Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
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.