Factor Investing : A Bayesian Hierarchical Approach

Research output: Working PapersPreprint

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Original languageEnglish
PublisherSocial Science Research Network (SSRN)
Number of pages38
Publication statusOnline published - 6 Feb 2019


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.

Research Area(s)

  • Asset Allocation, Bayes, Hierarchical Prior, Estimation Risk, Characteristics, Macro Predictors, Risk factor

Bibliographic Note

Research Unit(s) information for this publication is provided by the author(s) concerned.

Citation Format(s)

Factor Investing : A Bayesian Hierarchical Approach. / Feng, Guanhao; He, Jingyu.

Social Science Research Network (SSRN), 2019.

Research output: Working PapersPreprint