A Practical Liquidity-Sensitive Automated Market Maker

Othman, Abraham; Pennock, David M.; Reeves, Daniel M.; Sandholm, Tüomas

doi:10.1145/2509413.2509414

Cited by 42 publications

(33 citation statements)

References 19 publications

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“…As the choice of the liquidity parameter controls many aspects of market performance and behaviors, determining an appropriate value of the liquidity parameter could be subjective and complicated. Setting the value of the liquidity parameter is considered more of an art than a science (Othman et al , 2010). It is difficult to determine the value the liquidity parameter that yields appropriate market liquidity, or price swings, in a particular market setting.…”

Section: Related Workmentioning

confidence: 99%

“…These dilemmas include the setting of the parameter “ b ” or “liquidity parameter” (Chen and Pennock, 2010), which plays a vital role in the market price calculation and behaviors of the market including the market liquidity, the price adaptability and the cost to market makers. As this parameter has significant effects on the market performance, setting the value of the liquidity parameter to manage these tradeoffs is considered more of an art than a science (Brahma et al , 2012 and Chen and Pennock, 2010, Othman et al , 2010), and thus, few studies focus on how to set this parameter.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing the liquidity parameter of logarithmic market scoring rules prediction markets

Lekwijit

Sutivong

2018

JM2

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Purpose Prediction markets are techniques to aggregate dispersed public opinions via market mechanisms to predict uncertain future events’ outcome. Many experiments have shown that prediction markets outperform other traditional forecasting methods in terms of accuracy. Logarithmic market scoring rules (LMSR) is one of the most simple and widely used market mechanisms; however, market makers have to confront crucial design decisions including the setting of the parameter “b” or the “liquidity parameter” in the price functions. As the liquidity parameter has significant effects on the market performance, this paper aims to provide a comprehensive basis for the setting of the parameter. Design/methodology/approach The analyses include the effects of the liquidity parameter on the forecast standard error and the amount of time for the market price to converge to the true value. These experiments use artificial prediction markets, the proposed simulation models that mimic real prediction markets. Findings The simulation results indicate that prediction market’s forecast standard error decreases as the value of the liquidity parameter increases. Moreover, for any given number of traders in the market, there exists an optimal liquidity parameter value that yields appropriate price adaptability and leads to the fastest price convergence. Originality/value Understanding these tradeoffs, the market makers can effectively determine the liquidity parameter value under various objectives on the standard error, the time to convergence and cost.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing the liquidity parameter of logarithmic market scoring rules prediction markets

Lekwijit

Sutivong

2018

JM2

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“…A market maker uses an MSR to calculate the aggregated price of a security. Recently, there has been considerable interest in analyzing marker scoring rules [Chen and Pennock 2007;Hanson 2007;Othman et al 2010], and the LMSR has been shown to guarantee truthful revelation of beliefs by the trading agents [Hanson 2007]. The LMSR allows a security's price, and payoffs to agents buying/selling the security, to be expressed in terms of the purchased or outstanding quantity.…”

Section: Preliminariesmentioning

confidence: 99%

Automated Pricing in a Multiagent Prediction Market Using a Partially Observable Stochastic Game

Jumadinova

Dasgupta

2015

ACM Trans. Intell. Syst. Technol.

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Prediction markets offer an efficient market-based mechanism to aggregate large amounts of dispersed or distributed information from different people to predict the possible outcome of future events. Recently, automated prediction markets where software trading agents perform market operations such as trading and updating beliefs on behalf of humans have been proposed. A challenging aspect in automated prediction markets is to develop suitable techniques that can be used by automated trading agents to update the price at which they should trade securities related to an event so that they can increase their profit. This problem is nontrivial, as the decision to trade and the price at which trading should occur depends on several dynamic factors, such as incoming information related to the event for which the security is being traded, the belief-update mechanism and risk attitude of the trading agent, and the trading decision and trading prices of other agents. To address this problem, we have proposed a new behavior model for trading agents based on a game-theoretic framework called partially observable stochastic game with information (POSGI). We propose a correlated equilibrium (CE)-based solution strategy for this game that allows each agent to dynamically choose an action (to buy or sell or hold) in the prediction market. We have also performed extensive simulation experiments using the data obtained from the Intrade prediction market for four different prediction markets. Our results show that our POSGI model and CE strategy produces prices that are strongly correlated with the prices of the real prediction markets. Results comparing our CE strategy with five other strategies commonly used in similar market show that our CE strategy improves price predictions and provides higher utilities to the agents compared to other existing strategies.

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“…As natively digital assets continue to grow, there is an increasing need for mechanisms of exchange similar to those found in traditional financial markets. These digital assets, which include online ad impressions, cryptocurrencies, and prediction market bets, are often complex to interact with and suffer from low liquidity [30]. Over the last decade, a number of designs for automated market makers (AMMs) have been proposed to reduce this complexity.…”

Section: Introductionmentioning

confidence: 99%

Improved Price Oracles

Angeris

Chitra²

2020

Proceedings of the 2nd ACM Conference on Advances in Financial Technologies

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Automated market makers, first popularized by Hanson's logarithmic market scoring rule (or LMSR) for prediction markets, have become important building blocks, called 'primitives,' for decentralized finance. A particularly useful primitive is the ability to measure the price of an asset, a problem often known as the pricing oracle problem. In this paper, we focus on the analysis of a very large class of automated market makers, called constant function market makers (or CFMMs) which includes existing popular market makers such as Uniswap, Balancer, and Curve, whose yearly transaction volume totals to billions of dollars. We give sufficient conditions such that, under fairly general assumptions, agents who interact with these constant function market makers are incentivized to correctly report the price of an asset and that they can do so in a computationally efficient way. We also derive several other useful properties that were previously not known. These include lower bounds on the total value of assets held by CFMMs and lower bounds guaranteeing that no agent can, by any set of trades, drain the reserves of assets held by a given CFMM.

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A Practical Liquidity-Sensitive Automated Market Maker

Cited by 42 publications

References 19 publications

Optimizing the liquidity parameter of logarithmic market scoring rules prediction markets

Optimizing the liquidity parameter of logarithmic market scoring rules prediction markets

Automated Pricing in a Multiagent Prediction Market Using a Partially Observable Stochastic Game

Improved Price Oracles

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