Out-of-sample equity premium predictability and sample split-invariant inference

Article


Kolev, G. and Karapandza, R. 2017. Out-of-sample equity premium predictability and sample split-invariant inference. Journal of Banking and Finance. 84, pp. 188-201. https://doi.org/10.1016/j.jbankfin.2016.07.017
TypeArticle
TitleOut-of-sample equity premium predictability and sample split-invariant inference
AuthorsKolev, G. and Karapandza, R.
Abstract

For a comprehensive set of 21 equity premium predictors we find extreme variation in out-of-sample predictability results depending on the choice of the sample split date. To resolve this issue we propose reporting in graphical form the out-of-sample predictability criteria for every possible sample split, and two out-of-sample tests that are invariant to the sample split choice. We provide Monte Carlo evidence that our bootstrap-based inference is valid. The in-sample, and the sample split invariant out-of-sample mean and maximum tests that we propose, are in broad agreement. Finally we demonstrate how one can construct sample split invariant out-of-sample predictability tests that simultaneously control for data mining across many variables.

LanguageEnglish
PublisherElsevier
JournalJournal of Banking and Finance
ISSN0378-4266
Publication dates
Online21 Oct 2016
Print01 Nov 2017
Publication process dates
Deposited19 Oct 2016
Accepted11 Jul 2016
Output statusPublished
Publisher's version
License
Accepted author manuscript
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Copyright Statement

© 2016 The Author(s). Published by Elsevier B.V. The author's accepted manuscript and published version are made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

Digital Object Identifier (DOI)https://doi.org/10.1016/j.jbankfin.2016.07.017
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