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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 macroec...
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| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2025
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| _version_ | 1867613157674975232 |
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| access_status_str | Open Access |
| author | Orton, Alexa |
| author2 | Gebbie, Timothy |
| author_browse | Gebbie, Timothy Orton, Alexa |
| author_facet | Gebbie, Timothy Orton, Alexa |
| author_sort | Orton, Alexa |
| collection | Thesis |
| description | 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 |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/41861 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:31:41.113Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/41861 Representation learning for regime detection in financial markets Orton, Alexa Gebbie, Timothy deep manifold representation learning SPD matrix classification nested block hierarchical financial market correlations regime detection causal feedback loops 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 2025-09-18T10:53:18Z 2025-09-18T10:53:18Z 2025 2025-09-18T10:37:33Z Thesis / Dissertation Masters PhD http://hdl.handle.net/11427/41861 eng application/pdf Department of Statistical Sciences Faculty of Science Universiy of Cape Town |
| spellingShingle | deep manifold representation learning SPD matrix classification nested block hierarchical financial market correlations regime detection causal feedback loops Orton, Alexa Representation learning for regime detection in financial markets |
| thesis_degree_str | Doctoral |
| title | Representation learning for regime detection in financial markets |
| title_full | Representation learning for regime detection in financial markets |
| title_fullStr | Representation learning for regime detection in financial markets |
| title_full_unstemmed | Representation learning for regime detection in financial markets |
| title_short | Representation learning for regime detection in financial markets |
| title_sort | representation learning for regime detection in financial markets |
| topic | deep manifold representation learning SPD matrix classification nested block hierarchical financial market correlations regime detection causal feedback loops |
| url | http://hdl.handle.net/11427/41861 |
| work_keys_str_mv | AT ortonalexa representationlearningforregimedetectioninfinancialmarkets |