# MambaStock: Selective state space model for stock prediction

arXiv: https://arxiv.org/abs/2402.18959 · HTML: https://mlsanity.com/paper/2402.18959/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Feb 2024.

## Built on by (most cited first, 13 of 13)

- [State Space Model for New-Generation Network Alternative to Transformers: A Survey](https://mlsanity.com/paper/2404.09516/) (2024)
- [CMamba: Channel Correlation Enhanced State Space Models for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2406.05316/) (2024)
- [How Much Can Time-related Features Enhance Time Series Forecasting?](https://mlsanity.com/paper/2412.01557/) (2024)
- [Mamba Meets Financial Markets: A Graph-Mamba Approach for Stock Price Prediction](https://mlsanity.com/paper/2410.03707/) (2024)
- [Mamba time series forecasting with uncertainty quantification](https://mlsanity.com/paper/2503.10873/) (2025)
- [Technologies on Effectiveness and Efficiency: A Survey of State Spaces Models](https://mlsanity.com/paper/2503.11224/) (2025)
- [Venturing into Uncharted Waters: The Navigation Compass from Transformer to Mamba](https://mlsanity.com/paper/2406.16722/) (2024)
- [SambaMixer: State of Health Prediction of Li-ion Batteries using Mamba State Space Models](https://mlsanity.com/paper/2411.00233/) (2024)
- [Alternators With Noise Models](https://mlsanity.com/paper/2505.12544/) (2025)
- [From Rattle to Roar: Optimizer Showdown for MambaStock on S&P 500](https://mlsanity.com/paper/2508.04707/) (2025)
- [Kinematic Tokenization: Optimization-Based Continuous-Time Tokens for Learnable Decision Policies in Noisy Time Series](https://mlsanity.com/paper/2601.09949/) (2026)
- [Permutation-Equivariant 2D State Space Models: Theory and Canonical Architecture for Multivariate Time Series](https://mlsanity.com/paper/2603.08753/) (2026)
- [Constitutive State-Space Modeling of Path-Dependent Plasticity: A Resolution-Consistent and Parallelizable Computational Framework](https://mlsanity.com/paper/2609.07294/) (2026)

## Abstract (arXiv)

The stock market plays a pivotal role in economic development, yet its intricate volatility poses challenges for investors. Consequently, research and accurate predictions of stock price movements are crucial for mitigating risks. Traditional time series models fall short in capturing nonlinearity, leading to unsatisfactory stock predictions. This limitation has spurred the widespread adoption of neural networks for stock prediction, owing to their robust nonlinear generalization capabilities. Recently, Mamba, a structured state space sequence model with a selection mechanism and scan module (S6), has emerged as a powerful tool in sequence modeling tasks. Leveraging this framework, this paper proposes a novel Mamba-based model for stock price prediction, named MambaStock. The proposed MambaStock model effectively mines historical stock market data to predict future stock prices without handcrafted features or extensive preprocessing procedures. Empirical studies on several stocks indicate that the MambaStock model outperforms previous methods, delivering highly accurate predictions. This enhanced accuracy can assist investors and institutions in making informed decisions, aiming to maximize returns while minimizing risks. This work underscores the value of Mamba in time-series forecasting. Source code is available at https://github.com/zshicode/MambaStock.