Robust portfolio construction using sorting signatures

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dc.contributor.advisorBradfield, Daveen_ZA
dc.contributor.advisorBailey, Geraldineen_ZA
dc.contributor.authorKasenene, Lillianen_ZA
dc.date.accessioned2014-10-17T10:09:56Z
dc.date.available2014-10-17T10:09:56Z
dc.date.issued2014en_ZA
dc.descriptionIncludes bibliographical references.en_ZA
dc.description.abstractMean-variance analysis introduced by Harry Markowitz has been criticised in the past mainly due to the counter-intuitive and unstable nature of the resultant portfolios from the optimisation. These disappointing results have been linked to the presence of estimation error in the estimates of the expected returns and covariances which serve as input to the optimisation. Several attempts have been made to produce more reliable estimates, with a significant amount of effort and resources placed in estimation of expected returns, which is generally a more difficult task than estimation of covariances. Almgren and Chriss (2006) provide a methodology for portfolio selection in which the order of expected returns replaces the numerical values of the returns. This framework allows full use of the covariance matrix, in a method analogous to mean-variance optimisation. We adopt this framework in our analysis together with the robust optimisation technique introduced by Golts and Jones (2009) which improves the estimate of the covariance matrix by direct modification in the optimisation process. Golts and Jones (2009) argue that a reduction of the angle between the input return forecasts and the output portfolio positions results in more investment relevant portfolios, inline with the investment manager's insights. They relate this angle to the condition number of the covariance matrix and use robust optimisation to improve the conditioning of this matrix. Assuming perfect alpha foresight of an investment manager, we apply a combination of the techniques of Almgren and Chriss (2006) and Golts and Jones (2009) to South African equity data and show that the resultant robust portfolios, though conservative in their risk-adjusted return statistics, are more diversified and exhibit lower leverage than mean-variance portfolios. We further show that independent of the optimisation method, there is a marginal difference in the performance of portfolios created using ordering information and actual returns.en_ZA
dc.identifier.apacitationKasenene, L. (2014). <i>Robust portfolio construction using sorting signatures</i>. (Thesis). University of Cape Town ,Faculty of Commerce ,Division of Actuarial Science. Retrieved from http://hdl.handle.net/11427/8527en_ZA
dc.identifier.chicagocitationKasenene, Lillian. <i>"Robust portfolio construction using sorting signatures."</i> Thesis., University of Cape Town ,Faculty of Commerce ,Division of Actuarial Science, 2014. http://hdl.handle.net/11427/8527en_ZA
dc.identifier.citationKasenene, L. 2014. Robust portfolio construction using sorting signatures. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Kasenene, Lillian AB - Mean-variance analysis introduced by Harry Markowitz has been criticised in the past mainly due to the counter-intuitive and unstable nature of the resultant portfolios from the optimisation. These disappointing results have been linked to the presence of estimation error in the estimates of the expected returns and covariances which serve as input to the optimisation. Several attempts have been made to produce more reliable estimates, with a significant amount of effort and resources placed in estimation of expected returns, which is generally a more difficult task than estimation of covariances. Almgren and Chriss (2006) provide a methodology for portfolio selection in which the order of expected returns replaces the numerical values of the returns. This framework allows full use of the covariance matrix, in a method analogous to mean-variance optimisation. We adopt this framework in our analysis together with the robust optimisation technique introduced by Golts and Jones (2009) which improves the estimate of the covariance matrix by direct modification in the optimisation process. Golts and Jones (2009) argue that a reduction of the angle between the input return forecasts and the output portfolio positions results in more investment relevant portfolios, inline with the investment manager's insights. They relate this angle to the condition number of the covariance matrix and use robust optimisation to improve the conditioning of this matrix. Assuming perfect alpha foresight of an investment manager, we apply a combination of the techniques of Almgren and Chriss (2006) and Golts and Jones (2009) to South African equity data and show that the resultant robust portfolios, though conservative in their risk-adjusted return statistics, are more diversified and exhibit lower leverage than mean-variance portfolios. We further show that independent of the optimisation method, there is a marginal difference in the performance of portfolios created using ordering information and actual returns. DA - 2014 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2014 T1 - Robust portfolio construction using sorting signatures TI - Robust portfolio construction using sorting signatures UR - http://hdl.handle.net/11427/8527 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/8527
dc.identifier.vancouvercitationKasenene L. Robust portfolio construction using sorting signatures. [Thesis]. University of Cape Town ,Faculty of Commerce ,Division of Actuarial Science, 2014 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/8527en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDivision of Actuarial Scienceen_ZA
dc.publisher.facultyFaculty of Commerceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.titleRobust portfolio construction using sorting signaturesen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMPhilen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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