Cash Flow News and Stock Price Dynamics
Allan Timmermann,
Davide Pettenuzzo () and
Riccardo Sabbatucci
No 14117, CEPR Discussion Papers from C.E.P.R. Discussion Papers
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; Dividend growth; Present value model (search for similar items in EconPapers)
Date: 2019-11
New Economics Papers: this item is included in nep-mst
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://cepr.org/publications/DP14117 (application/pdf)
CEPR Discussion Papers are free to download for our researchers, subscribers and members. If you fall into one of these categories but have trouble downloading our papers, please contact us at subscribers@cepr.org
Related works:
Journal Article: Cash Flow News and Stock Price Dynamics (2020) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:cpr:ceprdp:14117
Ordering information: This working paper can be ordered from
https://cepr.org/publications/DP14117
Access Statistics for this paper
More papers in CEPR Discussion Papers from C.E.P.R. Discussion Papers Centre for Economic Policy Research, 33 Great Sutton Street, London EC1V 0DX.
Bibliographic data for series maintained by ().