Cash Flow News and Stock Price Dynamics

Abstract

We develop a new approach to modeling dynamics in cash flow data extracted from daily firm-level dividend announcements. We decompose daily cash flow news into a persistent component, jumps, and temporary shocks. Empirically, we find that the persistent cash flow component is a highly significant predictor of future growth in dividends and consumption. Using a log-linearized present value model, we show that news about the persistent dividend growth component helps predict stock returns consistent with asset-pricing constraints implied by this model. News about the daily dividend growth process also helps explain concurrent return volatility and the probability of jumps in stock returns.

Keywords: High-frequency cash flow news; predictability of dividend growth; present value model; dynamics and predictability of stock returns; Bayesian modeling

JEL Classification: G12, G17