Stock return predictability and the adaptive markets hypothesis: Evidence from century-long U.S. data
Jae Kim,
Abul Shamsuddin () and
Kian-Ping Lim
Journal of Empirical Finance, 2011, vol. 18, issue 5, 868-879
Abstract:
This paper provides strong evidence of time-varying return predictability of the Dow Jones Industrial Average index from 1900 to 2009. Return predictability is found to be driven by changing market conditions, consistent with the implication of the adaptive markets hypothesis. During market crashes, no statistically significant return predictability is observed, but return predictability is associated with a high degree of uncertainty. In times of economic or political crises, stock returns have been highly predictable with a moderate degree of uncertainty in predictability. We find that return predictability has been smaller during economic bubbles than in normal times. We also find evidence that return predictability is associated with stock market volatility and economic fundamentals.
Keywords: Economic bubbles; Economic crises; Adaptive markets hypothesis; Market efficiency; U.S. stock market (search for similar items in EconPapers)
JEL-codes: G14 (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (150)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0927539811000612
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:empfin:v:18:y:2011:i:5:p:868-879
DOI: 10.1016/j.jempfin.2011.08.002
Access Statistics for this article
Journal of Empirical Finance is currently edited by R. T. Baillie, F. C. Palm, Th. J. Vermaelen and C. C. P. Wolff
More articles in Journal of Empirical Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().