Modeling the Cross Section of Stock Returns Using Sensible Models in a Model Pool

39 Pages Posted: 4 Nov 2017 Last revised: 25 Nov 2020

See all articles by I-Hsuan Ethan Chiang

I-Hsuan Ethan Chiang

University of North Carolina (UNC) at Charlotte

Yin Liao

Macquarie University - Department of Applied Finance and Actuarial Studies; Australian National University - The Centre for Applied Macroeconomic Analysis (CAMA)

Qing Zhou

Department of Applied Finance and Actuarial Studies, Macquarie University

Date Written: November 25, 2020

Abstract

An increase in the number of asset pricing models intensifies model uncertainties in asset
pricing. While a pure "model selection" (singling out a best model) can result in a loss of useful
information, a full “model pooling” may increase the risk of including noisy information.
We make a trade-off between the two methods and develop a new two-step trimming-then-pooling
method to forecast the joint distributions of asset returns using a large pool of asset
pricing models. Our method allows investors to focus on certain regions of the distributions.
In the first step, we trim the uninformative models from a pool of candidates, and
in the second step, we pool the forecasts of the surviving models. We find that our method
significantly enhances portfolio performance and predicts downside risk precisely, and the
improvements are mainly due to trimming. The pool of sensible models becomes larger
when focusing on extreme events, responds rapidly to rising uncertainty, and reflects the
magnitude of factor premiums. These findings provide new insights into asset pricing model
evaluation.

Keywords: asset pricing model; model uncertainty; model confidence set; model pooling; model selection; joint density forecast

JEL Classification: C52; C53; C58; G12

Suggested Citation

Chiang, I-Hsuan Ethan and Liao, Yin and Zhou, Qing, Modeling the Cross Section of Stock Returns Using Sensible Models in a Model Pool (November 25, 2020). Journal of Empirical Finance, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3064014 or http://dx.doi.org/10.2139/ssrn.3064014

I-Hsuan Ethan Chiang (Contact Author)

University of North Carolina (UNC) at Charlotte ( email )

9201 University City Boulevard
Charlotte, NC 28223
United States

HOME PAGE: http://go.uncc.edu/chiang

Yin Liao

Macquarie University - Department of Applied Finance and Actuarial Studies ( email )

Eastern Rd.
North Ryde
Sydney, NSW 2109
United States

Australian National University - The Centre for Applied Macroeconomic Analysis (CAMA) ( email )

Canberra, Australian Capital Territory 2601
Australia

Qing Zhou

Department of Applied Finance and Actuarial Studies, Macquarie University ( email )

Sydney, NSW
Australia

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

Paper statistics

Downloads
93
Abstract Views
732
rank
341,987
PlumX Metrics