Uncommon Factors for Bayesian Asset Clusters

63 Pages Posted: 29 Sep 2022

See all articles by Lin William Cong

Lin William Cong

Cornell University - Samuel Curtis Johnson Graduate School of Management; National Bureau of Economic Research (NBER)

Guanhao Feng

City University of Hong Kong (CityU)

Jingyu He

City University of Hong Kong (CityU)

Junye Li

Fudan University - School of Management

Date Written: September 15, 2022

Abstract

Asset returns exhibit grouped heterogeneity and a ``one-size-fits-all" model has been elusive empirically. We propose a novel Bayesian Clustering Model (BCM) combining Bayesian variable selection and tree growth guided by the marginal likelihood of factor models, with each leaf cluster fitting an endogenously selected model with potential uncommon factors and dynamic betas. BCM is applied to split the cross section of U.S. individual stock returns, identifying market, size, and short-term reversal as common factors, and several uncommon factors that lose exposure to some clusters during tree growth. Differential factor exposure and potential segmentation manifest primarily through differential stock variance, followed by market equity and earnings-to-price ratio. Built on leaf clusters, a tangency portfolio on cluster-selected factor models deliver exceptional in-sample and out-of-sample performance. We further discover that more skeptical beliefs about factor usefulness produce better interval coverages, and lag market equity positively drives market betas in most clusters. Beyond asset pricing, our framework applies to general problems of jointly clustering (unbalanced) panel data and estimating heterogeneous models for the clusters.

Keywords: Bayesian Inference, Cross Section, Factor Selection, Self-Supervised Clustering, Spike-and-Slab.

JEL Classification: C1, G11, G12

Suggested Citation

Cong, Lin and Feng, Guanhao and He, Jingyu and Li, Junye, Uncommon Factors for Bayesian Asset Clusters (September 15, 2022). Available at SSRN: https://ssrn.com/abstract=4219905 or http://dx.doi.org/10.2139/ssrn.4219905

Lin Cong

Cornell University - Samuel Curtis Johnson Graduate School of Management ( email )

Ithaca, NY 14853
United States

HOME PAGE: http://www.linwilliamcong.com/

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Guanhao Feng

City University of Hong Kong (CityU) ( email )

83 Tat Chee Avenue
Kowloon Tong
Hong Kong

Jingyu He (Contact Author)

City University of Hong Kong (CityU) ( email )

83 Tat Chee Avenue
Hong Kong
Hong Kong

Junye Li

Fudan University - School of Management ( email )

No. 670, Guoshun Road
No.670 Guoshun Road
Shanghai, 200433
China

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