Growing the Efficient Frontier on Panel Trees
(joint with Will Cong, Jingyu He, and Xin He)
Prof. Gavin, Guanhao FENG
Assistant Professor of Business Statistics
City University of Hong Kong
Date: 16 November 2023 (Thursday)
Time: 10:30 am to 12:00 pm
Host: Prof. Yi DING, Assistant Professor in Business Intelligence and Analytics
Online registration: https://umac.au1.qualtrics.com/jfe/form/SV_aftnv5qvqZBFa7k
We develop a new class of tree-based models (P-Trees) for analyzing (unbalanced) panel data using economically guided, global (instead of local) split criteria that guard against overfitting while preserving interpretability. To generalize security sorting and better estimate the efficient frontier, we grow a P-Tree top-down to split the cross section of asset returns to construct stochastic discount factors and test assets under the MVE framework, visualizing (asymmetric) nonlinear interactions among firm characteristics (and with macroeconomic states). When applied to U.S. equities and especially when boosted, P-Trees significantly advance the efficient frontier relative to those constructed with established factors and common test assets. Diversified P-Tree test portfolios exhibit significant unexplained alphas against benchmark factor models. P-Trees also outperforms most known observable and latent factor models in pricing cross-sectional returns, delivering transparent trading strategies, and generating risk-adjusted investment outcomes. Beyond asset pricing, our framework offers a more interpretable and computationally efficient alternative to recent machine learning and AI models for analyzing panel data through goal-oriented, high-dimensional clustering.
Guanhao (Gavin) Feng is an assistant professor of business statistics at the City University of Hong Kong. He is also the program leader of MSc. in Business Data Analytics, a faculty affiliate at the School of Data Science, and a scientist in the Lab for AI-Powered FinTech. Gavin’s research publications have appeared in the Journal of Finance, Journal of Financial and Quantitative Analysis, Journal of Econometrics, and International Economic Review. Gavin obtained his Ph.D. and MBA from the University of Chicago in 2017. His research interests include Bayesian statistics, empirical asset pricing, machine learning in finance, and time-varying econometrics.
All are welcome!