Dividend Momentum and Stock Return Predictability: A Bayesian Approach

Juan Antolin-Diaz, Ivan Petrella and Juan F Rubio-Ramirez

No 2021-25, FRB Atlanta Working Paper from Federal Reserve Bank of Atlanta

Abstract: A long tradition in macro finance studies the joint dynamics of aggregate stock returns and dividends using vector autoregressions (VARs), imposing the cross-equation restrictions implied by the Campbell-Shiller (CS) identity to sharpen inference. We take a Bayesian perspective and develop methods to draw from any posterior distribution of a VAR that encodes a priori skepticism about large amounts of return predictability while imposing the CS restrictions. In doing so, we show how a common empirical practice of omitting dividend growth from the system amounts to imposing the extra restriction that dividend growth is not persistent. We highlight that persistence in dividend growth induces a previously overlooked channel for return predictability, which we label "dividend momentum." Compared to estimation based on ordinary least squares, our restricted informative prior leads to a much more moderate, but still significant, degree of return predictability, with forecasts that are helpful out of sample and realistic asset allocation prescriptions with Sharpe ratios that outperform common benchmarks.

Keywords: CS restrictions; Bayesian VARs; optimal allocation (search for similar items in EconPapers)
JEL-codes: C32 C53 E47 G11 G12 (search for similar items in EconPapers)
Pages: 78
Date: 2021-11-10
New Economics Papers: this item is included in nep-cwa, nep-for, nep-mac and nep-ore
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Published in 2021

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Related works:
Working Paper: Dividend Momentum and Stock Return Predictability: A Bayesian Approach (2021)
Working Paper: Dividend Momentum and Stock Return Predictability: A Bayesian Approach (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedawp:93480

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DOI: 10.29338/wp2021-25

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