Institutional investors and stock return anomalies
Roger M. Edelen,
Ozgur S. Ince and
Gregory B. Kadlec
Journal of Financial Economics, 2016, vol. 119, issue 3, 472-488
Abstract:
We examine institutional demand prior to well-known stock return anomalies and find that institutions have a strong tendency to buy stocks classified as overvalued (short leg of anomaly), and that these stocks have particularly negative ex post abnormal returns. Our results differ from numerous studies documenting a positive relation between institutional demand and future returns. We trace the difference to measurement horizon. We too find a positive relation at a quarterly horizon. However, the relation turns strongly negative at the one-year horizon used in anomaly studies. We consider several explanations for institutions’ tendency to trade contrary to anomaly prescriptions. Our evidence largely rules out explanations based on flow and limits-of-arbitrage, but is more consistent with agency-induced preferences for stock characteristics that relate to poor long-run performance.
Keywords: Investor base; Limits-of-arbitrage; Mispricing; Herding; Trading strategies (search for similar items in EconPapers)
JEL-codes: G12 G14 G23 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (76)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304405X16000039
Full text for ScienceDirect subscribers only
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:eee:jfinec:v:119:y:2016:i:3:p:472-488
DOI: 10.1016/j.jfineco.2016.01.002
Access Statistics for this article
Journal of Financial Economics is currently edited by G. William Schwert
More articles in Journal of Financial Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().