Dividend Momentum and Stock Return Predictability: A Bayesian Approach

Rubio-Ramírez, Juan Francisco, Ivan Petrella and Juan Antolin-Diaz

No 16613, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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 OLS, our restricted informative prior leads to a much more moderate, but still signi cant, degree of return predictability, with forecasts that are helpful out-of-sample and realistic asset allocation prescriptions with Sharpe ratios that out-perform common benchmarks.

JEL-codes: C32 C53 E47 (search for similar items in EconPapers)
Date: 2021-10
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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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