Cash-flow or return predictability at long horizons? The case of earnings yield

Paulo Maio and Danielle Xu

Journal of Empirical Finance, 2020, vol. 59, issue C, 172-192

Abstract: We examine the predictive ability of the aggregate earnings yield for both 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 method, produces long-run variance decompositions that are substantially more similar to the decomposition obtained 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; Weighted long-horizon regressions; Earnings yield; VAR implied predictability; Present-value model; Dividend yield; Projection Minimum Distance method (search for similar items in EconPapers)
JEL-codes: C22 G12 G14 G17 G35 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:59:y:2020:i:c:p:172-192

DOI: 10.1016/j.jempfin.2020.10.001

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