B1559
Title: Beta sorted portfolio
Authors: Weining Wang - University of York (United Kingdom) [presenting]
Abstract: Beta-sorted portfolios comprising assets with similar covariation to selected risk factors are a popular tool in empirical finance to analyze models of (conditional) expected returns. Despite their widespread use, little is known of their statistical properties in contrast to comparable procedures such as two-pass regressions. We formally investigate the properties of beta-sorted portfolio returns by casting the procedure as a two-step nonparametric estimator with a nonparametric first step and a beta-adaptive portfolio construction. The framework rationalizes the well-known estimation algorithm with precise economic and statistical assumptions on the general data-generating process and characterizes its key features. We study beta-sorted portfolios for both a single cross-section as well as for aggregation over time (e.g., the grandmean), offering conditions that ensure consistency and asymptotic normality along with new uniform inference procedures allowing for uncertainty quantification and testing of various relevant hypotheses in financial applications. We also highlight some limitations of current empirical practices and discuss what inferences can and cannot be drawn from returns to beta-sorted portfolios for either a single cross-section or across the whole sample. Finally, we illustrate the functionality of our new procedures in an empirical application
