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

60 Pages Posted: 19 Dec 2019 Last revised: 5 Aug 2023

See all articles by Svetlana Bryzgalova

Svetlana Bryzgalova

London Business School - Department of Finance; Centre for Economic Policy Research (CEPR)

Markus Pelger

Stanford University - Department of Management Science & Engineering

Jason Zhu

Stanford University - Management Science & Engineering

Date Written: August 25, 2019

Abstract

We build cross-sections of asset returns for a given set of characteristics, that is, managed portfolios serving as test assets, as well as building blocks for tradable risk factors. We use decision trees to endogenously group similar stocks together by selecting optimal portfolio splits to span the Stochastic Discount Factor, projected on individual stocks. Our portfolios are interpretable and well diversified, reflecting many characteristics and their interactions. Compared to combinations of dozens (even hundreds) of single/double sorts, as well as machine learning prediction-based portfolios, our cross-sections are low-dimensional yet have up to three times higher out-of-sample Sharpe ratios and alphas.

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 (August 25, 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

Centre for Economic Policy Research (CEPR) ( email )

London
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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