Using the classification and regression tree (CART) model for stock selection on the S&P 700

Simple item page
dc.contributor.advisorVan Rensburg, Paulen_ZA
dc.contributor.authorPienaar, Neil Deonen_ZA
dc.date.accessioned2016-07-25T11:37:27Z
dc.date.available2016-07-25T11:37:27Z
dc.date.issued2016en_ZA
dc.description.abstractTraditionally, investment practitioners and academics alike have used stock fundamentals and a linear framework in order to predict future stock performance. This approach has been shown to have flaws as literature has shown that stock returns can exhibit non-linearity and involve complex relations beyond that of a linear nature (Hsieh, 1991; Sarantis, 2001; Shively, 2003). These findings present an opportunity to investment practitioners who are better able to model these returns. This dissertation attempts to classify stocks on the S&P 700 index using a Classification and Regression Tree (CART) built during an in-sample period and then used for predicative purposes during an out-of-sample period deliberately comprising both a period of financial crisis and recovery. For these periods, various portfolios and performance measures are calculated in order to assess the models performance relative to the benchmark, the Standard and Poor (S&P) 700 index.en_ZA
dc.identifier.apacitationPienaar, N. D. (2016). <i>Using the classification and regression tree (CART) model for stock selection on the S&P 700</i>. (Thesis). University of Cape Town ,Faculty of Commerce ,Department of Finance and Tax. Retrieved from http://hdl.handle.net/11427/20728en_ZA
dc.identifier.chicagocitationPienaar, Neil Deon. <i>"Using the classification and regression tree (CART) model for stock selection on the S&P 700."</i> Thesis., University of Cape Town ,Faculty of Commerce ,Department of Finance and Tax, 2016. http://hdl.handle.net/11427/20728en_ZA
dc.identifier.citationPienaar, N. 2016. Using the classification and regression tree (CART) model for stock selection on the S&P 700. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Pienaar, Neil Deon AB - Traditionally, investment practitioners and academics alike have used stock fundamentals and a linear framework in order to predict future stock performance. This approach has been shown to have flaws as literature has shown that stock returns can exhibit non-linearity and involve complex relations beyond that of a linear nature (Hsieh, 1991; Sarantis, 2001; Shively, 2003). These findings present an opportunity to investment practitioners who are better able to model these returns. This dissertation attempts to classify stocks on the S&P 700 index using a Classification and Regression Tree (CART) built during an in-sample period and then used for predicative purposes during an out-of-sample period deliberately comprising both a period of financial crisis and recovery. For these periods, various portfolios and performance measures are calculated in order to assess the models performance relative to the benchmark, the Standard and Poor (S&P) 700 index. DA - 2016 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2016 T1 - Using the classification and regression tree (CART) model for stock selection on the S&P 700 TI - Using the classification and regression tree (CART) model for stock selection on the S&P 700 UR - http://hdl.handle.net/11427/20728 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/20728
dc.identifier.vancouvercitationPienaar ND. Using the classification and regression tree (CART) model for stock selection on the S&P 700. [Thesis]. University of Cape Town ,Faculty of Commerce ,Department of Finance and Tax, 2016 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/20728en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Finance and Taxen_ZA
dc.publisher.facultyFaculty of Commerceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherInvestment Managementen_ZA
dc.titleUsing the classification and regression tree (CART) model for stock selection on the S&P 700en_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMComen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
thesis_com_2016_pienaar_neil_deon.pdf
Size:
1.67 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Masters