Representation Learning for Regime detection in Block Hierarchical Financial Markets

• URL: http://arxiv.org/abs/2410.22346v1
• Date: Mon, 14 Oct 2024 20:23:00 GMT
• Title: Representation Learning for Regime detection in Block Hierarchical Financial Markets
• Authors: Alexa Orton, Tim Gebbie,
• Abstract summary: We consider financial market regime detection from the perspective of deep representation learning of the causal information geometry underpinning traded asset systems. We show that using a singular performance metric is misleading in our financial market investment use cases where deep learning models overfit in learning-temporal correlation dynamics.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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