Restocchi, V.; McGroarty, F.; Gerding, E.; Brede, M. Opinion Dynamics Explain Price Formation in Prediction Markets. Entropy2023, 25, 1152.
Restocchi, V.; McGroarty, F.; Gerding, E.; Brede, M. Opinion Dynamics Explain Price Formation in Prediction Markets. Entropy 2023, 25, 1152.
Restocchi, V.; McGroarty, F.; Gerding, E.; Brede, M. Opinion Dynamics Explain Price Formation in Prediction Markets. Entropy2023, 25, 1152.
Restocchi, V.; McGroarty, F.; Gerding, E.; Brede, M. Opinion Dynamics Explain Price Formation in Prediction Markets. Entropy 2023, 25, 1152.
Abstract
Prediction markets are heralded as powerful forecasting tools, but models to describe them often fail to capture the full complexity of the underlying mechanisms that drive price dynamics. To address this issue, we propose a model in which agents belong in a social network, have an opinion about the probability of a particular event to occur, and bet on the prediction market accordingly. Agents update their opinions about the event by interacting with their neighbours in the network, following the Deffuant model of opinion dynamics. Our results suggest that a simple market model that takes into account opinion formation dynamics is capable to replicate the empirical properties of historical prediction market time series, including volatility clustering and fat-tailed distribution of returns. Interestingly, the best results are obtained when there is the right level of variance in the opinions of the agents. Moreover, this paper provides a new way to indirectly validate opinion dynamics models against real data by using historical data obtained from PredictIt, an exchange platform whose data has never been used before to validate models of opinion diffusion.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
