# Explaining Agent-Based Financial Market Simulation

arXiv: https://arxiv.org/abs/1909.11650 · HTML: https://mlsanity.com/paper/1909.11650/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2019.

## Built on by (most cited first, 10 of 10)

- [Towards Realistic Market Simulations: a Generative Adversarial Networks Approach](https://mlsanity.com/paper/2110.13287/) (2021)
- [Multi-Modal Financial Time-Series Retrieval Through Latent Space Projections](https://mlsanity.com/paper/2309.16741/) (2023)
- [Efficient Calibration of Multi-Agent Simulation Models from Output Series with Bayesian Optimization](https://mlsanity.com/paper/2112.03874/) (2021)
- [Learning who is in the market from time series: market participant discovery through adversarial calibration of multi-agent simulators](https://mlsanity.com/paper/2108.00664/) (2021)
- [TADACap: Time-series Adaptive Domain-Aware Captioning](https://mlsanity.com/paper/2504.11441/) (2025)
- [Towards Interpretable Time Series Foundation Models](https://mlsanity.com/paper/2507.07439/) (2025)
- [Calibrating Over-Parametrized Simulation Models: A Framework via Eligibility Set](https://mlsanity.com/paper/2105.12893/) (2021)
- [Visual Time Series Forecasting: An Image-driven Approach](https://mlsanity.com/paper/2107.01273/) (2021)
- [Similarity metrics for Different Market Scenarios in Abides](https://mlsanity.com/paper/2107.09352/) (2021)
- [CTMSTOU driven markets: simulated environment for regime-awareness in trading policies](https://mlsanity.com/paper/2202.00941/) (2022)

## Abstract (arXiv)

This paper is intended to explain, in simple terms, some of the mechanisms and agents common to multiagent financial market simulations. We first discuss the necessity to include an exogenous price time series ("the fundamental value") for each asset and three methods for generating that series. We then illustrate one process by which a Bayesian agent may receive limited observations of the fundamental series and estimate its current and future values. Finally, we present two such agents widely examined in the literature, the Zero Intelligence agent and the Heuristic Belief Learning agent, which implement different approaches to order placement.