Data Snooping in Equity Premium Prediction

49 Pages Posted: 22 May 2017 Last revised: 11 Dec 2019

See all articles by Hubert Dichtl

Hubert Dichtl

dichtl research & consulting GmbH

Wolfgang Drobetz

University of Hamburg

Andreas Neuhierl

Washington University in St. Louis - John M. Olin Business School

Viktoria-Sophie Wendt

University of Hamburg

Date Written: November 24, 2019

Abstract

We analyze the performance of a comprehensive set of equity premium forecasting strategies. All strategies were found to outperform the mean in previous academic publications. However, using a multiple testing framework to account for data snooping, our findings support Welch and Goyal (2008) in that almost all equity premium forecasts fail to beat the mean out-of-sample. Only few forecasting strategies that are based on Ferreira and Santa-Clara’s (2011) “sum-of-the-parts” approach generate robust and statistically significant economic gains relative to the historical mean even after controlling for data snooping and accounting for transaction costs.

Keywords: Equity risk premium prediction, data snooping bias

JEL Classification: G11, G12, G14

Suggested Citation

Dichtl, Hubert and Drobetz, Wolfgang and Neuhierl, Andreas and Wendt, Viktoria-Sophie, Data Snooping in Equity Premium Prediction (November 24, 2019). Available at SSRN: https://ssrn.com/abstract=2972011 or http://dx.doi.org/10.2139/ssrn.2972011

Hubert Dichtl

dichtl research & consulting GmbH ( email )

Am Bahnhof 7
65812 Bad Soden am Taunus
Germany

HOME PAGE: http://www.dichtl-rc.de

Wolfgang Drobetz

University of Hamburg ( email )

Moorweidenstrasse 18
Hamburg, 20148
Germany

Andreas Neuhierl

Washington University in St. Louis - John M. Olin Business School ( email )

St. Louis, MO
United States

Viktoria-Sophie Wendt (Contact Author)

University of Hamburg ( email )

Moorweidenstr. 18
Hamburg, 20148
Germany

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
373
Abstract Views
1,685
rank
88,725
PlumX Metrics