A Network Simulation of OTC Markets with Multiple Agents

TL;DR

This paper introduces an agent-based OTC market simulation incorporating network topology, market makers, and AI-driven traders, revealing how market structure influences price dynamics and potential fragmentation.

Contribution

It presents a novel network-based OTC market model integrating neural network-based trend investors and static value investors, enabling analysis of market structure effects on price behavior.

Findings

Distribution of price changes is fat-tailed, resembling real markets

Market volatility exhibits auto-correlation patterns

Market fragmentation occurs at a critical connectivity threshold

Abstract

We present a novel agent-based approach to simulating an over-the-counter (OTC) financial market in which trades are intermediated solely by market makers and agent visibility is constrained to a network topology. Dynamics, such as changes in price, result from agent-level interactions that ubiquitously occur via market maker agents acting as liquidity providers. Two additional agents are considered: trend investors use a deep convolutional neural network paired with a deep Q-learning framework to inform trading decisions by analysing price history; and value investors use a static price-target to determine their trade directions and sizes. We demonstrate that our novel inclusion of a network topology with market makers facilitates explorations into various market structures. First, we present the model and an overview of its mechanics. Second, we validate our findings via comparison to the real-world: we demonstrate a fat-tailed distribution of price changes, auto-correlated volatility, a skew negatively correlated to market maker positioning, predictable price-history patterns and more. Finally, we demonstrate that our network-based model can lend insights into the effect of market-structure on price-action. For example, we show that markets with sparsely connected intermediaries can have a critical point of fragmentation, beyond which the market forms distinct clusters and arbitrage becomes rapidly possible between the prices of different market makers. A discussion is provided on future work that would be beneficial.