Dividend Momentum and Stock Return Predictability: A Bayesian Approach
用贝叶斯方法研究股息增长持续性带来的“股息动量”对股票收益可预测性的影响，发现结合贝叶斯收缩和Campbell-Shiller约束能提高预测精度和夏普比率。
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.