Predicting Returns Out of Sample: A Naïve Model Averaging Approach
68 Pages Posted: 27 Sep 2019 Last revised: 20 Jan 2021
Date Written: November 3, 2020
Prior literature finds that variables that can forecast market returns in sample do not beat historical averages in forecasting market returns out of sample. We propose a naïve model averaging (NMA) method, which produces mostly positive out-of-sample R2s for the variables that are significant in sample. The NMA method is helpful even after we impose additional restrictions, as in Campbell and Thompson (2008). The Bayesian model averaging (BMA) approach does not perform better than the NMA method. Model misspecification, instead of declining return predictability, might explain the predictive performance of the NMA method.
Keywords: out of sample tests, Naïve model averaging, market returns, ridge regression
JEL Classification: G12, G11
Suggested Citation: Suggested Citation