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

Gueorgui Kolev, Rasa Karapandza

Research output: Contribution to journalArticle

3 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)188-201
Number of pages14
JournalJournal of Banking and Finance
Volume84
Early online date21 Oct 2016
DOIs
Publication statusPublished - Nov 2017
Externally publishedYes

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Bibliographical note

© 2016 The Author(s). Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license.
(http://creativecommons.org/licenses/by-nc-nd/4.0/)

Keywords

  • Equity premium predictability
  • Out-of-sample inference
  • Sample split choice
  • Bootstrap

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