# FinMamba: Market-Aware Graph Enhanced Multi-Level Mamba for Stock Movement Prediction

arXiv: https://arxiv.org/abs/2502.06707 · HTML: https://mlsanity.com/paper/2502.06707/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2025.

## Built on by (most cited first, 11 of 11)

- [FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting](https://mlsanity.com/paper/2502.18834/) (2025)
- [Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting](https://mlsanity.com/paper/2509.14181/) (2025)
- [Glocal Information Bottleneck for Time Series Imputation](https://mlsanity.com/paper/2510.04910/) (2025)
- [Revisiting Multivariate Time Series Forecasting with Missing Values](https://mlsanity.com/paper/2509.23494/) (2025)
- [From Observations to States: Latent Time Series Forecasting](https://mlsanity.com/paper/2602.00297/) (2026)
- [Forecasting as Rendering: A 2D Gaussian Splatting Framework for Time Series Forecasting](https://mlsanity.com/paper/2603.02220/) (2026)
- [Forecasting with Guidance: Representation-Level Supervision for Time Series Forecasting](https://mlsanity.com/paper/2603.24262/) (2026)
- [Existence Precedes Value: Joint Modeling of Observational Existence and Evolving States in Time Series Forecasting](https://mlsanity.com/paper/2606.13571/) (2026)
- [Multivariate Time Series Forecasting needs Cross Variable Loss](https://mlsanity.com/paper/2608.05742/) (2026)
- [ACT: Anti-Crosstalk Learning for Cross-Sectional Stock Ranking via Temporal Disentanglement and Structural Purification](https://mlsanity.com/paper/2604.20204/) (2026)
- [Nystr"om Attention Matches Full Attention for Cross-Sectional Stock Prediction](https://mlsanity.com/paper/2609.08106/) (2026)

## Abstract (arXiv)

Recently, combining stock features with inter-stock correlations has become a common and effective approach for stock movement prediction. However, financial data presents significant challenges due to its low signal-to-noise ratio and the dynamic complexity of the market, which give rise to two key limitations in existing methods. First, the relationships between stocks are highly influenced by multifaceted factors including macroeconomic market dynamics, and current models fail to adaptively capture these evolving interactions under specific market conditions. Second, for the accuracy and timeliness required by real-world trading, existing financial data mining methods struggle to extract beneficial pattern-oriented dependencies from long historical data while maintaining high efficiency and low memory consumption. To address the limitations, we propose FinMamba, a Mamba-GNN-based framework for market-aware and multi-level hybrid stock movement prediction. Specifically, we devise a dynamic graph to learn the changing representations of inter-stock relationships by integrating a pruning module that adapts to market trends. Afterward, with a selective mechanism, the multi-level Mamba discards irrelevant information and resets states to skillfully recall historical patterns across multiple time scales with linear time costs, which are then jointly optimized for reliable prediction. Extensive experiments on U.S. and Chinese stock markets demonstrate the effectiveness of our proposed FinMamba, achieving state-of-the-art prediction accuracy and trading profitability, while maintaining low computational complexity. The code is available at https://github.com/TROUBADOUR000/FinMamba.