Paper: arXiv 2410.03707

Abstract

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.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper uses advanced mathematics including state space models, discretization, and bidirectional Mamba blocks, while also providing specific empirical results with comparisons to state-of-the-art baselines and publicly available code and datasets for reproducibility.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Accurate Stock Price Prediction<br>Low Computational Complexity"] --> B["Input Data:<br>Historical Stock Price Data"]
  B --> C["Methodology 1:<br>Bidirectional Mamba Block<br>for Long-Term Dependencies"]
  B --> D["Methodology 2:<br>Adaptive Graph Convolution<br>for Feature Dependencies"]
  C & D --> E["Computational Process:<br>Integrated Graph-Mamba Framework"]
  E --> F["Key Finding 1:<br>SOTA Prediction Accuracy"]
  E --> G["Key Finding 2:<br>Near-Linear Computational Complexity"]

Cited References (39)

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and 31 more
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  2. Attention is All you Need (2017)
  3. Financial time series forecasting with multi-modality graph neural network (2022)
  4. A Dynamic B ernstein Graph Recurrent Network for Wireless Cellular Traffic Prediction (2023)
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1 of 39 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.