Predicting Returns Out of Sample: A Naïve Model Averaging Approach

106 Pages Posted: 27 Sep 2019 Last revised: 18 Oct 2021

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: October 18, 2021

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 also helpful relative to other more sophisticated methods. Surprisingly, more sophisticated weighting schemes that combine the predictive variable and the historical mean do not consistently perform better than the NMA method. Model misspecification, rather than declining return predictability, likely explains 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 (October 18, 2021). 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)

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
65
Abstract Views
623
rank
423,914
PlumX Metrics