A comparative analysis of non-linear techniques in South African stock selection

 

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dc.contributor.advisor Bosman, Petrus en_ZA
dc.contributor.author Hutheram, Nikhil Arnaidas en_ZA
dc.date.accessioned 2015-12-09T14:44:05Z
dc.date.available 2015-12-09T14:44:05Z
dc.date.issued 2015 en_ZA
dc.identifier.citation Hutheram, N. 2015. A comparative analysis of non-linear techniques in South African stock selection. University of Cape Town. en_ZA
dc.identifier.uri http://hdl.handle.net/11427/15732
dc.description Includes bibliographical references en_ZA
dc.description.abstract Forecasting stock performance has long been one of the primary objectives of financial practitioners. Literature has shown that the classical linear approach to modelling the interactions among company-specific factors and its stock market re- turns in time have become less suited for capturing the movements of the stock market. Hence, attempts to predict the performance of a stock have become associated with additional layers of complexity. This has led to the adoption of non-linear approaches to forecast stock performance. This dissertation explores the performance of some non-linear models in the South African market. These were classification and regression trees (CART), logistic regression and a random forest approach com- pared against a linear regression model. Moreover, a hybrid model between CART and logistic regression was considered. The models fell into two categories (i.e., static and dynamic models). Using a set of classification and portfolio performance metrics it was found that that a dynamic modelling approach outperformed a static approach. Overall, the logistic and linear regression models dominated in terms of performance against the tree-based models and hybrid approaches. The results also demonstrated that a hybrid approach offered an improvement over a stand-alone CART. en_ZA
dc.language.iso eng en_ZA
dc.subject.other Mathematical Finance en_ZA
dc.title A comparative analysis of non-linear techniques in South African stock selection en_ZA
dc.type Master Thesis
uct.type.publication Research en_ZA
uct.type.resource Thesis en_ZA
dc.publisher.institution University of Cape Town
dc.publisher.faculty Faculty of Commerce en_ZA
dc.publisher.department Division of Actuarial Science en_ZA
dc.type.qualificationlevel Masters
dc.type.qualificationname MPhil en_ZA
uct.type.filetype Text
uct.type.filetype Image
dc.identifier.apacitation Hutheram, N. A. (2015). <i>A comparative analysis of non-linear techniques in South African stock selection</i>. (Thesis). University of Cape Town ,Faculty of Commerce ,Division of Actuarial Science. Retrieved from http://hdl.handle.net/11427/15732 en_ZA
dc.identifier.chicagocitation Hutheram, Nikhil Arnaidas. <i>"A comparative analysis of non-linear techniques in South African stock selection."</i> Thesis., University of Cape Town ,Faculty of Commerce ,Division of Actuarial Science, 2015. http://hdl.handle.net/11427/15732 en_ZA
dc.identifier.vancouvercitation Hutheram NA. A comparative analysis of non-linear techniques in South African stock selection. [Thesis]. University of Cape Town ,Faculty of Commerce ,Division of Actuarial Science, 2015 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/15732 en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Hutheram, Nikhil Arnaidas AB - Forecasting stock performance has long been one of the primary objectives of financial practitioners. Literature has shown that the classical linear approach to modelling the interactions among company-specific factors and its stock market re- turns in time have become less suited for capturing the movements of the stock market. Hence, attempts to predict the performance of a stock have become associated with additional layers of complexity. This has led to the adoption of non-linear approaches to forecast stock performance. This dissertation explores the performance of some non-linear models in the South African market. These were classification and regression trees (CART), logistic regression and a random forest approach com- pared against a linear regression model. Moreover, a hybrid model between CART and logistic regression was considered. The models fell into two categories (i.e., static and dynamic models). Using a set of classification and portfolio performance metrics it was found that that a dynamic modelling approach outperformed a static approach. Overall, the logistic and linear regression models dominated in terms of performance against the tree-based models and hybrid approaches. The results also demonstrated that a hybrid approach offered an improvement over a stand-alone CART. DA - 2015 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2015 T1 - A comparative analysis of non-linear techniques in South African stock selection TI - A comparative analysis of non-linear techniques in South African stock selection UR - http://hdl.handle.net/11427/15732 ER - en_ZA


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