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

Gueorgui I. Kolev, Rasa Karapandza

Research output: Contribution to journalArticle

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 (US)
Pages (from-to)188-201
Number of pages14
JournalJournal of Banking and Finance
Volume84
DOIs
StatePublished - Nov 1 2017

Fingerprint

Equity premium
Inference
Predictability
Bootstrap
Predictors
Data mining

Keywords

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

ASJC Scopus subject areas

  • Finance
  • Economics and Econometrics

Cite this

Out-of-sample equity premium predictability and sample split–invariant inference. / Kolev, Gueorgui I.; Karapandza, Rasa.

In: Journal of Banking and Finance, Vol. 84, 01.11.2017, p. 188-201.

Research output: Contribution to journalArticle

Kolev, Gueorgui I. ; Karapandza, Rasa. / Out-of-sample equity premium predictability and sample split–invariant inference. In: Journal of Banking and Finance. 2017 ; Vol. 84. pp. 188-201.
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