Non-Standard Errors in Portfolio Sorts

Abstract

We study the size and drivers of non-standard errors (Menkveld et al., 2021) in portfolio sorts across 14 common methodological decision nodes and 40 sorting variables. These non-standard errors range between 0.05 and 0.26 percent and are, on average, larger than standard errors. Supposedly innocuous decisions cause large variation in estimated premiums, standard errors, non-standard errors, and t-statistics. The impact of decision nodes varies widely across sorting variables. Irrespective of choices in portfolio sorts, we find pervasively positive premiums and alphas for almost all sorting variables. This suggests that while the size of these premiums is uncertain, their sign is remarkably stable. Our code is publicly available.

Keywords: Non-standard errors, portfolio sorts, data mining, p-hacking, risk factors, anomalies

JEL Classification: C58, G10, G11, G12, G14