Predicting the Equity Premium with Dividend Ratios

33 Pages Posted: 14 Feb 2002 Last revised: 8 Jul 2022

See all articles by Amit Goyal

Amit Goyal

University of Lausanne; Swiss Finance Institute

Ivo Welch

University of California, Los Angeles (UCLA); National Bureau of Economic Research (NBER)

Multiple version iconThere are 2 versions of this paper

Date Written: February 2002

Abstract

Our paper reexamines the forecasting regressions which predict annual aggregate stock market returns net of the risk-free rate with lagged aggregate dividend-yield ratios and dividend-price ratios. Prior to 1990, the conditional dividend yield could reliably outperform the historical equity premium mean in predicting future equity premia *in-sample*. But our paper shows that the dividend ratios could not outperform the prevailing unconditional mean *out-of-sample*, plus any residual power was directly related to only two years, 1974 and 1975. As of 2000, even this in-sample predictive ability has disappeared. Our paper also documents changes in the time-series processes of the dividends themselves and shows that an increasing persistence of dividend-price ratio is largely responsible for weak stock return predictability.

Suggested Citation

Goyal, Amit and Welch, Ivo, Predicting the Equity Premium with Dividend Ratios (February 2002). NBER Working Paper No. w8788, Available at SSRN: https://ssrn.com/abstract=300750

Amit Goyal

University of Lausanne ( email )

Batiment Extranef 226
Lausanne, Vaud CH-1015
Switzerland
+41 21 692 3676 (Phone)
+41 21 692 3435 (Fax)

HOME PAGE: http://www.hec.unil.ch/agoyal/

Swiss Finance Institute ( email )

c/o University of Geneva
40, Bd du Pont-d'Arve
CH-1211 Geneva 4
Switzerland

Ivo Welch (Contact Author)

University of California, Los Angeles (UCLA) ( email )

110 Westwood Plaza
C519
Los Angeles, CA 90095-1481
United States
310-825-2508 (Phone)

HOME PAGE: http://www.ivo-welch.info

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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