Dynamic Learning and Market Making in Spread Betting Markets With Informed Bettors
64 Pages Posted: 7 Dec 2018
Date Written: November 12, 2018
We study the profit maximization problem of a market maker in a spread betting market for a future event. Anonymous bettors with heterogeneous strategic behavior and information levels participate in the market. The market maker is initially uninformed of the event outcome distribution, aiming to extract information from the market (i.e., "learning") while guarding against an informed bettor's strategic manipulation via bets (i.e., "bluff-proofing"). We show that Bayesian policies that ignore bluffing are typically vulnerable to the informed bettor's manipulation. We propose a novel family of policies, called inertial policies, that balance the tradeoff between learning and bluff-proofing, achieving an expected regret up to a logarithmic factor of the number of bets.
Keywords: spread betting market, market making, sequential learning, market manipulation, prediction markets, sports analytics
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