---
title: ML and Kalman Filter for Pair Trading
url: https://www.ml-quant.com/papers/ssrn/4590815/
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 4590815
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4590815
featured: 2023-10-04
citations: unknown
topic: Trading, Microstructure & Execution
---


# ML and Kalman Filter for Pair Trading

The research uses machine learning and Kalman filtering to improve pair trading strategies, leading to better returns.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4590815
- Identifier: SSRN 4590815
- Released: 2023-10-03
- First featured: Quant Letter No. 18 (2023-10-04): https://www.ml-quant.com/issues/2023-10-04/
- 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.
- [LIGHT Benchmark - Comprehensive Backtesting Framework for Market Risk Models Comparison](https://www.ml-quant.com/papers/ssrn/4586897/): Market Risk Backtesting: The article presents LIGHT Benchmark, a tool for comparing market risk models, including a scoring system for evaluating Value at Risk and Expected Shortfall models.
- [Efficiency Metrics in Investments and Trading: A Comprehensive Examination](https://www.ml-quant.com/papers/ssrn/4584524/): A study emphasizes the need to measure risk-adjusted returns in investments and trading, highlighting metrics like the Sortino Ratio, Calmar Ratio, and Pareschi Ratio.
- [Gray-box Adversarial Attack of Deep Reinforcement Learning-based Trading Agents*](https://www.ml-quant.com/papers/arxiv/2309.14615/): A study has shown that a gray-box method can significantly reduce the profits of a Deep Reinforcement Learning-based trading agent, highlighting the need for stronger automated trading systems.
- [Don't Let MEV Slip: The Costs of Swapping on the Uniswap Protocol](https://www.ml-quant.com/papers/arxiv/2309.13648/): Efficiency and Slippage Analysis: The research analyzes the costs of trading on a decentralized exchange, showing that costs vary based on trade characteristics, and proposes that DEXs could be a trustworthy alternative to centralized exchanges for trading digital assets.