Adaptive Liquidity in Prediction Markets via Online Learning

2026-05-10

AI Summary

Existing prediction markets employ fixed liquidity mechanisms that struggle to adapt to non-stationary trading environments, forcing a static trade-off between price responsiveness and worst-case loss. This work formulates liquidity adjustment as an online learning problem and introduces an adaptive market mechanism based on a learnable weighted mixture of cost functions, guided by structural risk signals for weight updates. The proposed approach achieves a switching regret bound relative to the best sequence of liquidity parameters in hindsight while preserving key desiderata: no arbitrage, bounded worst-case loss, strong expressiveness, and the potential for positive returns. Simulations demonstrate that the mechanism dynamically adjusts liquidity in response to order flow and inventory levels, substantially enhancing adaptability in volatile environments.

Abstract

Prediction markets rely on liquidity to convert trades into informative prices, yet existing mechanisms fix liquidity ex ante. This restriction enforces a static trade-off between price responsiveness and worst-case loss despite inherently nonstationary trading conditions. We propose a fundamentally different approach that treats liquidity selection itself as an online learning problem. Our mechanism mixes a family of cost-function markets via learnable weights, yielding a single adaptive market that preserves no-arbitrage, bounded worst-case loss, expressiveness, and positive upside. We introduce a hybrid structural risk signal, a per-round objective that quantifies the trade-off between price impact and inventory risk, and show that standard online learning algorithms achieve switching-regret guarantees relative to the best sequence of liquidity regimes in hindsight. Simulations demonstrate that the mechanism adaptively shifts liquidity across regimes in response to both order flow and inventory dynamics. Our results establish a principled framework for adaptive liquidity, connecting prediction market design with online learning.

Problem

• prediction markets
• liquidity
• online learning
• nonstationary trading conditions
• price responsiveness

Innovation

• adaptive liquidity
• prediction markets
• online learning
• cost-function markets
• switching regret