Optimizing the liquidity parameter of logarithmic market scoring rules prediction markets

Abstract

Purpose Prediction markets are techniques to aggregate dispersed public opinions via market mechanisms to predict uncertain future events’ outcome. Many experiments have shown that prediction markets outperform other traditional forecasting methods in terms of accuracy. Logarithmic market scoring rules (LMSR) is one of the most simple and widely used market mechanisms; however, market makers have to confront crucial design decisions including the setting of the parameter “b” or the “liquidity parameter” in the price functions. As the liquidity parameter has significant effects on the market performance, this paper aims to provide a comprehensive basis for the setting of the parameter. Design/methodology/approach The analyses include the effects of the liquidity parameter on the forecast standard error and the amount of time for the market price to converge to the true value. These experiments use artificial prediction markets, the proposed simulation models that mimic real prediction markets. Findings The simulation results indicate that prediction market’s forecast standard error decreases as the value of the liquidity parameter increases. Moreover, for any given number of traders in the market, there exists an optimal liquidity parameter value that yields appropriate price adaptability and leads to the fastest price convergence. Originality/value Understanding these tradeoffs, the market makers can effectively determine the liquidity parameter value under various objectives on the standard error, the time to convergence and cost.

Design and Analysis of a Synthetic Prediction Market using Dynamic Convex Sets

“…This is the softmax function (Boltzmann distribution with constant β = k/T for fixed k and T ) of the inputs (q 0 t , q 1 t ). The β term is a liquidity factor [54] that adjusts the amount the price will increase or decrease given a change in the asset quantities. By using a Boltzmann distribution, the prices can be interpreted as probabilities.…”