预测市场种子资本从噪声主导流动中的回收
Prediction-Market Seed Capital Recovery from Noise-Dominant Flow
- McGill University（麦吉尔大学）
- MBZUAI（穆罕默德·本·扎耶德人工智能大学）
- The University of Manchester（曼彻斯特大学）
- The Fin AI
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出方向条件收费机制SCF，在预测市场中改善种子资本回收，通过集中费用于流动性驱动方向，提升预期回收能力，并刻画机制设计权衡。
AI中文摘要：
自动化预测市场要求赞助商在观察订单流之前预先提供流动性，这在启动时造成了融资挑战。我们研究是否基于可观察的支付方向的条件非负收费能够改善这笔预融资资本的回收，同时限制其对知情参与的影响。我们开发了种子资本流（SCF），一种方向条件征收，应用于一个包含知情交易者和流动性驱动交易者的简化二元成本函数市场。当订单组成在不同方向上存在差异时，SCF将允许的费用负担集中在相对更多流动性驱动流动的方向上，而统一费用则将其分散到两个方向。在足够严格的共同保留约束下，这种分配产生更高的预期回收能力，并可能使额外的流动性选择在财务上可行。合成数值审计检验了对替代流动模式、随机到达和标签错误指定的稳健性。结果刻画了一种机制设计权衡而非经验预测：市场仍然预先融资，回收是预期的而非保证的，且分析仅限于开放队列设置。
英文摘要：
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.
