PREDICTING STOCK RETURNS — THE INFORMATION CONTENT OF PREDICTORS ACROSS HORIZONS
Kaihua Deng and
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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
Chang-Jin Kim: #x2021;Department of Economics, University of Washington, Seattle, WA 98195-3330, USADepartment of Economics, Korea University, Seoul, South Korea
Annals of Financial Economics (AFE), 2015, vol. 10, issue 02, 1-27
We evaluate and compare the information contents of dividend-price ratio and consumption-wealth ratio (cay) 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 R2.
Keywords: Canonical correlation; multi-resolution analysis; multi-scale variance decomposition (search for similar items in EconPapers)
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