{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/37818"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/37818","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"Market state discovery","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Singo, Unarine"],"institution":"Department of Statistical Sciences","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Gebbie, Timothy"],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022","date_published":"2022","updated_at":"2026-07-22T22:22:54Z","subjects":["statistical sciences"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11427/37818","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Gebbie, Timothy"]},{"key":"dc:creator","label":"Author","values":["Singo, Unarine"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-04-21T12:10:02Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-04-21T12:10:02Z"]},{"key":"dc:date.issued","label":"Date","values":["2022"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Department of Statistical Sciences"]},{"key":"dc:type","label":"Dc Type","values":["Master Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Masters","MSc"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["statistical sciences"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11427/37818"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["Market state discovery"]}]}],"canonical_facts":{"dc:contributor.advisor":["Gebbie, Timothy"],"dc:creator":["Singo, Unarine"],"dc:date.accessioned":["2023-04-21T12:10:02Z"],"dc:date.available":["2023-04-21T12:10:02Z"],"dc:date.issued":["2022"],"dc:description.abstract":["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."],"dc:identifier.uri":["http://hdl.handle.net/11427/37818"],"dc:publisher.department":["Department of Statistical Sciences"],"dc:subject":["statistical sciences"],"dc:title":["Market state discovery"],"dc:type":["Master Thesis"],"dc:type.qualificationlevel":["Masters","MSc"]},"updated_at":"2026-07-22T22:22:54Z"}