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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/29885
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| Title: | Modeling Dependency in Prediction Markets |
| Authors: | Archak, Nikolay
Ipeirotis, Panagiotis G. |
| Issue Date: | 1-Dec-2010 |
| Series/Report no.: | CeDER-10-05 |
| Abstract: | In the last decade, prediction markets became popular forecasting tools
in areas ranging from election results to movie revenues and Oscar
nominations. One of the features that make prediction markets
particularly attractive for decision support applications is that they
can be used to answer what-if questions and estimate probabilities of
complex events. Traditional approach to answering such questions
involves running a combinatorial prediction market, what is not always
possible. In this paper, we present an alternative, statistical approach
to pricing complex claims, which is based on analyzing co-movements of
prediction market prices for basis events. Experimental evaluation of
our technique on a collection of 51 InTrade contracts representing the
Democratic Party Nominee winning Electoral College Votes of a particular
state shows that the approach outperforms traditional forecasting
methods such as price and return regressions and can be used to extract
meaningful business intelligence from raw price data. |
| URI: | http://hdl.handle.net/2451/29885 |
| Appears in Collections: | CeDER Working Papers
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