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Introduction to fast Super-Paramagnetic Clustering
We map stock market interactions to spin models to recover their hierarchical structure using a simulated annealing based Super-Paramagnetic Clustering (SPC) algorithm. This is directly compared to a modified implementation of a maximum likelihood approach to fast-Super-Paramagnetic Clustering (f-SP...
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| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2020
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| _version_ | 1867613245460709376 |
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| access_status_str | Open Access |
| author | Yelibi, Lionel |
| author2 | Gebbie, Timothy |
| author_browse | Gebbie, Timothy Yelibi, Lionel |
| author_facet | Gebbie, Timothy Yelibi, Lionel |
| author_sort | Yelibi, Lionel |
| collection | Thesis |
| description | We map stock market interactions to spin models to recover their hierarchical structure using a simulated annealing based Super-Paramagnetic Clustering (SPC) algorithm. This is directly compared to a modified implementation of a maximum likelihood approach to fast-Super-Paramagnetic Clustering (f-SPC). The methods are first applied standard toy test-case problems, and then to a dataset of 447 stocks traded on the New York Stock Exchange (NYSE) over 1249 days. The signal to noise ratio of stock market correlation matrices is briefly considered. Our result recover approximately clusters representative of standard economic sectors and mixed clusters whose dynamics shine light on the adaptive nature of financial markets and raise concerns relating to the effectiveness of industry based static financial market classification in the world of real-time data-analytics. A key result is that we show that the standard maximum likelihood methods are confirmed to converge to solutions within a Super-Paramagnetic (SP) phase. We use insights arising from this to discuss the implications of using a Maximum Entropy Principle (MEP) as opposed to the Maximum Likelihood Principle (MLP) as an optimization device for this class of problems. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/31332 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:33:05.164Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| 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:11427/31332 Introduction to fast Super-Paramagnetic Clustering Yelibi, Lionel Gebbie, Timothy maximum likelihood Potts Models unsupervised learning clustering maximum entropy We map stock market interactions to spin models to recover their hierarchical structure using a simulated annealing based Super-Paramagnetic Clustering (SPC) algorithm. This is directly compared to a modified implementation of a maximum likelihood approach to fast-Super-Paramagnetic Clustering (f-SPC). The methods are first applied standard toy test-case problems, and then to a dataset of 447 stocks traded on the New York Stock Exchange (NYSE) over 1249 days. The signal to noise ratio of stock market correlation matrices is briefly considered. Our result recover approximately clusters representative of standard economic sectors and mixed clusters whose dynamics shine light on the adaptive nature of financial markets and raise concerns relating to the effectiveness of industry based static financial market classification in the world of real-time data-analytics. A key result is that we show that the standard maximum likelihood methods are confirmed to converge to solutions within a Super-Paramagnetic (SP) phase. We use insights arising from this to discuss the implications of using a Maximum Entropy Principle (MEP) as opposed to the Maximum Likelihood Principle (MLP) as an optimization device for this class of problems. 2020-02-25T12:08:37Z 2020-02-25T12:08:37Z 2019 2020-02-25T09:19:34Z Master Thesis Masters MSc http://hdl.handle.net/11427/31332 eng application/pdf Department of Statistical Sciences Faculty of Science |
| spellingShingle | maximum likelihood Potts Models unsupervised learning clustering maximum entropy Yelibi, Lionel Introduction to fast Super-Paramagnetic Clustering |
| thesis_degree_str | Master's |
| title | Introduction to fast Super-Paramagnetic Clustering |
| title_full | Introduction to fast Super-Paramagnetic Clustering |
| title_fullStr | Introduction to fast Super-Paramagnetic Clustering |
| title_full_unstemmed | Introduction to fast Super-Paramagnetic Clustering |
| title_short | Introduction to fast Super-Paramagnetic Clustering |
| title_sort | introduction to fast super paramagnetic clustering |
| topic | maximum likelihood Potts Models unsupervised learning clustering maximum entropy |
| url | http://hdl.handle.net/11427/31332 |
| work_keys_str_mv | AT yelibilionel introductiontofastsuperparamagneticclustering |