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

Arwa Bokhari, Arwa Bokhari

2025

Abstract

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.

Paper Citation

in Harvard Style

Bokhari A. (2025). Price Drivers in Prediction Markets: An Agent-Based Model of Competing Narratives. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 1124-1131. DOI: 10.5220/0013266100003890

in Bibtex Style

@conference{icaart25,
author={Arwa Bokhari},
title={Price Drivers in Prediction Markets: An Agent-Based Model of Competing Narratives},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={1124-1131},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013266100003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Price Drivers in Prediction Markets: An Agent-Based Model of Competing Narratives
SN - 978-989-758-737-5
AU - Bokhari A.
PY - 2025
SP - 1124
EP - 1131
DO - 10.5220/0013266100003890
PB - SciTePress