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Uniform-Loss Automated Market Making for Prediction Markets
Ciamac C. Moallemi, Dan Robinson, Brian Zhu
https://arxiv.org/abs/2607.17428 https://arxiv.org/pdf/2607.17428 https://arxiv.org/html/2607.17428
arXiv:2607.17428v1 Announce Type: new
Abstract: Automated market makers (AMMs) for prediction markets descend from market scoring rules, where a mechanism operator subsidizes a market to aggregate beliefs about uncertain events. The existing literature has focused on bounding the total worst-case loss to the subsidizer, but has not addressed how that loss is distributed across price states or over time. We use the framework of loss-versus-rebalancing (LVR) to study this distribution and introduce \textit{uniform AMMs}, defined by the property that instantaneous LVR is proportional to pool value and independent of the current token price. In a static setting, we show that for a broad class of \textit{win-martingales} -- processes that converge to 0 or 1 at a fixed resolution time -- there exists a pricing function that achieves uniform LVR under that process, and conversely, that any sufficiently regular pricing function induces a win-martingale under which it is uniform. We then extend the framework to dynamic liquidity management, showing that liquidity levels can be adjusted over time to implement a prescribed target expected cumulative loss schedule. This theory is illustrated with canonical examples of win-martingales and pricing functions. Our results can inform AMM designers and liquidity providers on how the inevitable cost of subsidizing price discovery can be shaped and controlled across both price and time.
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Optimal Market Making in Prediction Markets
Dominik Feil, Max Nendel
https://arxiv.org/abs/2607.17991 https://arxiv.org/pdf/2607.17991 https://arxiv.org/html/2607.17991
arXiv:2607.17991v1 Announce Type: new
Abstract: Prediction markets are attracting growing attention as trading volumes rise and their practical relevance increases. To ensure efficient price discovery, liquidity provision becomes ever more important. Due to the binary settlement structure in prediction markets, optimal market making leads to an optimization problem that is fundamentally different from the ones studied in classical settings. In this paper, we develop a stochastic control framework for prediction markets in which the market price is modeled as a conditional probability of the outcome that is generated by a transformed latent belief diffusion. A market maker selects bid and ask quotes to maximize expected terminal wealth while controlling both mark-to-market inventory risk and the settlement risk of remaining positions at resolution. We derive the associated Hamilton--Jacobi--Bellman equation and characterize the unique optimal bid and ask quotes. By transforming the equation to the latent belief space and using a fixed-point argument, we prove existence and uniqueness of a classical solution and verify the resulting optimal quoting strategy. In addition, we provide a numerical analysis, which reveals how optimal liquidity provision in prediction markets depends on inventory, market beliefs, time to resolution, and risk aversion. Further, we demonstrate that the optimal quoting strategy substantially improves downside protection while preserving most of its expected profit relative to a myopic benchmark that maximizes the instantaneous expected mark-to-market profit.
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Replaced article(s) found for q-fin.TR. https://arxiv.org/list/q-fin.TR/new
[1/1]:
- A Validated Volatility-Volume-Gap Classifier for Regime Identification in MNQ Intraday Data
Mathias Mesfin
https://arxiv.org/abs/2605.11423 https://mastoxiv.page/@arXiv_qfinTR_bot/116566203784556024
- Signature-Based Optimal Execution for Statistical Arbitrage with Path-Dependent Trading Signals
Gianmarco Morbelli, Sven Karbach, Mike Derksen
https://arxiv.org/abs/2606.31387 https://mastoxiv.page/@arXiv_qfinTR_bot/116843601758653140
- When large trades are not (automatically) news: Liquidity tail risk and price discovery
Umut \c{C}etin, Mingwei Lin, Giulia Livieri
https://arxiv.org/abs/2607.01198 https://mastoxiv.page/@arXiv_qfinTR_bot/116849281748508156
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