The Cross-section of Long-run Expected Stock Returns

80 Pages Posted: 26 Feb 2021

See all articles by Paul Geertsema

Paul Geertsema

University of Auckland Business School

Helen Lu

University of Auckland Business School; University of Auckland - Department of Accounting and Finance

Date Written: December 10, 2020

Abstract

Abstract We predict cumulative stock returns over horizons from 1 month to 10 years using a tree-based machine learning approach. Cumulative stock returns are significantly predictable in the cross-section over all horizons. A hedge portfolio generates 250 bp/month at a 1 year horizon and 110 bp/month at a 10 year horizon. Individual stock returns are significantly predictable at all horizons in panel data. Cashflow and momentum related predictors are mostly important at shorter horizons while dividend yield and value related predictors are more important at longer horizons. By contrast, variables related to turnover and volatility are influential at all horizons.

Keywords: Long-Run Discount Rates; Return Predictability; Cross-Section of Returns; Machine Learning; Gradient Boosting; Variable Importance; SHAP Values

JEL Classification: G12, G17, C53

Suggested Citation

Geertsema, Paul G. and Lu, Helen, The Cross-section of Long-run Expected Stock Returns (December 10, 2020). Available at SSRN: https://ssrn.com/abstract=3774548 or http://dx.doi.org/10.2139/ssrn.3774548

Paul G. Geertsema (Contact Author)

University of Auckland Business School ( email )

12 Grafton Rd
Private Bag 92019
Auckland, 1010
New Zealand

Helen Lu

University of Auckland Business School ( email )

12 Grafton Rd
Private Bag 92019
Auckland, 1010
New Zealand

HOME PAGE: http://www.business.auckland.ac.nz/people/hlu079

University of Auckland - Department of Accounting and Finance ( email )

Private Bag 92019
Auckland 1001
New Zealand

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