Time‐varying Predictability for Stock Returns, Dividend Growth and Consumption Growth

David G. McMillan

International Journal of Finance & Economics, 2015, vol. 20, issue 4, 362-373

Abstract: Using a state‐space model, this paper examines time variation in the predictive regressions for stock returns, dividend growth and consumption growth. Moreover, we linked time variation explicitly to movements in economic factors that can account for risk and cash flow. Results support the view that stock return predictability is enhanced when risk is high (negative growth, higher volatility and positive growth/return covariance). In contrast, dividend growth and consumption growth predictability is enhanced during economic expansions. These results are supported by subsample analysis and a vector autoregressive approach. Furthermore, these latter exercises may uncover differences in the stock return predictability relationship when viewed over different time horizons. Overall, the paper contributes to the literature by highlighting the different nature of returns predictability, which arises largely through the risk channel, and dividend and consumption growth predictability, which arise through the cash flow channel. Copyright © 2015 John Wiley & Sons, Ltd.

Date: 2015
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/

Related works:
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:wly:ijfiec:v:20:y:2015:i:4:p:362-373

Ordering information: This journal article can be ordered from
http://jws-edcv.wile ... PRINT_ISSN=1076-9307

Access Statistics for this article

International Journal of Finance & Economics is currently edited by Mark P. Taylor, Keith Cuthbertson and Michael P. Dooley

More articles in International Journal of Finance & Economics from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().