2010/09/07 by Aseem Brahma, Brahma, Aseem, Sanmay Das +4
Computer Science · Decision Sciences · Economics, Econometrics and Finance · Mathematics · #Adaptability #Algorithmic trading #Artificial Intelligence (cs.AI) #Bounded function #Business #Complex Systems and Time Series Analysis #Computational Engineering #Computer science #Convergence (economics) #Econometrics #Economics #FOS: Computer and information sciences #FOS: Economics and business #Finance #Financial Markets and Investment Strategies #Financial economics #J.4 #Machine learning #Market impact #Market liquidity #Market maker #Market microstructure #Mathematics #Order (exchange) #Population #Prediction market #Random walk #Sports Analytics and Performance #Stability (learning theory) #Stock Market Forecasting Methods #Trading and Market Microstructure (q-fin.TR) #and Science (cs.CE) #cs.AI #cs.CE #q-fin.TR
paper · pdf · doi:10.48550/arxiv.1009.1446
published in arXiv (Cornell University) (Cornell University)
arxiv created 2010/09/08 · openalex publication_date 2010/09/08 · arxiv updated 2010/09/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.