---
title: Market Liquidity Estimation with Machine Learning
url: https://www.ml-quant.com/papers/ssrn/4666684/
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 4666684
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4666684
featured: 2023-12-20
citations: unknown
topic: Trading, Microstructure & Execution
---


# Market Liquidity Estimation with Machine Learning

Machine learning is used to estimate the average daily bid-ask spread in the US and Chinese stock markets, enhancing performance by capturing more raw data and utilizing learned nonlinear relationships.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4666684
- Identifier: SSRN 4666684
- Released: 2023-03-03
- First featured: Quant Letter No. 30 (2023-12-20): https://www.ml-quant.com/issues/2023-12-20/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Trading, Microstructure & Execution

## Related

- [FAST: Efficient Action Tokenization for Vision-Language-Action Models](https://www.ml-quant.com/papers/arxiv/2501.09747/): A new tokenization scheme, Frequency-space Action Sequence Tokenization (FAST), has been proposed for robot actions, facilitating the training of vision-language action policies for complex and high-frequency tasks.
- [Deep Reinforcement Learning for Active High Frequency Trading](https://www.ml-quant.com/papers/arxiv/2101.07107/): A new Deep Reinforcement Learning framework has been developed for high frequency stock trading, showing potential for profitable long-term strategies.
- [Deep reinforcement learning for arbitrage in decentralized exchanges](https://www.ml-quant.com/papers/ssrn/4666504/): The study explores trading performances under arbitrage conditions in decentralized exchanges, using a simulation model and deep reinforcement learning to determine optimal arbitrage strategies for eight cryptocurrency pairs.
- [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.
- [Deep Reinforcement Learning: Policy Gradients for US Equities Trading](https://www.ml-quant.com/papers/ssrn/4645453/): The study shows that Deep Reinforcement Learning can effectively interpret synthetic alpha signals in financial trading, outperforming the market benchmark.
- [The Paradox Of Just-in-Time Liquidity in Decentralized Exchanges: More Providers Can Sometimes Mean Less Liquidity](https://www.ml-quant.com/papers/arxiv/2311.18164/): The research analyzes the paradox of just-in-time (JIT) liquidity provision in decentralized exchanges, which can reduce liquidity, and suggests a two-tiered fee structure to counteract this.
