Uncommon Factors for Bayesian Asset Clusters

68 Pages Posted: 29 Sep 2022 Last revised: 21 Nov 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. This paper proposes a Bayesian Clustering Model (BCM) combining Bayesian factor selection and panel tree for asset clustering. The Bayesian model marginal likelihood guides the tree growth for clustering assets, where each leaf cluster fits heterogeneous model selection and estimation. We apply BCM to split the cross section of U.S. individual stock returns, and find MktRF, SMB, and STR (short-term reversal) as common factors. We also identify several uncommon factors that are partially useful to some leaf clusters when splitting the cross section. The tree visualizes individual stock clustering with important splitting characteristics, such as stock variance and market equity. By considering different prior beliefs for factor usefulness, we further discover that factor models with more skeptic beliefs produce more accurate interval coverage. Beyond asset pricing, our framework generally applies to modeling grouped heterogeneity through jointly clustering panel data and variable selection.

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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