Financial Assets Dependency Prediction Utilizing Spatiotemporal Patterns

ArXiv ID: 2406.11886 “View on arXiv”

Authors: Unknown

Abstract

Financial assets exhibit complex dependency structures, which are crucial for investors to create diversified portfolios to mitigate risk in volatile financial markets. To explore the financial asset dependencies dynamics, we propose a novel approach that models the dependencies of assets as an Asset Dependency Matrix (ADM) and treats the ADM sequences as image sequences. This allows us to leverage deep learning-based video prediction methods to capture the spatiotemporal dependencies among assets. However, unlike images where neighboring pixels exhibit explicit spatiotemporal dependencies due to the natural continuity of object movements, assets in ADM do not have a natural order. This poses challenges to organizing the relational assets to reveal better the spatiotemporal dependencies among neighboring assets for ADM forecasting. To tackle the challenges, we propose the Asset Dependency Neural Network (ADNN), which employs the Convolutional Long Short-Term Memory (ConvLSTM) network, a highly successful method for video prediction. ADNN can employ static and dynamic transformation functions to optimize the representations of the ADM. Through extensive experiments, we demonstrate that our proposed framework consistently outperforms the baselines in the ADM prediction and downstream application tasks. This research contributes to understanding and predicting asset dependencies, offering valuable insights for financial market participants.

Keywords: Asset Dependency Matrix, ConvLSTM, Video Prediction, Portfolio Diversification, Deep Learning

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail
  • Why: The paper introduces advanced deep learning concepts like ConvLSTM and MoE transformations, requiring knowledge of neural network architectures and spatiotemporal modeling, warranting high math complexity. It includes extensive experiments with baselines, application to portfolio risk reduction, and discusses implementation details, indicating strong empirical rigor.
  flowchart TD
    A["Research Goal<br>Predict Financial Asset Dependencies"] --> B["Data<br>Time-series of asset returns"]
    B --> C["Methodology<br>Asset Dependency Matrix ADM"]
    C --> D["Core Innovation<br>Static/Dynamic Transformation<br>Optimizes Asset Ordering"]
    D --> E["Computational Model<br>ADNN with ConvLSTM<br>Video Prediction Approach"]
    E --> F["Outcomes<br>Superior ADM Prediction &<br>Downstream Portfolio Performance"]