized to minimize aggregate volatility. We exploit patterns in historical price fluctuations to identify optimized portfolios whose aggregate volatility is expected to remain low. These portfolios are evaluated by comparing them against the performance of market capitalization and low-volatility quintile benchmarks out-of-sample. The results reveal that, as well as outperforming the market, both in terms of returns and risk, optimized low-volatility strategies also outperform the S&P Low-Volatility Index. These findings provide further support for a low-volatility effect, and imply that the root of the anomaly may lie with a failure to exploit diversification opportunities.

Item Type:	Article
Additional Information:	Cite as: Maguire, P., Kelly, S., Miller, R. et al. J Asset Manag (2017) 18: 326. https://doi.org/10.1057/s41260-016-0036-1
Keywords:	low-volatility anomaly; portfolio optimization; buy-and-hold portfolio; variance minimization; diversification; out-of-sample testing;
Academic Unit:	Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Psychology
Item ID:	11639
Identification Number:	https://doi.org/10.1057/s41260-016-0036-1
Depositing User:	Rebecca Maguire
Date Deposited:	05 Nov 2019 16:40
Journal or Publication Title:	Journal of Asset Management
Publisher:	Palgrave Macmillan
Refereed:	Yes
URI:	https://link.springer.com
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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