Paper: arXiv 2410.22346

Abstract

We consider financial market regime detection from the perspective of deep representation learning of the causal information geometry underpinning traded asset systems using a hierarchical correlation structure to characterise market evolution. We assess the robustness of three toy models: SPDNet, SPD-NetBN and U-SPDNet whose architectures respect the underlying Riemannian manifold of input block hierarchical SPD correlation matrices. Market phase detection for each model is carried out using three data configurations: randomised JSE Top 60 data, synthetically-generated block hierarchical SPD matrices and block-resampled chronology-preserving JSE Top 60 data. We show that using a singular performance metric is misleading in our financial market investment use cases where deep learning models overfit in learning spatio-temporal correlation dynamics.

Complexity vs Empirical Score

  • Math Complexity: 9.2/10
  • Empirical Rigor: 4.5/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper employs advanced deep learning on Riemannian manifolds (SPD matrices) and uses synthetic data for controlled experiments, showing high mathematical complexity but limited backtest-ready implementation details, aligning with the Lab Rats quadrant.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Deep Representation Learning<br>for Financial Regime Detection"] --> B["Data Configurations"]
  B --> C["JSE Top 60<br>Randomized"]
  B --> D["Synthetic Block<br>Hierarchical SPD"]
  B --> E["JSE Top 60<br>Chronology-Preserving"]
  
  C & D & E --> F["Computational Process:<br>Three SPD-Net Variants<br>SPDNet / SPD-NetBN / U-SPDNet"]
  
  F --> G["Key Findings"]
  G --> H["Deep models overfit<br>spatio-temporal dynamics"]
  G --> I["Singular performance<br>metrics are misleading<br>in investment use cases"]