Does Probability Weighting Drive Skewness Preferences?
34 Pages Posted: 14 Jul 2017 Last revised: 17 Apr 2018
Date Written: December 31, 2017
We propose a test of Barberis and Huang’s (2008) theory of skewness preferences. The probability weighting feature that is the basis of their theory relies on investors overweighting the probability of tail events. The resulting investor preferences for positive skewness in return distributions will lead to excess demand, contemporaneous price premiums, and negative expected returns. We use the well-documented 52-week high bias as a method to truncate the probability of expected right-tail events. We find evidence supporting the Barberis and Huang theory as the negative return premia associated with positive skewness are driven almost entirely by stocks that are farther away from the their 52-week high .
Keywords: Lotteries, Anchoring, Skewness, Behavioral Finance, Probability Weighting
JEL Classification: G02, G11, G12, G14
Suggested Citation: Suggested Citation