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Path-Dependent and Randomized Strategies in Barberis’ Casino Gambling Model

Abstract:
We consider the dynamic casino gambling model initially proposed by Barberis (2012) and study the optimal stopping strategy of a pre-committing gambler with cumulative prospect theory (CPT) preferences. We illustrate how the strategies computed in Barberis (2012) can be strictly improved by reviewing the betting history or by tossing an independent coin, and we explain that the improvement generated by using randomized strategies results from the lack of quasi-convexity of CPT preferences. Moreover, we show that any path-dependent strategy is equivalent to a randomization of path-independent strategies.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1287/opre.2016.1545

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Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Role:
Author


More from this funder
Funding agency for:
Obloj, J
Zhou, X
Grant:
FP7/2007-2013) no. 33542
More from this funder
Funding agency for:
Obloj, J
Grant:
FP7/2007-2013) no. 33542
More from this funder
Funding agency for:
Obloj, J
Grant:
FP7/2007-2013) no. 33542


Publisher:
INFORMS
Journal:
Operations Research More from this journal
Volume:
65
Issue:
1
Pages:
97 - 103
Publication date:
2016-11-01
Acceptance date:
2016-06-03
DOI:
EISSN:
1526-5463
ISSN:
0030-364X


Keywords:
Pubs id:
pubs:656543
UUID:
uuid:6e50d4d5-192f-44d1-8692-95d4167790dc
Local pid:
pubs:656543
Source identifiers:
656543
Deposit date:
2016-11-02
ARK identifier:

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