Representation Learning for Regime detection in Block Hierarchical Financial Markets

TL;DR

This paper explores deep representation learning methods for detecting market regimes in hierarchical financial data, emphasizing the importance of robust evaluation beyond single performance metrics.

Contribution

It introduces and assesses three Riemannian manifold-respecting models for market regime detection using diverse data configurations.

Findings

Models respect the Riemannian structure of correlation matrices

Single performance metrics can be misleading in financial regime detection

Deep models tend to overfit in spatio-temporal correlation learning

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.