Factor Investing: A Bayesian Hierarchical Approach

38 Pages Posted: 6 Feb 2019 Last revised: 7 Oct 2022

See all articles by Guanhao Feng

Guanhao Feng

City University of Hong Kong (CityU)

Jingyu He

City University of Hong Kong (CityU)

Date Written: November 2021

Abstract

This paper investigates the asset allocation problem when returns are predictable. We introduce a market-timing Bayesian hierarchical (BH) approach that adopts heterogeneous time-varying coefficients driven by lagged fundamental characteristics. Our approach estimates the conditional expected returns and residual covariance matrix jointly enables evaluating the estimation risk in the portfolio analysis. The hierarchical prior allows the modeling of different assets separately while sharing information across assets. We demonstrate the performance of the U.S. equity market, and our BH approach outperforms most alternative methods in terms of point prediction and interval coverage. In addition, the BH efficient portfolio achieves monthly returns of 0.92% and a significant Jensen's alpha of 0.32% in sector investment over the past twenty years. We detect that technology, energy, and manufacturing are the most critical sectors in the past decade, and size, investment, and short-term reversal factors are heavily weighted in our portfolio. Furthermore, the stochastic discount factor constructed by our BH approach can explain many risk anomalies.

Keywords: Asset Allocation, Bayes, Hierarchical Prior, Estimation Risk, Characteristics, Macro Predictors, Risk Factor

JEL Classification: C1, G1

Suggested Citation

Feng, Guanhao and He, Jingyu, Factor Investing: A Bayesian Hierarchical Approach (November 2021). Journal of Econometrics, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3326617 or http://dx.doi.org/10.2139/ssrn.3326617

Guanhao Feng (Contact Author)

City University of Hong Kong (CityU) ( email )

83 Tat Chee Avenue
Kowloon Tong
Hong Kong

Jingyu He

City University of Hong Kong (CityU) ( email )

83 Tat Chee Avenue
Hong Kong
Hong Kong

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