The 52-week high, momentum, and investor sentiment
Ying Hao,
Robin K. Chou,
Kuan-Cheng Ko and
Nien-Tzu Yang
International Review of Financial Analysis, 2018, vol. 57, issue C, 167-183
Abstract:
This paper examines the link between the profitability of the 52-week high momentum strategy and investor sentiment. We hypothesize that investors' investment decisions are subject to behavioral biases when the level of investor sentiment is high, resulting in higher profits for the 52-week high momentum following high-sentiment periods. Our empirical results confirm this prediction. In addition, we find that the significant profit of the 52-week high momentum following high-sentiment periods persists up to five years. Further investigations show that the strong persistence of the 52-week high winners (losers) is concentrated in stocks with higher (lower) earnings surprises, especially during periods following high sentiment. Overall, our results provide supportive evidence for the anchoring biases in explaining the 52-week high momentum, especially when the role of investor sentiment is taken into account.
Keywords: 52-Week high; Momentum profits; Investor sentiment; Earnings announcement (search for similar items in EconPapers)
JEL-codes: G11 G12 G14 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1057521918300747
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:finana:v:57:y:2018:i:c:p:167-183
DOI: 10.1016/j.irfa.2018.01.014
Access Statistics for this article
International Review of Financial Analysis is currently edited by B.M. Lucey
More articles in International Review of Financial Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().