发表机构
Princeton University; Kaleidoscope Blockchain（普林斯顿大学; Kaleidoscope 区块链）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究如何在预测市场提供连投产品，提出自动连投做市商APMM，采用分层参数化避免信息冗余，给出不同情况下做市商损失界限，并通过模拟和历史订单流验证，为预测市场提供连投产品提供了有效方法。
AI 中文摘要
连投是博彩市场中交易最活跃的产品之一，但预测市场难以原生提供。本文在M个二元事件之上提供完整的连投组合系列作为流动市场，将做市商补贴市场的损失限制在O(M^2)。任何单个连投吸引的交易者少，是固有薄市场，对数市场评分规则是薄市场的自然机制。运行多个LMSR会造成信息冗余，我们提出自动连投做市商（APMM），它使用分层参数化，一个定价更新可使相关连投一致更新。我们做出三点贡献：一是引入APMM；二是表明在不同情况下做市商最坏情况损失分别为O(M^2)和O(M)；三是通过模拟和历史订单流验证了这些界限。
英文摘要
Parlays - joint contracts on the simultaneous resolution of several events - are among the most heavily traded products in betting markets, but prediction markets have struggled to offer them natively. The family of parlays over $M$ binary events is exponentially large, and the mechanisms that could price it either cannot be computed at that scale or need to pay for the same information multiple times. We start from an empirical observation that reframes the obstacle. On Kalshi order flow, most of the trades are placed on low-order parlays. For example, on Kalshi 7 days of traded parlay data, $87.03\%$ of the trades have at most 13 legs and $95.13\%$ have at most 20, with trade counts bounded by geometrical decay in increasing leg numbers. Given this concentration, and the structural correlation between parlays that share legs, current designs waste most of either their computation or their subsidy. We propose Low-Order Parlay Market Maker (LOPMM), which keeps a separate book for each combination of legs to stay tractable, then shares information from lower-order parlays into the higher-order ones containing them to reduce the market maker's loss. We formalize the observed concentration as a low-order flow condition. Empirically, LOPMM substantially reduces the cost of supporting parlay trading. On synthetic low-order flow, its losses remain close to that of running the base markets alone. On a replay of 83 Kalshi NBA games, independent parlay books lose \$174,050 in aggregate while LOPMM earns \$31 under the same liquidity parameter. Its computational cost depends on the order (m) of the requested parlay rather than the number (M) of base events, requiring $Θ(4^m)$ work per quote and $Θ(5^m)$ per full trade. We complement these empirical results with theoretical guarantees ...
