Modeling Dependency in Prediction Markets

Abstract

In the last decade, prediction markets became popular forecasting toolsin areas ranging from election results to movie revenues and Oscarnominations. One of the features that make prediction marketsparticularly attractive for decision support applications is that theycan be used to answer what-if questions and estimate probabilities ofcomplex events. Traditional approach to answering such questionsinvolves running a combinatorial prediction market, what is not alwayspossible. In this paper, we present an alternative, statistical approachto pricing complex claims, which is based on analyzing co-movements ofprediction market prices for basis events. Experimental evaluation ofour technique on a collection of 51 InTrade contracts representing theDemocratic Party Nominee winning Electoral College Votes of a particularstate shows that the approach outperforms traditional forecastingmethods such as price and return regressions and can be used to extractmeaningful business intelligence from raw price data.