Price Drivers in Prediction Markets: An Agent-Based Model of Competing Narratives

In this paper, I investigate price formation in prediction markets via an agent-based model (ABM). Prediction market prices can be interpreted as the probability of an event occurring, based on the aggregated beliefs of market participants. By utilizing a simple market exchange populated with opinionated agents and calibrating the model parameters, I aim to identify the effect on market price introduced by the three main drivers of the opinion formation process within two competing groups of agents: self-reinforcement; herding; and additive responses to inputs. Using a real-world dataset of Bitcoin prices, I show that both groups tend to follow the overall market sentiment. However, when the market mood aligns with a particular group’s opinion, that group becomes more self-reinforcing; conversely, when the mood does not favour their opinion, they become less self-reinforcing. Furthermore, I propose to use the temporally generated parameter values—produced by the calibrated model—as well as the temporal prices and market moods shifted by seven days as the training set for a supervised machine learning and solve the multi-target learning problem to forecast both short-term price trends and the expected trajectory of the two groups’ opinion dynamics. The code from this research is available for other researchers to use, build upon, and extend.