Arbitrage-Free Combinatorial Market Making via Integer Programming

Christian Kroer, Miroslav Dudík, Sébastien Lahaie, Sivaraman Balakrishnan
2016 Proceedings of the 2016 ACM Conference on Economics and Computation - EC '16  
We present a new combinatorial market maker that operates arbitrage-free combinatorial prediction markets specified by integer programs. Although the problem of arbitrage-free pricing, while maintaining a bound on the subsidy provided by the market maker, is #P-hard in the worst case, we posit that the typical case might be amenable to modern integer programming (IP) solvers. At the crux of our method is the Frank-Wolfe (conditional gradient) algorithm which is used to implement a Bregman
more » ... ent a Bregman projection aligned with the market maker's cost function, using an IP solver as an oracle. We demonstrate the tractability and improved accuracy of our approach on real-world prediction market data from combinatorial bets placed on the 2010 NCAA Men's Division I Basketball Tournament, where the outcome space is of size 2^63. To our knowledge, this is the first implementation and empirical evaluation of an arbitrage-free combinatorial prediction market on this scale.
doi:10.1145/2940716.2940767 dblp:conf/sigecom/KroerDLB16 fatcat:qgyzdg2ohreknl4npukd2tsaf4