Dividend Momentum and Stock Return Predictability: A Bayesian Approach
Abstract
Abstract A long tradition in macro-finance studies the dynamics of aggregate stock returns and dividends using vector autoregressions, imposing the restrictions implied by the Campbell-Shiller (CS) identity to sharpen inference. We develop Bayesian methods that encode a priori skepticism about return predictability while imposing the restrictions. We highlight that persistence in dividend growth induces “dividend momentum,” a previously overlooked channel for return predictability. By combining Bayesian shrinkage and the CS restrictions, we obtain more plausible degrees of return predictability, superior out-of-sample forecasts, and Sharpe ratios, which cannot be obtained by using either shrinkage or the CS restrictions on their own.
Fields of Study & Subjects
Financial Markets and Investment Strategies
Corporate Finance and Governance
Financial Reporting and Valuation Research
Publication details
- Journal / source
- Review of Financial Studies
- Volume
- 39
- Issue
- 5
- Pages
- 1506–1554
- Publication date
- 15 January 2026
- Language
- EN