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PREDICTING STOCK RETURNS — THE INFORMATION CONTENT OF PREDICTORS ACROSS HORIZONS

KAIHUA DENG

Department of Economics, University of Washington, Seattle, WA 98195-3330, USA

Hanqing Advanced Institute of Economics and Finance, Renmin University of China, Beijing 100872, China

E-mail Address: dengk@uw.edu

Corresponding author.

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and

CHANG-JIN KIM

Department of Economics, University of Washington, Seattle, WA 98195-3330, USA

Department of Economics, Korea University, Seoul, South Korea

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https://doi.org/10.1142/S201049521550013XCited by:1 (Source: Crossref)

Abstract

We evaluate and compare the information contents of dividend-price ratio and consumption-wealth ratio ( $c a y)$ $c a y)$ for predicting stock returns at different horizons. To do this, we conduct a canonical correlation analysis of wavelet-decomposed stock returns and a selected group of predictors. We show that predictive information is often wasted due to a weak signal problem: The highly predictive component is met with very low variation. Nevertheless, we find that cay contains valuable information about the long run and that, after allowing for structural breaks, dividend-price ratio becomes very informative about short-to-medium-horizon returns and outperforms cay in terms of in-sample $R^{2}$ $R^{2}$ .

Keywords:

JEL Classifications: C22, C53, G12

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