Cash-Flow or Return Predictability at Long Horizons? The Case of Earnings Yield

58 Pages Posted: 2 Nov 2012 Last revised: 3 Jun 2020

See all articles by Paulo F. Maio

Paulo F. Maio

Hanken School of Economics - Department of Finance and Statistics

Danielle Xu

Gonzaga University

Date Written: June 2, 2020

Abstract

We examine the predictive ability of the aggregate earnings yield for market returns and earnings growth by estimating variance decompositions at multiple horizons. Based on weighted long-horizon regressions, we find that most of the variation in the earnings yield is due to return predictability, with earnings growth predictability assuming a minor role. However, by using implied estimates from a first-order restricted VAR, we find an opposite predictability mix. The inconsistency in results stems from a misspecification of the restricted VAR. Using an unrestricted first-order VAR estimated by OLS, or alternatively estimating the restricted VAR by the Projection Minimum Distance (PMD) method, produces long-run variance decompositions that are substantially more similar to the decomposition estimated under the direct method. Hence, earnings yield is not fundamentally different from the dividend yield. These results suggest that the practice of analyzing long-run return and cash-flow predictability from a restricted VAR can be quite misleading.

Keywords: predictability of stock returns; earnings-growth predictability; long-horizon regressions; earnings yield; VAR implied predictability; present-value model; dividend-payout ratio

JEL Classification: C22, G12, G14, G17, G35

Suggested Citation

Maio, Paulo F. and Xu, Danielle, Cash-Flow or Return Predictability at Long Horizons? The Case of Earnings Yield (June 2, 2020). Available at SSRN: https://ssrn.com/abstract=2170079 or http://dx.doi.org/10.2139/ssrn.2170079

Paulo F. Maio (Contact Author)

Hanken School of Economics - Department of Finance and Statistics ( email )

FI-00101 Helsinki
Finland

HOME PAGE: http://sites.google.com/site/paulofmaio/home

Danielle Xu

Gonzaga University ( email )

Spokane, WA 99258
United States

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