Representation learning for regime detection in financial markets

We investigate financial market regime detection from the perspective of deep representation learning of the causal (reflexive) information geometry underpinning complex (multi-scale) dynamical traded asset systems using an emergent hierarchical correlation structure to characterise evolving macroeconomic market phases. Specifically, we assess the robustness of three toy models: SPD Matrix Network (SPDNet), SPD Matrix Network with Riemannian Batch Normalisation (SPDNetBN) and U-shaped SPD Matrix Network (U-SPDNet) whose architectures respect the underlying Riemannian manifold of input block hierarchical Symmetric Positive Definite (SPD) correlation matrices by employing Log-Euclidean Metric (LEM)s. Market phase detection for each model is carried out using three data configurations: i.) Randomised Johannesburg Stock Exchange (JSE) Top 60 data, ii.) synthetically-generated block hierarchical SPD matrices, and iii.) chronology-preserving block-resampled JSE Top 60 data. We show that using a singular performance metric is misleading in our financial market use cases. We confirm that U-SPDNet performs improved latent feature extraction with better classification performance in stressed and rally market phases, despite achieving lower Out-of-Sample (OOS) backtest scenario accuracy than that of the benchmark SPDNet. The SPDNet-based models fail in capturing latent reflexive spatio-temporal block hierarchical correlation dynamics and deliver corner solutions across all input data sets. The U-SPDNet is promising in terms of its utility in regime dependent portfolio optimisation strategy generation as a model better-suited to capturing latent block hierarchical correlation structures arising from lead-lag causal feedback information loops that often drive the evolution of evolving market regimes

Grantor dc:publisher.institution

Department of Statistical Sciences

Year dc:date.issued

2025

Author dc:creator

• Orton, Alexa

Advisor dc:contributor.advisor

• Gebbie, Timothy

• deep manifold representation learning
• SPD matrix classification
• nested block hierarchical financial market correlations
• regime detection
• causal feedback loops

Handle dc:identifier.uri

http://hdl.handle.net/11427/41861

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/41861