Prediction-Market Seed Capital Recovery from Noise-Dominant Flow

2026-09-12

AI Summary

研究通过引入基于可观察收益方向的非负收费机制，即种子资本流动(SCF)，来提高自动化预测市场中预付资本的回收率，同时减少对知情参与者的影响。

Abstract

Automated prediction markets require sponsors to prefund liquidity before observing order flow, creating a financing challenge at launch. We study whether nonnegative charges conditioned on observable payoff direction can improve recovery of this prefunded capital while limiting their effect on informed participation. We develop Seed Capital Flow (SCF), a direction-conditioned levy, in a stylized binary cost-function market with informed and liquidity-motivated traders. When order composition differs across directions, SCF concentrates the permitted fee burden on the direction with relatively more liquidity-motivated flow, whereas a uniform fee spreads it across both directions. Under a sufficiently tight common retention constraint, this allocation yields higher expected recovery capacity and can make additional liquidity choices financially viable. Synthetic numerical audits examine robustness to alternative flow patterns, stochastic arrivals, and label misspecification. The results characterize a mechanism-design tradeoff rather than an empirical prediction: the market remains prefunded, recovery is expected rather than guaranteed, and the analysis is limited to an opening-cohort setting.

Problem

• prediction markets
• seed capital recovery
• liquidity provision
• nonnegative charges
• informed participation

Innovation

• Seed Capital Flow
• direction-conditioned levy
• liquidity-motivated flow
• capital recovery