Forest Through the Trees: Building Cross-Sections of Stock Returns

72 Pages Posted: 19 Dec 2019 Last revised: 21 May 2020

See all articles by Svetlana Bryzgalova

Svetlana Bryzgalova

London Business School - Department of Finance

Markus Pelger

Stanford University - Management Science & Engineering

Jason Zhu

Stanford University - Management Science & Engineering

Date Written: December 1, 2019

Abstract

We show how to build a cross-section of asset returns, that is, a small set of basis assets that capture complex information contained in a given set of stock characteristics. We use decision trees to generalize the concept of conventional sorting and introduce a new approach to the robust recovery of a low-dimensional set of portfolios that span the stochastic discount factor (SDF). Constructed from the same pricing signals as conventional double- or triple-sorted portfolios, our cross-sections have on average 30% higher Sharpe ratios and pricing errors relative to the leading reduced-form asset pricing models. They include long-only investment strategies that are well diversified, easily interpretable, and that could be built to reflect many characteristics at the same time. Empirically, we show that traditionally used cross-sections of portfolios and their combinations often present too low a hurdle for candidate asset pricing models, as they miss a lot of the underlying information from the original returns.

Keywords: Asset Pricing, Sorting, Portfolios, Cross-Section of Expected Returns, Decision Trees, Elastic Net, Stock Characteristics, Machine Learning

JEL Classification: G11, G12, C55, C58

Suggested Citation

Bryzgalova, Svetlana and Pelger, Markus and Zhu, Jason, Forest Through the Trees: Building Cross-Sections of Stock Returns (December 1, 2019). Available at SSRN: https://ssrn.com/abstract=3493458 or http://dx.doi.org/10.2139/ssrn.3493458

Svetlana Bryzgalova

London Business School - Department of Finance ( email )

Sussex Place
Regent's Park
London NW1 4SA
United Kingdom

Markus Pelger (Contact Author)

Stanford University - Management Science & Engineering ( email )

473 Via Ortega
Stanford, CA 94305-9025
United States

Jason Zhu

Stanford University - Management Science & Engineering ( email )

314L Huang Engineering Center
475 Via Ortega
Stanford, CA 94305
United States

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