DP16613 Dividend Momentum and Stock Return Predictability: A Bayesian Approach

Author(s): Juan Antolin-Diaz, Ivan Petrella, Juan Francisco Rubio-Ramírez
Publication Date: October 2021
JEL(s): C32, C53, E47
Programme Areas: Financial Economics
Link to this Page: cepr.org/active/publications/discussion_papers/dp.php?dpno=16613

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 signiâ??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.