---
title: Spoofing and Manipulating Order Books with Learning Algorithms
url: https://www.ml-quant.com/papers/ssrn/4639959/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4639959
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4639959
featured: 2023-11-29
citations: 4
topic: Trading, Microstructure & Execution
---


# Spoofing and Manipulating Order Books with Learning Algorithms

The paper presents a model to test if a trading algorithm can manipulate the limit order book, concluding that market conditions can allow such manipulation.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4639959
- Identifier: SSRN 4639959
- Released: 2023-11-21
- First featured: Quant Letter No. 27 (2023-11-29): https://www.ml-quant.com/issues/2023-11-29/
- Citations (Semantic Scholar): 4
- Published in: not yet
- Topic: Trading, Microstructure & Execution

## Related

- [Limit Order Book Dynamics and Order Size Modelling Using Compound Hawkes Process](https://www.ml-quant.com/papers/arxiv/2312.08927/): The research introduces a new method using Compound Hawkes Process to model Limit Order Book dynamics, taking into account order size and maintaining a positive spread.
- [Non-uniformly sampled simulated price impact of an order-book](https://www.ml-quant.com/papers/arxiv/2310.06079/): Order Book Simulation: The paper expands a numerical method to simulate the spread of financial market orders, showing the price impact of flash limit-orders and market orders, and advocates for non-uniform sampling in diffusive dynamics simulations.
- [Equity auction dynamics: latent liquidity models with activity acceleration](https://www.ml-quant.com/papers/arxiv/2401.06724/): Liquidity Models: The study applies the latent/revealed order book framework to equity auctions, showing no indicative price predictability and providing accurate model parameter measurements.
- [JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading](https://www.ml-quant.com/papers/arxiv/2308.13289/): JAX-LOB: The paper introduces JAX-LOB, the first GPU-powered limit order book simulator capable of processing multiple books simultaneously, designed for efficient large-scale simulations of LOB dynamics for research, calibration, and reinforcement learning training.
- [Fill Probabilities in a Limit Order Book with State-Dependent Stochastic Order Flows](https://www.ml-quant.com/papers/arxiv/2403.02572/): A new stochastic model accurately calculates fill probabilities for limit orders at different price levels in the order book, effectively capturing its dynamics.
- [Limit Order Book Simulations: A Review](https://www.ml-quant.com/papers/ssrn/4745587/): The piece reviews models of Limit Order Books simulations, emphasizing the role of AI in improving these models and the significance of price impacts in algorithmic trading.