Understanding the Performance of Components in Betting Against Beta

Forthcoming, Critical Finance Review

50 Pages Posted: 27 Nov 2018 Last revised: 25 Sep 2019

See all articles by Xing Han

Xing Han

University of Auckland Business School; Ghent University - Department of Financial Economics

Date Written: August 15, 2019

Abstract

Betting against beta (BAB) can be seen as the combination of three investable component portfolios: Two cross-sectional components exploiting the beta anomaly attributable to stock selection and rank weighting scheme, and one time-series component with a dynamic net-long position due to “beta-parity”. Virtually all superior performance of BAB stems from the time-series component. The two cross-sectional components only provide hedging benefits in market downturns. The time-series component has modest portfolio turnover. Betting against correlation (BAC) yields similar findings, except that the two cross-sectional components in BAC outperform on a risk-adjusted basis. However, this effect arises purely from the positive association between firm size and stock correlation. Excluding micro-cap stocks, the performance of BAC shrinks more than that of BAB. Overall, only the time-series component remains as the robust source for the profits of the BA-type strategies.

Keywords: Beta Anomaly, Return Decomposition, Betting Against Correlation, Asset Pricing

JEL Classification: G11, G12

Suggested Citation

Han, Xing, Understanding the Performance of Components in Betting Against Beta (August 15, 2019). Forthcoming, Critical Finance Review. Available at SSRN: https://ssrn.com/abstract=3286500 or http://dx.doi.org/10.2139/ssrn.3286500

Xing Han (Contact Author)

University of Auckland Business School ( email )

Private Bag 92019
Auckland Mail Centre
Auckland, 1142
New Zealand

Ghent University - Department of Financial Economics ( email )

Sint-Pietersplein 5
Ghent, 9000
Belgium

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
59
Abstract Views
333
rank
376,496
PlumX Metrics