Time-Varying Predictive Power of the Dividend Yield for Stock Returns: Evaluation Based on Bootstrapping

38 Pages Posted: 1 Dec 2014 Last revised: 17 Feb 2017

See all articles by Jae H. Kim

Jae H. Kim

La Trobe University - School of Economics and Finance

Abul Shamsuddin

University of Newcastle (Australia) - Newcastle Business School

Date Written: November 30, 2014

Abstract

This paper evaluates the predictive ability of dividend yield for stock return using a new bootstrap test for the significance of predictive coefficients. The predictive model is expressed as a restricted vector autoregressive model, and the bootstrap is conducted with resampling based on generalized least-squares estimation. Our Monte Carlo study shows that the bootstrap test has no size distortion in small samples with highly satisfactory power properties. We employ a range of bivariate predictive models using monthly U.S. data from 1926 to 2012. We find that the dividend yield does show predictive ability for stock return from time to time, but its effect size and out-of-sample forecasting performance have been weak except for the 1990s.

Keywords: Dividend-yield, EGLS estimation, Monte Carlo experiment, Return predictability

JEL Classification: C32, G12, G14

Suggested Citation

Kim, Jae H. and Shamsuddin, Abul, Time-Varying Predictive Power of the Dividend Yield for Stock Returns: Evaluation Based on Bootstrapping (November 30, 2014). Available at SSRN: https://ssrn.com/abstract=2532122 or http://dx.doi.org/10.2139/ssrn.2532122

Jae H. Kim (Contact Author)

La Trobe University - School of Economics and Finance ( email )

Department of Finance
La Trobe Business School
Bundoora, IN 3086
Australia

Abul Shamsuddin

University of Newcastle (Australia) - Newcastle Business School ( email )

City Campus East – 231
Callaghan, NSW 2308
Australia

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