Dividend Momentum and Stock Return Predictability: A Bayesian Approach

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

No 2021-14, Working Papers from FEDEA

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 significant, 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.

Date: 2021-11
New Economics Papers: this item is included in nep-cwa, nep-ecm, nep-fmk and nep-for
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https://documentos.fedea.net/pubs/dt/2021/dt2021-14.pdf (application/pdf)

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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