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A Bayesian market maker
 In ACM EC
, 2012
"... Ensuring sufficient liquidity is one of the key challenges for designers of prediction markets. Variants of the logarithmic market scoring rule (LMSR) have emerged as the standard. LMSR market makers are lossmaking in general and need to be subsidized. Proposed variants, including liquidity sensiti ..."
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Cited by 7 (2 self)
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Ensuring sufficient liquidity is one of the key challenges for designers of prediction markets. Variants of the logarithmic market scoring rule (LMSR) have emerged as the standard. LMSR market makers are lossmaking in general and need to be subsidized. Proposed variants, including liquidity sensitive market makers, suffer from an inability to react rapidly to jumps in population beliefs. In this paper we propose a Bayesian Market Maker for binary outcome (or continuous 01) markets that learns from the informational content of trades. By sacrificing the guarantee of bounded loss, the Bayesian Market Maker can simultaneously offer: (1) significantly lower expected loss at the same level of liquidity, and, (2) rapid convergence when there is a jump in the underlying true value of the security. We present extensive evaluations of the algorithm in experiments with intelligent trading agents and in human subject experiments. Our investigation also elucidates some general properties of market makers in prediction markets. In particular, there is an inherent tradeoff between adaptability to market shocks and convergence during market equilibrium.
ProfitCharging Market Makers with Bounded Loss, Vanishing Bid/Ask Spreads, and Unlimited Market Depth
"... Automated market makers are algorithmic agents that price fixedodds bets with traders. Four key qualities for automated market makers have appeared in the artificial intelligence literature: (1) bounded loss, (2) the ability to make a profit, (3) a vanishing bid/ask spread, and (4) unlimited market ..."
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Cited by 3 (0 self)
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Automated market makers are algorithmic agents that price fixedodds bets with traders. Four key qualities for automated market makers have appeared in the artificial intelligence literature: (1) bounded loss, (2) the ability to make a profit, (3) a vanishing bid/ask spread, and (4) unlimited market depth. Intriguingly, market makers that satisfy any three of these desiderata have appeared in the literature. However, it is an open question as to whether there exist market makers which can simultaneously satisfy all four of these properties; the issue is not simple to resolve because several of the qualities are oppositional, particularly in tandem. In this paper, we answer the open question in the affirmative by constructing market makers that satisfy all four qualities.
Inventorybased versus Priorbased Options Trading Agents
, 2012
"... Options are a basic, widelytraded form of financial derivative that offer payouts based on the future price of an underlying asset. The finance literature gives us optiontrading algorithms that take into consideration information about how prices move over time but do not explicitly involve the tr ..."
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Cited by 1 (1 self)
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Options are a basic, widelytraded form of financial derivative that offer payouts based on the future price of an underlying asset. The finance literature gives us optiontrading algorithms that take into consideration information about how prices move over time but do not explicitly involve the trades the agent made in the past. In contrast, the prediction market literature gives us automated marketmaking agents (like the popular LMSR) that are eventindependent and price trades based only on the inventories the agent holds. We simulate the performance of five trading agents inspired by these literatures on a large database of recent historical option prices. We find that a combination of the two approaches produced the best results in our experiments: a trading agent that keeps track of previouslymade trades combined with a good prior distribution on how prices move over time. The experimental success of this synthesized trader has implications for agent design in both financial and prediction markets.
Rational Market Making with Probabilistic Knowledge
"... A market maker sets prices over time for wagers that pay out contingent on the future state of the world. The market maker has knowledge of the probability of realizing each state of the world, and of how the price of a bet affects the probability that traders will accept it. We compare the optimal ..."
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A market maker sets prices over time for wagers that pay out contingent on the future state of the world. The market maker has knowledge of the probability of realizing each state of the world, and of how the price of a bet affects the probability that traders will accept it. We compare the optimal policy for riskneutral (expected utility maximizing) and Kelly criterion (expected logutility maximizing) market makers. Computing the optimal policy for a riskneutral market maker is relatively simple, while computing the optimal policy for a Kelly criterion market maker is challenging, requiring advanced techniques adapted from the computational economics literature to run efficiently. We show that while a riskneutral market maker has an optimal policy that does not depend on the market maker’s state, a Kelly criterion market maker’s optimal policy has an intricate dependence on both time and state. Counterintuitively, a Kelly criterion market maker may offer bets that are myopically irrational with respect to the market maker’s beliefs for the entire trading period. In contrast, a riskneutral market maker never offers a myopically irrational bet.
IOS Press Inventorybased versus Priorbased Options Trading Agents
"... Abstract. Options are a basic, widelytraded form of financial derivative that offer payouts based on the future price of an underlying asset. The finance literature gives us optiontrading algorithms that take into consideration information about how prices move over time but do not explicitly invo ..."
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Abstract. Options are a basic, widelytraded form of financial derivative that offer payouts based on the future price of an underlying asset. The finance literature gives us optiontrading algorithms that take into consideration information about how prices move over time but do not explicitly involve the trades the agent made in the past. In contrast, the prediction market literature gives us automated marketmaking agents (like the popular LMSR) that are eventindependent and price trades based only on the inventories the agent holds. We simulate the performance of five trading agents inspired by these literatures on a large database of recent historical option prices. We find that a combination of the two approaches produced the best results in our experiments: a trading agent that keeps track of previouslymade trades combined with a good prior distribution on how prices move over time. The experimental success of this synthesized trader has implications for agent design in both financial and prediction markets.
On Manipulation in Prediction Markets When Participants Influence Outcomes Directly
"... Prediction markets are popular mechanisms for aggregating information about a future event such as the outcome of an election. In situations where market participants may significantly influence the outcome (as in an election with a small number of voters), running the prediction market could change ..."
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Prediction markets are popular mechanisms for aggregating information about a future event such as the outcome of an election. In situations where market participants may significantly influence the outcome (as in an election with a small number of voters), running the prediction market could change the incentives of participants in the process that creates the outcome (e.g. agents may want to change their vote in the election). We propose a new gametheoretic model that captures two aspects of realworld prediction markets: (1) agents directly affect the outcome the market is predicting, (2) some outcomedeciders may not participate in the market. We show that this game has two different types of equilibria; when some outcomedeciders are unlikely to participate in the market, equilibrium prices reveal expected market outcomes conditional on market participants ’ private information, whereas when all outcomedeciders are likely to participate, equilibria are collusive – participants effectively coordinate in an uninformative and untruthful way. Finally, we suggest an approach towards incentivizing truthfulness by subsidizing agents appropriately using ideas from peer prediction.