Analysis of the Impact of an Execution Algorithm with an Order Book Imbalance Strategy on a Financial Market Using an Agent-based Simulation

ArXiv ID: 2509.16912 “View on arXiv”

Authors: Shuto Endo, Takanobu Mizuta, Isao Yagi

Abstract

Order book imbalance (OBI) - buy orders minus sell orders near the best quote - measures supply-demand imbalance that can move prices. OBI is positively correlated with returns, and some investors try to use it to improve performance. Large orders placed at once can reveal intent, invite front-running, raise volatility, and cause losses. Execution algorithms therefore split parent orders into smaller lots to limit price distortion. In principle, using OBI inside such algorithms could improve execution, but prior evidence is scarce because isolating OBI’s effect in real markets is nearly impossible amid many external factors. Multi-agent simulation offers a way to study this. In an artificial market, individual actors are agents whose rules and interactions form the model. This study builds an execution algorithm that accounts for OBI, tests it across several market patterns in artificial markets, and analyzes mechanisms, comparing it with a conventional (OBI-agnostic) algorithm. Results: (i) In stable markets, the OBI strategy’s performance depends on the number of order slices; outcomes vary with how the parent order is partitioned. (ii) In markets with unstable prices, the OBI-based algorithm outperforms the conventional approach. (iii) Under spoofing manipulation, the OBI strategy is not significantly worse than the conventional algorithm, indicating limited vulnerability to spoofing. Overall, OBI provides a useful signal for execution. Incorporating OBI can add value - especially in volatile conditions - while remaining reasonably robust to spoofing; in calm markets, benefits are sensitive to slicing design.

Keywords: Order Book Imbalance (OBI), Multi-agent Simulation, Execution Algorithms, Artificial Markets, Spoofing Manipulation, Equity / General Market Microstructure

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper employs a complex agent-based simulation model with detailed mathematical formulations for agent behavior and market mechanisms, but it uses artificial data in a simulated environment rather than real historical backtests or live trading data.

  flowchart TD
    A["Research Goal:<br>Assess OBI's impact<br>on execution algorithms"] --> B["Methodology:<br>Multi-agent simulation<br>in artificial markets"]
    B --> C["Inputs:<br>Market conditions<br>(Stable, Volatile, Spoofing)"]
    C --> D["Process:<br>Compare OBI-strategy<br>vs. Conventional algorithm"]
    D --> E["Outcome 1:<br>Stable markets: Performance<br>depends on order slicing"]
    D --> F["Outcome 2:<br>Volatile markets:<br>OBI strategy outperforms"]
    D --> G["Outcome 3:<br>Spoofing: OBI shows<br>limited vulnerability"]