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 language | English (US) |
|---|---|
| Pages (from-to) | 188-201 |
| Number of pages | 14 |
| Journal | Journal of Banking and Finance |
| Volume | 84 |
| DOIs | |
| State | Published - Nov 1 2017 |
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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 journal › Article
}
TY - JOUR
T1 - Out-of-sample equity premium predictability and sample split–invariant inference
AU - Kolev, Gueorgui I.
AU - Karapandza, Rasa
PY - 2017/11/1
Y1 - 2017/11/1
N2 - 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.
AB - 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.
KW - Bootstrap
KW - Equity premium predictability
KW - Out-of-sample inference
KW - Sample split choice
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UR - http://www.scopus.com/inward/citedby.url?scp=85028601557&partnerID=8YFLogxK
U2 - 10.1016/j.jbankfin.2016.07.017
DO - 10.1016/j.jbankfin.2016.07.017
M3 - Article
AN - SCOPUS:85028601557
VL - 84
SP - 188
EP - 201
JO - Journal of Banking and Finance
JF - Journal of Banking and Finance
SN - 0378-4266
ER -