Dividend momentum and stock return predictability: A Bayesian approach - Juan Antolín-Díaz; Ivan Petrella; Juan Francisco Rubio-Ramírez (Federal Reserve Bank of Atlanta, 2021)
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: C32; C53; G11; G12; E47; 330; CS restrictions; Bayesian VAR; optimal allocation; Dividende; Börsenkurs; Bayes-Statistik; Prognoseverfahren; USA