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


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
TitleOut-of-sample equity premium predictability and sample split-invariant inference
AuthorsKolev, G. and Karapandza, R.

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.

JournalJournal of Banking and Finance
Publication dates
Online21 Oct 2016
Print01 Nov 2017
Publication process dates
Deposited19 Oct 2016
Accepted11 Jul 2016
Output statusPublished
Publisher's version
Accepted author manuscript
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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