The Impact of Sequential versus Parallel Clearing Mechanisms in Agent-Based Simulations of Artificial Limit Order Book Exchanges

ArXiv ID: 2509.01683 “View on arXiv”

Authors: Matej Steinbacher, Mitja Steinbacher, Matjaz Steinbacher

Abstract

This study examines the impact of different computing implementations of clearing mechanisms on multi-asset price dynamics within an artificial stock market framework. We show that sequential processing of order books introduces a systematic and significant bias by affecting the allocation of traders’ capital within a single time step. This occurs because applying budget constraints sequentially grants assets processed earlier preferential access to funds, distorting individual asset demand and consequently their price trajectories. The findings highlight that while the overall price level is primarily driven by macro factors like the money-to-stock ratio, the market’s microstructural clearing mechanism plays a critical role in the allocation of value among individual assets. This underscores the necessity for careful consideration and validation of clearing mechanisms in artificial markets to accurately model complex financial behaviors.

Keywords: Market Microstructure, Clearing Mechanisms, Artificial Stock Market, Order Book Simulation, Price Dynamics, Equities

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper presents advanced mathematical formalism to prove a theoretical bias in sequential clearing mechanisms, but its empirical evaluation is limited to artificial simulations without real-market data, backtests, or statistical performance metrics.

  flowchart TD
    A["Research Goal"] --> B{"Methodology"}
    B --> C["Data / Inputs"]
    C --> D{"Computational Process"}
    D --> E{"Outcome / Finding"}
    
    style A fill:#e1f5fe
    style E fill:#e8f5e9
    
    subgraph A ["Research Goal"]
        A1["How does sequential vs parallel clearing<br>impact price dynamics in ABMs?"]
    end
    
    subgraph B ["Methodology"]
        B1["Agent-Based Simulation<br>Artificial Stock Market"]
    end
    
    subgraph C ["Data / Inputs"]
        C1["Market Parameters:<br>Money-to-Stock Ratio"]
        C2["Order Book Data:<br>Buy/Sell Orders"]
    end
    
    subgraph D ["Computational Process"]
        D1["Sequential Clearing<br>Sequential Budget Allocation"]
        D2["Parallel Clearing<br>Simultaneous Budget Allocation"]
    end
    
    subgraph E ["Key Findings"]
        E1["Sequential = Bias<br>Preferential Capital Access"]
        E2["Parallel = Neutral<br>Fair Allocation"]
        E3["Microstructure matters<br>for asset value allocation"]
    end

    %% Connections
    A1 --> B1
    B1 --> C1
    B1 --> C2
    C1 --> D1
    C1 --> D2
    C2 --> D1
    C2 --> D2
    D1 --> E1
    D2 --> E2
    E1 --> E3
    E2 --> E3