Dividend Momentum and Stock Return Predictability: A Bayesian Approach
成果类型：
Article
署名作者：
Antolin-Diaz, Juan; Petrella, Ivan; Rubio-Ramirez, Juan
署名单位：
Massachusetts Institute of Technology (MIT); Collegio Carlo Alberto; University of Turin; Emory University; Federal Reserve System - USA; Federal Reserve Bank - Atlanta
刊物名称：
REVIEW OF FINANCIAL STUDIES
ISSN/ISSBN：
0893-9454; 1465-7368
DOI：
10.1093/rfs/hhaf110
发表日期：
2026-05
页码：
1506-1554
关键词：
C32
C53
G11
G12
E47
long-run
variance decomposition
vector autoregressions
stochastic volatility
exchange-rates
consumption
expectations
inference
risks
MODEL
摘要：
A long tradition in macro-finance studies the dynamics of aggregate stock returns and dividends using vector autoregressions, imposing the restrictions implied by the Campbell-Shiller (CS) identity to sharpen inference. We develop Bayesian methods that encode a priori skepticism about return predictability while imposing the restrictions. We highlight that persistence in dividend growth induces dividend momentum, a previously overlooked channel for return predictability. By combining Bayesian shrinkage and the CS restrictions, we obtain more plausible degrees of return predictability, superior out-of-sample forecasts, and Sharpe ratios, which cannot be obtained by using either shrinkage or the CS restrictions on their own.
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