Representation Learning for Regime detection in Block Hierarchical Financial Markets
This work addresses regime detection for financial market investment, but it appears incremental as it focuses on evaluating existing models rather than introducing new ones.
The paper tackled the problem of financial market regime detection by evaluating three deep representation learning models on different data configurations, showing that using a single performance metric is misleading due to overfitting in learning spatio-temporal correlation dynamics.
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.