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Market state discovery
We explore the concept of financial market state discovery by assessing the robustness of two unsupervised machine learning algorithms: Inverse Covariance Clustering (ICC) and Agglomerative Super Paramagnetic Clustering (ASPC). The assessment is carried out by: simulating market datasets varying in...
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| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2023
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| _version_ | 1867611267383951360 |
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| access_status_str | Open Access |
| author | Singo, Unarine |
| author2 | Gebbie, Timothy |
| author_browse | Gebbie, Timothy Singo, Unarine |
| author_facet | Gebbie, Timothy Singo, Unarine |
| author_sort | Singo, Unarine |
| collection | Thesis |
| description | We explore the concept of financial market state discovery by assessing the robustness of two unsupervised machine learning algorithms: Inverse Covariance Clustering (ICC) and Agglomerative Super Paramagnetic Clustering (ASPC). The assessment is carried out by: simulating market datasets varying in complexity; implementing ICC and ASPC to estimate the underlying states (using only simulated log-returns as inputs); and measuring the algorithms' ability to recover the underlying states, using the Adjusted Rand Index (ARI) as a performance metric. Experiments revealed that ASPC is a more robust and better performing algorithm than ICC. ICC is able to produce competitive results in 2-state markets; however, ICC's primary disadvantage is its inability to maintain strong performance in 3, 4 and 5-state markets. For example, ASPC produced ARI numbers that were up to 800% superior to ICC in 5-state markets. Furthermore, ASPC does not rely on the art of selecting good hyper-parameters such as, the number of states a priori. ICC's utility as a market state discovery algorithm is limited. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/37818 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| 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:11427/37818 Market state discovery Singo, Unarine Gebbie, Timothy statistical sciences We explore the concept of financial market state discovery by assessing the robustness of two unsupervised machine learning algorithms: Inverse Covariance Clustering (ICC) and Agglomerative Super Paramagnetic Clustering (ASPC). The assessment is carried out by: simulating market datasets varying in complexity; implementing ICC and ASPC to estimate the underlying states (using only simulated log-returns as inputs); and measuring the algorithms' ability to recover the underlying states, using the Adjusted Rand Index (ARI) as a performance metric. Experiments revealed that ASPC is a more robust and better performing algorithm than ICC. ICC is able to produce competitive results in 2-state markets; however, ICC's primary disadvantage is its inability to maintain strong performance in 3, 4 and 5-state markets. For example, ASPC produced ARI numbers that were up to 800% superior to ICC in 5-state markets. Furthermore, ASPC does not rely on the art of selecting good hyper-parameters such as, the number of states a priori. ICC's utility as a market state discovery algorithm is limited. 2023-04-21T12:10:02Z 2023-04-21T12:10:02Z 2022 2023-04-21T12:09:44Z Master Thesis Masters MSc http://hdl.handle.net/11427/37818 eng application/pdf Department of Statistical Sciences Faculty of Science |
| spellingShingle | statistical sciences Singo, Unarine Market state discovery |
| thesis_degree_str | Master's |
| title | Market state discovery |
| title_full | Market state discovery |
| title_fullStr | Market state discovery |
| title_full_unstemmed | Market state discovery |
| title_short | Market state discovery |
| title_sort | market state discovery |
| topic | statistical sciences |
| url | http://hdl.handle.net/11427/37818 |
| work_keys_str_mv | AT singounarine marketstatediscovery |