Mad Money: Does the combination of stock recommendation and show segment matter?
Jose Gutierrez and
Robert Stretcher
Journal of Behavioral and Experimental Finance, 2015, vol. 6, issue C, 80-92
Abstract:
Few observable patterns of stimuli appear to consistently affect retail investor behavior. This study is motivated by what is presumably the most prominent media source of advice for retail investors in the United States, Jim Cramer’s Mad Money broadcast. We analyze return metrics of stocks recommended by Jim Cramer. We differentiate among five different recommendations and across five different segments of the show to determine if pricing behavior differs in magnitude and persistence, depending on the nature of the recommendation and the segment of the show in which the recommendation appears. Results indicate that the pricing impact is greatest for stocks discussed on longer-duration segments of the show and that there is an asymmetric effect in terms of “buy” versus “sell” recommendations. The results also highlight the tendency of the impact to be larger for smaller market capitalization firms.
Keywords: Market efficiency; Stock recommendation; Investor behavior; Retail investor; Media attention (search for similar items in EconPapers)
JEL-codes: G12 G14 (search for similar items in EconPapers)
Date: 2015
References: Add references at CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S2214635015000222
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:beexfi:v:6:y:2015:i:c:p:80-92
DOI: 10.1016/j.jbef.2015.03.005
Access Statistics for this article
Journal of Behavioral and Experimental Finance is currently edited by Michael Dowling and Jürgen Huber
More articles in Journal of Behavioral and Experimental Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().