MambaStock: Selective state space model for stock prediction

ArXiv ID: 2402.18959 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: Mamba Model, Stock Price Prediction, Structured State Space Models, Time-Series Forecasting, Nonlinear Generalization, Equities

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper introduces advanced state-space models (S4/Mamba) with derived equations and discrete-time transitions, requiring moderate mathematical sophistication. However, empirical rigor is limited by vague performance metrics, small dataset specifics, and lack of detailed backtesting methodology or risk metrics, despite providing code and data sources.

  flowchart TD
    A["Research Goal:<br>Stock Prediction"] --> B["Input Data:<br>Historical Stock Prices"]
    B --> C["Method: MambaStock<br>Structured State Space Model S6"]
    C --> D{"Computation:<br>Sequence Modeling & Forecasting"}
    D --> E["Output:<br>Future Stock Prices"]
    E --> F["Outcome:<br>SOTA Accuracy & Low Risk"]