---
title: Machine Learning for Liquidity Estimation in US Treasury Bonds
url: https://www.ml-quant.com/papers/repec/eme-ejmbep-ejmbe-06-2022-0176/
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: RePEc:eme:ejmbep:ejmbe-06-2022-0176
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.emerald.com%2Finsight%2Fcontent%2Fdoi%2F10.1108%2FEJMBE-06-2022-0176%2Ffull%2Fhtml%3Futm_source%3Drepec%26utm_medium%3Dfeed%26utm_campaign%3Drepec%3Bh%3Drepec%3Aeme%3Aejmbep%3Aejmbe-06-2022-0176
featured: 2024-07-03
citations: unknown
topic: Trading, Microstructure & Execution
---


# Machine Learning for Liquidity Estimation in US Treasury Bonds

The research uses machine learning to determine the predictive power of liquidity variables in economic fluctuations, with private sector data proving more useful than public sector data.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.emerald.com%2Finsight%2Fcontent%2Fdoi%2F10.1108%2FEJMBE-06-2022-0176%2Ffull%2Fhtml%3Futm_source%3Drepec%26utm_medium%3Dfeed%26utm_campaign%3Drepec%3Bh%3Drepec%3Aeme%3Aejmbep%3Aejmbe-06-2022-0176
- Identifier: RePEc:eme:ejmbep:ejmbe-06-2022-0176
- Released: 2023-12-25
- First featured: Quant Letter No. 55 (2024-07-03): https://www.ml-quant.com/issues/2024-07-03/
- 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.
- [Unwinding Toxic Flow with Partial Information](https://www.ml-quant.com/papers/arxiv/2407.04510/): A model is proposed that maximizes daily trading profit and minimizes end-of-day inventory penalization, using a partially observable stochastic control problem to manage unobserved toxicity in client orders.
- [Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market](https://www.ml-quant.com/papers/arxiv/2406.10695/): The research combines quantitative and machine learning techniques to enhance a statistical arbitrage strategy, resulting in improved returns and lower transaction costs.
- [Local wealth condensation for yard-sale models with wealth-dependent biases](https://www.ml-quant.com/papers/arxiv/2406.10978/): The article provides a new proof for Chakraborti's economic model, which suggests wealth concentration and poverty increase in a trading system.
- [Trading Devil: Robust backdoor attack via Stochastic investment models and Bayesian approach](https://www.ml-quant.com/papers/arxiv/2406.10719/): The research explores a specific type of audio data attack, called a Stochastic investment-based backdoor attack, and shows its effectiveness in tricking speech recognition systems.