Modeling metaorder impact with a Non-Markovian Zero Intelligence model

ArXiv ID: 2503.05254 “View on arXiv”

Authors: Unknown

Abstract

Devising models of the limit order book that realistically reproduce the market response to exogenous trades is extremely challenging and fundamental in order to test trading strategies. We propose a novel explainable model for small tick assets, the Non-Markovian Zero Intelligence, which is a variant of the well-known Zero Intelligence model. The main modification is that the probability of limit orders’ signs (buy/sell) is not constant but is a function of the exponentially weighted mid-price return, representing the past price dynamics, and can be interpreted as the reaction of traders with reservation prices to the price trend. With numerical simulations and analytical arguments, we show that the model predicts a concave price path during a metaorder execution and to a price reversion after the execution ends, as empirically observed. We analyze in-depth the mechanism at the root of the arising concavity, the components which constitute the price impact in our model, and the dependence of the results on the two main parameters, namely the time scale and the strength of the reaction of traders to the price trend.

Keywords: limit order book, market microstructure, price impact, metaorder execution, zero intelligence models

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper employs advanced stochastic processes and analytical arguments to model the limit order book, but lacks direct backtesting, statistical metrics, or implementation details, relying instead on conceptual simulations and theoretical derivations.

  flowchart TD
    A["Research Goal<br>Develop an explainable LOB model<br>for metaorder price impact"] --> B["Methodology: Non-Markovian ZI Model"]
    B --> C["Key Innovation<br>Limited order sign = f<br>EWMA mid-price return"]
    B --> D["Simulation<br>Numerical LOB simulations"]
    B --> E["Validation<br>Analytical approximation"]
    C --> F
    D --> F["Analysis<br>Metaorder execution paths<br>Impact decomposition"]
    E --> F
    F --> G["Key Findings"]
    G --> H["1. Concave price impact during metaorder"]
    G --> I["2. Price reversion post-execution"]
    G --> J["3. Mechanism: Trend reaction creates liquidity imbalance"]
    G --> K["4. Dependence on time scale & reaction strength"]