Predicting Returns Out of Sample: A Naïve Model Averaging Approach

68 Pages Posted: 27 Sep 2019 Last revised: 19 Nov 2020

See all articles by Huafeng (Jason) Chen

Huafeng (Jason) Chen

Fudan University - Fanhai International School of Finance (FISF)

Liang Jiang

Fudan University - Fanhai International School of Finance (FISF)

Weiwei Liu

Tsinghua University - PBC School of Finance (PBCSF)

Date Written: November 3, 2020

Abstract

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

Chen, Huafeng (Jason) and Jiang, Liang and Liu, Weiwei, Predicting Returns Out of Sample: A Naïve Model Averaging Approach (November 3, 2020). Available at SSRN: https://ssrn.com/abstract=3455866 or http://dx.doi.org/10.2139/ssrn.3455866

Huafeng (Jason) Chen

Fudan University - Fanhai International School of Finance (FISF) ( email )

220 Handan Road
Shanghai, 200433
China

Liang Jiang

Fudan University - Fanhai International School of Finance (FISF) ( email )

220 Handan Road
Shanghai, 200433
China

Weiwei Liu (Contact Author)

Tsinghua University - PBC School of Finance (PBCSF) ( email )

Beijing, Haidian Distreet, Chengfu Road NO.43
Beijing, 100083
China
15600325656 (Phone)

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