Beta-Sorted Portfolios

Part of FinEML online seminar series
Dollar bill
Weining Wang
Friday 1 Mar 2024, 16:00 - 17:00
Online event
Zoom registration Add to calendar
Weining Wang looking at the camera

Beta-sorted portfolios—portfolios comprised of assets with similar covariation to selected risk factors—are a popular tool in empirical finance to analyse models of (conditional) expected returns. Despite their widespread use, little is known of their econometric properties in contrast to comparable procedures such as two-pass regressions

Matias D. Cattaneo, Richard K. Crump, Weining Wang

The framework

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 portfolios construction. Our framework rationalises the well-known estimation algorithm with precise economic and statistical assumptions on the general data generating process. We provide conditions that ensure consistency and asymptotic normality along with uniform inference procedures allowing for uncertainty quantification and general hypothesis testing for financial applications. We show that the rate of convergence of the estimator is non-uniform and depends on the beta value of interest. 

We also show that the widely-used Fama-MacBeth variance estimator is asymptotically conservative in general, and can lead to substantial power loss in empirically-relevant settings. We propose a new variance estimator which is always consistent and provide an empirical implementation which produces more powerful inference. 

In an empirical application, we introduce a novel risk factor – a measure of the business credit cycle – and show that it is strongly predictive of both the cross-section and time-series behavior of U.S. stock returns.

FinEML seminar series

The FinEML seminar series is designed to create a collaborative platform for the exchange of insights and findings within the field. We aim to foster a friendly atmosphere that encourages constructive feedback, providing an opportunity for both junior and senior researchers to share their work.

Submit research paper

Submit original research papers in the following topics, but not limited to:

  • Asset Pricing
  • Big Data
  • Forecasting with Machine Learning
  • Macro Finance
  • Option Pricing

Submit your research paper at the FinEML page

See also

Unearthing Financial Statement Fraud: Insights from News Coverage Analysis

Jianqing Fan, Princeton University
Jianqing Fan smiling at the camera

The Anatomy of Machine Learning-Based Portfolio Performance

Christian Montes Schutte, Aarhus University
Christian Montes Schutte smiling at the camera

Compare @count study programme

  • @title

    • Duration: @duration
Compare study programmes