Miller and Modigliani, Predictive Return Regressions and Cointegration*
构建包含股利和非股利现金流的现金流收益率，利用20世纪美国数据，在协整向量自回归框架下证明该指标对股票收益有强且稳定的预测能力，并解释标准股利收益率预测力弱的原因。
Abstract This paper investigates the use of alternative measures of dividend yields to predict US aggregate stock returns. Following Miller and Modigliani [ Journal of Business (1961), Vol. 34, pp. 411–433] we construct a cashflow yield that includes both dividend and non‐dividend cashflows to shareholders. Using a data set covering the course of the 20th century, we show in a cointegrating vector autoregression framework that this measure has strong and stable predictive power for returns. The weak predictive power of standard measures of the dividend yield is explained by the strong rejection of the implied cointegrating and causality restrictions on the impact of non‐dividend cashflows.