---
title: Fractional Trading's Impact on Order Book Dynamics
url: https://www.ml-quant.com/papers/ssrn/4518690/
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 4518690
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4518690
featured: 2023-07-26
citations: unknown
topic: Trading, Microstructure & Execution
---


# Fractional Trading's Impact on Order Book Dynamics

The implementation of fractional trading in stock markets has significantly affected price levels and order book dynamics, potentially changing the investment habits of nonprofessional investors.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4518690
- Identifier: SSRN 4518690
- Released: 2022-10-31
- First featured: Quant Letter No. 9 (2023-07-26): https://www.ml-quant.com/issues/2023-07-26/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Trading, Microstructure & Execution

## Related

- [Estimation of an Order Book Dependent Hawkes Process for Large Datasets](https://www.ml-quant.com/papers/arxiv/2307.09077/): A new high-frequency trading model uses a Hawkes process and high-dimensional functions from the order book, capable of handling billions of data points and tested on four NYSE stocks.
- [Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness](https://www.ml-quant.com/papers/arxiv/2306.12806/): Conditional generative models used for order book simulation, enhanced with adversarial attacks.
- [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.
- [Deep attentive survival analysis in limit order books: estimating fill probabilities with convolutional-transformers](https://www.ml-quant.com/papers/arxiv/2306.05479/): A deep learning method outperforms other approaches in estimating filltimes of limit orders.
- [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.
- [Spoofing and Manipulating Order Books with Learning Algorithms](https://www.ml-quant.com/papers/ssrn/4639959/): 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.
