Data Snooping in Equity Premium Prediction

47 Pages Posted: 22 May 2017 Last revised: 20 Jan 2019

See all articles by Hubert Dichtl

Hubert Dichtl

dichtl research & consulting GmbH

Wolfgang Drobetz

Hamburg University

Andreas Neuhierl

University of Notre Dame - Department of Finance

Viktoria-Sophie Wendt

University of Hamburg

Date Written: January 18, 2019

Abstract

We study the performance of a comprehensive set of equity premium forecasting strategies. All of these strategies have been found to outperform the mean in previous academic publications. However, using a multiple testing framework to account for data snooping, our findings support Goyal and Welch (2008) – 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 (January 18, 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

Hamburg University ( email )

Moorweidenstrasse 18
Hamburg, 20148
Germany

Andreas Neuhierl

University of Notre Dame - Department of Finance ( email )

P.O. Box 399
Notre Dame, IN 46556-0399
United States

Viktoria-Sophie Wendt (Contact Author)

University of Hamburg ( email )

Moorweidenstr. 18
Hamburg, 20148
Germany

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