Market state discovery
| dc.contributor.advisor | Gebbie, Timothy | |
| dc.contributor.author | Singo, Unarine | |
| dc.date.accessioned | 2023-04-21T12:10:02Z | |
| dc.date.available | 2023-04-21T12:10:02Z | |
| dc.date.issued | 2022 | |
| dc.date.updated | 2023-04-21T12:09:44Z | |
| 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.apacitation | Singo, U. (2022). <i>Market state discovery</i>. (). ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/37818 | en_ZA |
| dc.identifier.chicagocitation | Singo, Unarine. <i>"Market state discovery."</i> ., ,Faculty of Science ,Department of Statistical Sciences, 2022. http://hdl.handle.net/11427/37818 | en_ZA |
| dc.identifier.citation | Singo, U. 2022. Market state discovery. . ,Faculty of Science ,Department of Statistical Sciences. http://hdl.handle.net/11427/37818 | en_ZA |
| dc.identifier.ris | TY - Master Thesis AU - Singo, Unarine AB - 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. DA - 2022 DB - OpenUCT DP - University of Cape Town KW - statistical sciences LK - https://open.uct.ac.za PY - 2022 T1 - Market state discovery TI - Market state discovery UR - http://hdl.handle.net/11427/37818 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/37818 | |
| dc.identifier.vancouvercitation | Singo U. Market state discovery. []. ,Faculty of Science ,Department of Statistical Sciences, 2022 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/37818 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Statistical Sciences | |
| dc.publisher.faculty | Faculty of Science | |
| dc.subject | statistical sciences | |
| dc.title | Market state discovery | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc |