Long-Run Expected Stock Returns

73 Pages Posted: 11 May 2021

See all articles by Paul Geertsema

Paul Geertsema

University of Auckland - Department of Accounting and Finance

Helen Lu

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

Date Written: May 11, 2021

Abstract

We predict individual stock returns over horizons from 1 month to 10 years using machine learning. Cumulative stock returns are significantly predictable in the cross-section over all horizons. One-month ahead predicted returns explain only a quarter of the variation in 10-year predicted returns, suggesting that predicted returns at different horizons follow distinct dynamics. Predictors related to turnover and volatility are influential at all horizons. Momentum, cash flow and size related predictors are mostly important at shorter horizons, while dividend yield, value and long-term reversal related predictors are more important at longer 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, Long-Run Expected Stock Returns (May 11, 2021). Available at SSRN: https://ssrn.com/abstract=3843501 or http://dx.doi.org/10.2139/ssrn.3843501

Paul G. Geertsema (Contact Author)

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

12 Grafton Rd
Private Bag 92019
Auckland, 1010
New Zealand

Helen Lu

University of Auckland Business School ( email )

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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