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

59 Pages Posted: 19 Dec 2019 Last revised: 20 Sep 2021

See all articles by Svetlana Bryzgalova

Svetlana Bryzgalova

London Business School - Department of Finance

Markus Pelger

Stanford University - Department of Management Science & Engineering

Jason Zhu

Stanford University - Management Science & Engineering

Date Written: September 25, 2020

Abstract

We build cross-sections of asset returns for a given set of characteristics, that is, managed portfolios that serve as test assets for asset pricing models and building blocks for new risk factors. We use decision trees to endogenously group similar stocks together by selecting optimal portfolio splits to span the Stochastic Discount Factor. Our portfolios are interpretable, and reflect many characteristics and their interactions. Compared to combinations of traditional sorts and machine learning prediction-based portfolios, our cross-sections have up to three times higher out-of-sample Sharpe ratios and pricing errors, and do not suffer from excessive repackaging/duplication of the original stocks.

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 (September 25, 2020). 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 - Department of 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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