APMM: Automated Parlay Market Maker
For prediction market operators, APMM offers a practical way to support complex parlay markets with bounded subsidy costs, addressing the long-standing challenge of native parlay trading.
The paper introduces the Automated Parlay Market Maker (APMM), which enables liquid trading of all combinatorial parlays on M binary events while bounding market maker loss to O(M^2). Simulations and historical data from Kalshi confirm that APMM's hierarchical parameterization reduces redundancy and maintains advantage under real trading patterns.
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. In this paper, we offer the full combinatorial family of parlays on top of $M$ binary events, as liquid markets, bounding the market maker loss for subsidizing the markets to $O(M^2)$. Any single parlay attracts few traders, so each is an inherently thin market, and the logarithmic market scoring rule (LMSR) is the natural mechanism for thin markets. But running a separate LMSR for each of the exponentially many parlays forces the market maker to pay for the same information many times over. We show that a market maker which automatically propagates information across related parlays avoids this redundancy. We make three contributions. First, we introduce the automated parlay market maker (APMM), which uses a \emph{hierarchical parameterization}: the state of each low-leg parlay is shared into every higher-leg parlay that contains it, so pricing one updates all the others coherently. Second, we show that when informed trading is concentrated in parlays with few legs, the market maker's worst-case loss is $O(M^2)$, and it falls to $O(M)$ when the traded parlays are also spread across different clusters rather than concentrated on a few. Third, we validate these bounds in simulation and on historical Kalshi order flow, confirming that real belief updates are dominated by low-leg changes and that APMM's advantage persists under real trading patterns.