Introduction to fast Super-Paramagnetic Clustering
| dc.contributor.advisor | Gebbie, Timothy | |
| dc.contributor.author | Yelibi, Lionel | |
| dc.date.accessioned | 2020-02-25T12:08:37Z | |
| dc.date.available | 2020-02-25T12:08:37Z | |
| dc.date.issued | 2019 | |
| dc.date.updated | 2020-02-25T09:19:34Z | |
| dc.description.abstract | 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. | |
| dc.identifier.apacitation | Yelibi, L. (2019). <i>Introduction to fast Super-Paramagnetic Clustering</i>. (). ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/31332 | en_ZA |
| dc.identifier.chicagocitation | Yelibi, Lionel. <i>"Introduction to fast Super-Paramagnetic Clustering."</i> ., ,Faculty of Science ,Department of Statistical Sciences, 2019. http://hdl.handle.net/11427/31332 | en_ZA |
| dc.identifier.citation | Yelibi, L. 2019. Introduction to fast Super-Paramagnetic Clustering. | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Yelibi, Lionel AB - 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. DA - 2019 DB - OpenUCT DP - University of Cape Town KW - maximum likelihood KW - Potts Models KW - unsupervised learning KW - clustering KW - maximum entropy LK - https://open.uct.ac.za PY - 2019 T1 - Introduction to fast Super-Paramagnetic Clustering TI - Introduction to fast Super-Paramagnetic Clustering UR - http://hdl.handle.net/11427/31332 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/31332 | |
| dc.identifier.vancouvercitation | Yelibi L. Introduction to fast Super-Paramagnetic Clustering. []. ,Faculty of Science ,Department of Statistical Sciences, 2019 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/31332 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Statistical Sciences | |
| dc.publisher.faculty | Faculty of Science | |
| dc.subject | maximum likelihood | |
| dc.subject | Potts Models | |
| dc.subject | unsupervised learning | |
| dc.subject | clustering | |
| dc.subject | maximum entropy | |
| dc.title | Introduction to fast Super-Paramagnetic Clustering | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationname | MSc |