Non-Standard Errors in Portfolio Sorts

67 Pages Posted: 25 Jul 2022

See all articles by Dominik Walter

Dominik Walter

Vienna Graduate School of Finance (VGSF)

Rüdiger Weber

WU Vienna; Vienna Graduate School of Finance (VGSF)

Patrick Weiss

Vienna University of Economics and Business - Department of Finance, Accounting & Statistics

Date Written: July 7, 2022

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

Suggested Citation

Walter, Dominik and Weber, Rüdiger and Weiss, Patrick, Non-Standard Errors in Portfolio Sorts (July 7, 2022). Available at SSRN: https://ssrn.com/abstract=4164117 or http://dx.doi.org/10.2139/ssrn.4164117

Dominik Walter

Vienna Graduate School of Finance (VGSF) ( email )

Welthandelsplatz 1
Vienna, 1020
Austria

Rüdiger Weber

WU Vienna ( email )

Welthandelsplatz 1 1
Wien, 1020
Austria

Vienna Graduate School of Finance (VGSF) ( email )

Welthandelsplatz 1
Vienna, 1020
Austria

Patrick Weiss (Contact Author)

Vienna University of Economics and Business - Department of Finance, Accounting & Statistics ( email )

Welthandelsplatz 1
Vienna, 1020
Austria
+43 1 313 36 / 5996 (Phone)

HOME PAGE: http://https://sites.google.com/view/patrick-weiss

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