Stock return predictability: the role of inflation and threshold dynamics
David G. McMillan
International Review of Applied Economics, 2017, vol. 31, issue 3, 357-375
Abstract:
This paper argues that the nature of stock return predictability varies with the level of inflation. We contend that the nature of relations between economic variables and returns differs according to the level of inflation, due to different economic risk implications. An increase in low level inflation may signal improving economic conditions and lower expected returns, while the opposite is true with an equal rise in high level inflation. Linear estimation provides contradictory coefficient values, which we argue arises from mixing coefficient values across regimes. We test for and estimate threshold models with inflation and the term structure as the threshold variable. These models reveal a change in either the sign or magnitude of the parameter values across the regimes such that the relation between stock returns and economic variables is not constant. Measures of in-sample fit and a forecast exercise support the threshold models. They produce a higher adjusted R2, lower MAE and RMSE and higher trading related measures. These results help explain the lack of consistent empirical evidence in favour of stock return predictability and should be of interest to those engaged in stock market modelling as well as trading and portfolio management.
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/02692171.2016.1257581 (text/html)
Access to full text is restricted to subscribers.
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:taf:irapec:v:31:y:2017:i:3:p:357-375
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CIRA20
DOI: 10.1080/02692171.2016.1257581
Access Statistics for this article
International Review of Applied Economics is currently edited by Professor Malcolm Sawyer
More articles in International Review of Applied Economics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().