Dividend Momentum and Stock Return Predictability: A Bayesian Approach

• Ivan Petrella ^[1] ; Helena Hernández-Pizarro ^[2] ; Juan F. Rubio-Ramírez ^[3]
  1. [1] London Business School

     London Business School

     Reino Unido

     
  2. [2] University of Warwick

     University of Warwick

     Reino Unido

     
  3. [3] Emory University

     Emory University

     Estados Unidos

     

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• Localización: Documentos de trabajo ( FEDEA ), ISSN 1696-7496, Nº. 14, 2021, págs. 1-79
• Idioma: inglés
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• Resumen

  • 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