Factor Investing: Hierarchical Ensemble Learning

29 Pages Posted: 6 Feb 2019

See all articles by Guanhao Feng

Guanhao Feng

City University of Hong Kong (CityUHK)

Jingyu He

University of Chicago - Booth School of Business - Econometrics and Statistics

Date Written: January 31, 2019

Abstract

We present a Bayesian hierarchical framework for both cross-sectional and time-series return prediction. Our approach builds on a market-timing predictive system that jointly allows for time-varying coefficients driven by fundamental characteristics. With a Bayesian formulation for ensemble learning, we examine the joint predictability as well as portfolio efficiency via predictive distribution. In the empirical analysis of asset-sector allocation, our hierarchical ensemble learning portfolio achieves 500% cumulative returns in the period 1998-2017, and outperforms most workhorse benchmarks as well as the passive investing index. Our Bayesian inference for model selection identifies useful macro predictors (long-term yield, inflation, and stock market variance) and asset characteristics (dividend yield, accrual, and gross profit). Using the selected model for predicting sector evolution, an equally weighted long-short portfolio on winners over losers achieves a 46% Sharpe ratio with a significant Jensen’s alpha. Finally, we explore an underexploited connection between classical Bayesian forecasting and modern ensemble learning.

Keywords: Hierarchical Model, Firm Characteristics, Market Timing, Portfolio Efficiency, Return Predictability, Risk Anomalies, Seemingly Unrelated Regressions

JEL Classification: C1, G1

Suggested Citation

Feng, Guanhao and He, Jingyu, Factor Investing: Hierarchical Ensemble Learning (January 31, 2019). 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 (CityUHK) ( email )

83 Tat Chee Avenue
Kowloon Tong
Hong Kong

Jingyu He

University of Chicago - Booth School of Business - Econometrics and Statistics ( email )

Chicago, IL 60637
United States

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