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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