Comparing Prediction Market Structures, With an Application to Market Making

TL;DR

This paper introduces a novel experimental framework to compare prediction market structures, specifically evaluating LMSR and a new Bayesian market maker, revealing tradeoffs in stability, loss, and adaptability.

Contribution

It presents a systematic experimental design for fair comparison of market makers and evaluates two algorithms, highlighting their respective advantages and limitations.

Findings

BMM offers better price stability and lower expected loss than LMSR under similar liquidity conditions.

BMM does not guarantee bounded loss, unlike LMSR.

Tradeoff identified between market adaptability and convergence stability.

Abstract

Ensuring sufficient liquidity is one of the key challenges for designers of prediction markets. Various market making algorithms have been proposed in the literature and deployed in practice, but there has been little effort to evaluate their benefits and disadvantages in a systematic manner. We introduce a novel experimental design for comparing market structures in live trading that ensures fair comparison between two different microstructures with the same trading population. Participants trade on outcomes related to a two-dimensional random walk that they observe on their computer screens. They can simultaneously trade in two markets, corresponding to the independent horizontal and vertical random walks. We use this experimental design to compare the popular inventory-based logarithmic market scoring rule (LMSR) market maker and a new information based Bayesian market maker (BMM). Our experiments reveal that BMM can offer significant benefits in terms of price stability and expected loss when controlling for liquidity; the caveat is that, unlike LMSR, BMM does not guarantee bounded loss. Our investigation also elucidates some general properties of market makers in prediction markets. In particular, there is an inherent tradeoff between adaptability to market shocks and convergence during market equilibrium.