Paper: arXiv 2303.00080

Authors: Zijian Shi, John Cartlidge

Abstract

Modern financial exchanges use an electronic limit order book (LOB) to store bid and ask orders for a specific financial asset. As the most fine-grained information depicting the demand and supply of an asset, LOB data is essential in understanding market dynamics. Therefore, realistic LOB simulations offer a valuable methodology for explaining empirical properties of markets. Mainstream simulation models include agent-based models (ABMs) and stochastic models (SMs). However, ABMs tend not to be grounded on real historical data, while SMs tend not to enable dynamic agent-interaction. To overcome these limitations, we propose a novel hybrid LOB simulation paradigm characterised by: (1) representing the aggregation of market events’ logic by a neural stochastic background trader that is pre-trained on historical LOB data through a neural point process model; and (2) embedding the background trader in a multi-agent simulation with other trading agents. We instantiate this hybrid NS-ABM model using the ABIDES platform. We first run the background trader in isolation and show that the simulated LOB can recreate a comprehensive list of stylised facts that demonstrate realistic market behaviour. We then introduce a population of trend' and value’ trading agents, which interact with the background trader. We show that the stylised facts remain and we demonstrate order flow impact and financial herding behaviours that are in accordance with empirical observations of real markets.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper introduces an innovative hybrid approach combining neural point processes with agent-based simulation (ABIDES) to model Limit Order Books. It demonstrates strong novelty and empirical rigor by successfully validating market stylized facts, order flow impact, and herding behaviors against real-world LOB dynamics.

Research Flowchart

  flowchart TD
    A["Research Goal: Create realistic LOB simulation combining data-driven dynamics with agent interactions"] --> B["Data & Model Input: Pre-train Neural Point Process background trader on historical LOB data"]
    B --> C["Implementation: Embed neural stochastic background trader into ABIDES framework"]
    C --> D["Phase 1: Run background trader in isolation to validate market dynamics"]
    C --> E["Phase 2: Introduce Trend and Value trading agents into multi-agent environment"]
    D & E --> F["Outcomes & Findings: Recreated key stylised facts, verified order flow impact, and observed financial herding"]