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Data Snooping in Equity Premium Prediction

39 Pages Posted: 22 May 2017 Last revised: 13 Jun 2017

Viktoria-Sophie Bartsch

University of Hamburg

Hubert Dichtl

dichtl research & consulting GmbH

Wolfgang Drobetz

University of Hamburg

Andreas Neuhierl

University of Notre Dame - Department of Finance

Date Written: May 22, 2017

Abstract

We study the performance of a comprehensive set of equity premium forecasting strategies that have been shown to outperform the historical mean out-of-sample when tested in isolation. Using a multiple testing framework, we find that previous evidence on out-of-sample predictability is primarily due to data snooping. We are not able to identify any forecasting strategy that produces robust and statistically significant economic gains after controlling for data snooping biases and transaction costs. By focusing on the application of equity premium prediction, our findings support Harvey’s (2017) more general concern that many of the published results in financial economics will fail to hold up.

Keywords: Equity risk premium prediction, data snooping bias

JEL Classification: G11, G12, G14

Suggested Citation

Bartsch, Viktoria-Sophie and Dichtl, Hubert and Drobetz, Wolfgang and Neuhierl, Andreas, Data Snooping in Equity Premium Prediction (May 22, 2017). Available at SSRN: https://ssrn.com/abstract=2972011

Viktoria-Sophie Bartsch (Contact Author)

University of Hamburg ( email )

Moorweidenstr. 18
Hamburg, 20148
Germany

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 )

Von-Melle-Park 5
Hamburg, 20146
Germany

Andreas Neuhierl

University of Notre Dame - Department of Finance ( email )

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

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