# Mamba Meets Financial Markets: A Graph-Mamba Approach for Stock Price Prediction

arXiv: https://arxiv.org/abs/2410.03707 · HTML: https://mlsanity.com/paper/2410.03707/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Time series forecasting with SSMs, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 26 Sep 2024.

## Placed in

- State-space sequence models (S4, Mamba) / [Time series forecasting with SSMs](https://mlsanity.com/ssm/time-series-forecasting-with-ssms/)

## Builds on (most cited first, 4 of 4)

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Is Mamba Effective for Time Series Forecasting?](https://mlsanity.com/paper/2403.11144/) (2024)
- [MambaStock: Selective state space model for stock prediction](https://mlsanity.com/paper/2402.18959/) (2024)
- [U-CNNpred: A Universal CNN-based Predictor for Stock Markets](https://mlsanity.com/paper/1911.12540/) (2019)

## Built on by (most cited first, 2 of 2)

- [Exploring Graph Mamba: A Comprehensive Survey on State-Space Models for Graph Learning](https://mlsanity.com/paper/2412.18322/) (2024)
- [Stock Prediction via a Dual Relation Fusion Network incorporating Static and Dynamic Relations](https://mlsanity.com/paper/2510.10695/) (2025)

## Abstract (arXiv)

Stock markets play an important role in the global economy, where accurate stock price predictions can lead to significant financial returns. While existing transformer-based models have outperformed long short-term memory networks and convolutional neural networks in financial time series prediction, their high computational complexity and memory requirements limit their practicality for real-time trading and long-sequence data processing. To address these challenges, we propose SAMBA, an innovative framework for stock return prediction that builds on the Mamba architecture and integrates graph neural networks. SAMBA achieves near-linear computational complexity by utilizing a bidirectional Mamba block to capture long-term dependencies in historical price data and employing adaptive graph convolution to model dependencies between daily stock features. Our experimental results demonstrate that SAMBA significantly outperforms state-of-the-art baseline models in prediction accuracy, maintaining low computational complexity. The code and datasets are available at github.com/Ali-Meh619/SAMBA.