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Dividend Momentum and Stock Return Predictability: A Bayesian Approach

Juan Drechsel Antolin-Diaz et al.

The Review of Financial Studies2026https://doi.org/10.1093/rfs/hhaf110preprint
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Abstract

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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https://doi.org/https://doi.org/10.1093/rfs/hhaf110

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@article{juan2026,
  title        = {{Dividend Momentum and Stock Return Predictability: A Bayesian Approach}},
  author       = {Juan Drechsel Antolin-Diaz et al.},
  journal      = {The Review of Financial Studies},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1093/rfs/hhaf110},
}

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