Growing the Efficient Frontier on Panel Trees

Estimating the mean-variance efficient (MVE) frontier is crucial for asset pricing and investment management. Yet, estimating the tangency portfolio (Markowitz, 1952) using the unbalanced panel of thousands of individual asset returns proves impracticable. Empirical studies typically consider a "diversified" set of test assets (e.g., ME-BM 25 portfolios) to estimate and evaluate factor models, hoping these test assets or a few common factors can span the same efficient frontier as individual assets. However, popular factor models hardly explain the cross section of conventional prespecified test assets (e.g., Kozak et al., 2018; Lopez-Lira and Roussanov, 2020), not to mention the ad hoc nature of these test assets hampers the effectiveness of model estimations and evaluations (Lewellen et al., 2010; Ang et al., 2020). For example, characteristics-based test assets are often limited to univariate-and bivariate-sorted portfolios due to the challenges of high-dimensional sorting (Cochrane, 2011), overlooking nonlinearity and asymmetric interactions (that do not uniformly apply to all assets), even with dependent sorting (Daniel et al., 1997).