# Modelling Opaque Bilateral Market Dynamics in Financial Trading: Insights from a Multi-Agent Simulation Study

arXiv: https://arxiv.org/abs/2405.02849 · HTML: https://mlsanity.com/paper/2405.02849/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 54 references that are in the map.
- First posted on arXiv 5 May 2024.

## Builds on (most cited first, 13 of 13)

- [Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification](https://mlsanity.com/paper/1802.00850/) (2018)
- [Reinforcement Learning for Market Making in a Multi-agent Dealer Market](https://mlsanity.com/paper/1911.05892/) (2019)
- [Toward Policy Explanations for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2204.12568/) (2022)
- [Towards a fully RL-based Market Simulator](https://mlsanity.com/paper/2110.06829/) (2021)
- [Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations](https://mlsanity.com/paper/2210.07184/) (2022)
- [How to Sell Information Optimally: an Algorithmic Study](https://mlsanity.com/paper/2011.14570/) (2020)
- [Multi-Level Mesa](https://mlsanity.com/paper/1904.08315/) (2019)
- [Explainability in Mechanism Design: Recent Advances and the Road Ahead](https://mlsanity.com/paper/2206.03031/) (2022)
- [Economic Efficiency Requires Interaction](https://mlsanity.com/paper/1311.4721/) (2013)
- [The Specification of Sugarscape](https://mlsanity.com/paper/1505.06012/) (2015)
- [Predicting the Behavior of Dealers in Over-The-Counter Corporate Bond Markets](https://mlsanity.com/paper/2103.09098/) (2021)
- [Growing the Simulation Ecosystem: Introducing Mesa Data to Provide Transparent, Accessible and Extensible Data Pipelines for Simulation Development](https://mlsanity.com/paper/2104.02809/) (2021)
- [Some challenges of calibrating differentiable agent-based models](https://mlsanity.com/paper/2307.01085/) (2023)

## Built on by (most cited first, 2 of 2)

- [Evaluating Binary Decision Biases in Large Language Models: Implications for Fair Agent-Based Financial Simulations](https://mlsanity.com/paper/2501.16356/) (2025)
- [TraderTalk: An LLM Behavioural ABM applied to Simulating Human Bilateral Trading Interactions](https://mlsanity.com/paper/2410.21280/) (2024)

## Abstract (arXiv)

Exploring complex adaptive financial trading environments through multi-agent based simulation methods presents an innovative approach within the realm of quantitative finance. Despite the dominance of multi-agent reinforcement learning approaches in financial markets with observable data, there exists a set of systematically significant financial markets that pose challenges due to their partial or obscured data availability. We, therefore, devise a multi-agent simulation approach employing small-scale meta-heuristic methods. This approach aims to represent the opaque bilateral market for Australian government bond trading, capturing the bilateral nature of bank-to-bank trading, also referred to as "over-the-counter" (OTC) trading, and commonly occurring between "market makers". The uniqueness of the bilateral market, characterized by negotiated transactions and a limited number of agents, yields valuable insights for agent-based modelling and quantitative finance. The inherent rigidity of this market structure, which is at odds with the global proliferation of multilateral platforms and the decentralization of finance, underscores the unique insights offered by our agent-based model. We explore the implications of market rigidity on market structure and consider the element of stability, in market design. This extends the ongoing discourse on complex financial trading environments, providing an enhanced understanding of their dynamics and implications.