Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ The Journal of Gambl...arrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
The Journal of Gambling Business and Economics
Article . 2013 . Peer-reviewed
Data sources: Crossref
EconStor
Research . 2011
Data sources: EconStor
versions View all 3 versions
addClaim

INFORMATION AND ATTITUDES TO RISK AT THE TRACK

Authors: Adi Schnytzer; Sara Westreich;

INFORMATION AND ATTITUDES TO RISK AT THE TRACK

Abstract

There have been many attempts, theoretical and empirical, to explain the persistence of a favorite-longshot bias in various horse betting markets. Most recently, Snowberg and Wolfers (2010) have shown that the data for the US markets support a “misperceptions of probability” approach in line with prospect theory over a neoclassical approach of the Quandt (1986) type. However, their paper suffers from two basic difficulties which beset much of this literature. First, the theoretical model used fails to allow for the existence of horse betting markets which either display no such bias (or a reverse bias) as in Hong Kong and at least one large Australian market (Busche and Hall, 1988, Schnytzer, Shilony and Thorne, 2003 and Luppi and Schnytzer, 2008). Second, econometric testing and theoretical modeling are facilitated by the highly unrealistic assumption that the betting population is homogeneous with respect to either information or attitude to risk or (usually) both. Our purpose is to show that allowing for heterogeneous betting populations (in terms of both attitude to risk and access to information) permits the explanation for the different biases (or their absence) observed in different markets accommodating both a framework of rational bettors and the requirements of prospect theory. We conclude with empirical support for our model.

Related Organizations
Keywords

Bias, ddc:330, Glücksspiel, Prognoseverfahren, Ökonometrisches Modell, Rationales Verhalten, Theorie, Pferdesport

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
bronze