Casting Doubt on the Predictability of Stock Returns in Real Time: Bayesian Model Averaging using Realistic Priors

James A. Turner

Review of Finance, 2015, vol. 19, issue 2, 785-821

Abstract: Previous studies have identified several variables that would have predicted future stock returns, though other studies suggest these results may be due to data snooping. To guard against data snooping, researchers have suggested use of Bayesian model averaging (BMA) to account for the uncertainty about prediction models. In common with other researchers, I find evidence of predictability during time periods when a hypothetical investor uses BMA with no restrictions on what variables may be included in the model. However, when the hypothetical investor is limited to using only variables whose predictive ability would have been known at the time of the forecast, predictability disappears. Moreover, predictability also disappears when data are updated through 2010, even without constraints on variable use. The results cast doubt on whether stock returns were ever predictable in real time and also suggest that returns may no longer be predictable even if real-time constraints are removed.

Date: 2015
References: View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://hdl.handle.net/10.1093/rof/rfu013 (application/pdf)
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:oup:revfin:v:19:y:2015:i:2:p:785-821.

Ordering information: This journal article can be ordered from
https://academic.oup.com/journals

Access Statistics for this article

Review of Finance is currently edited by Marcin Kacperczyk

More articles in Review of Finance from European Finance Association Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK. Contact information at EDIRC.
Bibliographic data for series maintained by Oxford University Press ().