---
title: "State-space models for limit order books (research idea)"
description: "Mamba and other selective state-space models are among the most frequent new architectures in the AI journals, built for long sequences. Limit order books produce exactly such sequences. Yet only 4 of 329 recent Mamba and state-space papers carry a finance keyword, all of them in AI journals. Can these models forecast short-term price moves better, and is the gain tradable after costs?"
url: https://scholar4ai.com/ideas/state-space-models-for-limit-order-books
markdown_url: https://scholar4ai.com/ideas/state-space-models-for-limit-order-books.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "finance", "market microstructure"]
keywords: ["Mamba", "limit order book", "high-frequency trading"]
---

# State-space models for limit order books

Mamba and other selective state-space models are among the most frequent new architectures in the AI journals, built for long sequences. Limit order books produce exactly such sequences. Yet only 4 of 329 recent Mamba and state-space papers carry a finance keyword, all of them in AI journals. Can these models forecast short-term price moves better, and is the gain tradable after costs?

## What prompted this

Since October 2025, "Mamba" appears on 293 papers, 290 of them in AI journals, and 329 papers carry a Mamba or state-space-model keyword; only 4 of them carry a finance, stock, trading, market or asset keyword. "Limit order book" appears on 13 papers and "high-frequency trading" on 21, mostly in finance journals.

## Questions

- On standard limit order book data, do state-space models predict mid-price moves better than convolutional and Transformer models at the same computing cost?
- Do the gains survive realistic trading costs and latency?
- Do they capture order-flow patterns that last longer than other models can see?

## Data that could help

Public limit order book benchmarks (for example the FI-2010 dataset or LOBSTER samples) and published baseline models.

## Next step

Train a Mamba model on a public limit order book benchmark, and compare its forecasts and simulated trading results with published baselines.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 23 – Oct 26* | Papers, last 36 months |
|---|---:|---:|---|---:|
| Mamba | 10 | #121 | 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 3, 3, 3, 7, 2, 4, 13, 12, 12, 19, 23, 10, 16, 14, 25, 33, 22, 15, 33, 33, 31, 25, 32, 25, 9 | 418 |
| limit order book | 1 | #2881 | 0, 0, 0, 0, 0, 0, 4, 0, 0, 2, 1, 0, 1, 1, 1, 1, 0, 0, 2, 0, 0, 1, 0, 1, 1, 2, 2, 2, 0, 2, 0, 1, 1, 1, 1, 1, 1 | 29 |
| high-frequency trading | 0 | – | 0, 0, 0, 0, 0, 0, 2, 4, 0, 2, 1, 5, 0, 1, 3, 3, 0, 3, 1, 1, 2, 1, 2, 2, 5, 1, 1, 2, 2, 3, 1, 3, 3, 0, 0, 1, 0 | 55 |

## Cite this page

Scholar4AI. (2026). State-space models for limit order books. Journal Research Rankings. Retrieved October 23, 2026, from https://scholar4ai.com/ideas/state-space-models-for-limit-order-books

```bibtex
@misc{scholar4ai-ideas-state-space-models-for-limit-order-books,
  author = {{Scholar4AI}},
  title = {State-space models for limit order books},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/ideas/state-space-models-for-limit-order-books}},
  urldate = {2026-10-23},
  note = {Journal Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).