Investigation of factor rotation routines in principal component analysis of stock returns

 

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dc.contributor.advisor Bosman, Petrus en_ZA
dc.contributor.author Weimar, Nicole en_ZA
dc.date.accessioned 2014-10-17T10:09:59Z
dc.date.available 2014-10-17T10:09:59Z
dc.date.issued 2014 en_ZA
dc.identifier.citation Weimar, N. 2014. Investigation of factor rotation routines in principal component analysis of stock returns. University of Cape Town. en_ZA
dc.identifier.uri http://hdl.handle.net/11427/8533
dc.description Includes bibliographical references. en_ZA
dc.description.abstract This paper investigates rotation routines that will produce uncorrelated rotated principal components for a dataset of stock returns, in an attempt to identify the macroeconomic factors that best explain the variability among risk-adjusted stock returns on the Johannesburg Stock Exchange. An alternative to the more traditional rotation approaches is used, which creates subsets of principal components with similar variances that are rotated in turn. It is found that only one of the three normalisation constraints examined can retain uncorrelated principal components after rotation. The results also show that when subspaces of components are rotated that have close eigenvalues, the different rotation criteria used to rotate principal components will produce similar results. After rotating the suitable subsets using varimax rotation, it is found that the first rotated component can be explained by the African Industrials sector, the second rotated component is related to the African Consumer Services sector while the third rotated component shows a significant relationship to the African Finance factor. en_ZA
dc.language.iso eng en_ZA
dc.subject.other Mathematical Finance en_ZA
dc.title Investigation of factor rotation routines in principal component analysis of stock returns 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 Weimar, N. (2014). <i>Investigation of factor rotation routines in principal component analysis of stock returns</i>. (Thesis). University of Cape Town ,Faculty of Commerce ,Division of Actuarial Science. Retrieved from http://hdl.handle.net/11427/8533 en_ZA
dc.identifier.chicagocitation Weimar, Nicole. <i>"Investigation of factor rotation routines in principal component analysis of stock returns."</i> Thesis., University of Cape Town ,Faculty of Commerce ,Division of Actuarial Science, 2014. http://hdl.handle.net/11427/8533 en_ZA
dc.identifier.vancouvercitation Weimar N. Investigation of factor rotation routines in principal component analysis of stock returns. [Thesis]. University of Cape Town ,Faculty of Commerce ,Division of Actuarial Science, 2014 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/8533 en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Weimar, Nicole AB - This paper investigates rotation routines that will produce uncorrelated rotated principal components for a dataset of stock returns, in an attempt to identify the macroeconomic factors that best explain the variability among risk-adjusted stock returns on the Johannesburg Stock Exchange. An alternative to the more traditional rotation approaches is used, which creates subsets of principal components with similar variances that are rotated in turn. It is found that only one of the three normalisation constraints examined can retain uncorrelated principal components after rotation. The results also show that when subspaces of components are rotated that have close eigenvalues, the different rotation criteria used to rotate principal components will produce similar results. After rotating the suitable subsets using varimax rotation, it is found that the first rotated component can be explained by the African Industrials sector, the second rotated component is related to the African Consumer Services sector while the third rotated component shows a significant relationship to the African Finance factor. DA - 2014 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2014 T1 - Investigation of factor rotation routines in principal component analysis of stock returns TI - Investigation of factor rotation routines in principal component analysis of stock returns UR - http://hdl.handle.net/11427/8533 ER - en_ZA


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