Analysis of the cross-section of equity returns on the JSE Securities Exchange based on linear and nonlinear modeling techniques

 

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dc.contributor.advisor Van Rensburg, Paul en_ZA
dc.contributor.author Hodnett, Kathleen E en_ZA
dc.date.accessioned 2014-12-31T20:03:08Z
dc.date.available 2014-12-31T20:03:08Z
dc.date.issued 2010 en_ZA
dc.identifier.citation Hodnett, K. 2010. Analysis of the cross-section of equity returns on the JSE Securities Exchange based on linear and nonlinear modeling techniques. University of Cape Town. en_ZA
dc.identifier.uri http://hdl.handle.net/11427/10795
dc.description Includes bibliographical references. en_ZA
dc.description.abstract This research investigates the relationship between firm-specific style attributes and the cross-section of equity returns on the JSE Securities Exchange (JSE) over the period from 1 January 1997 to 31 December 2007. Both linear and nonlinear expected returns forecasting models are constructed based on the cross-section of equity returns. A blended approach combining a linear modeling technique with a nonlinear artificial neural network technique is developed to identify future potential top performing shares on the JSE. 1. Both linear and nonlinear models identify book-value-to-price and cash flow-to-price as significant styles attributes that distinguish near-term future share returns on the JSE. 2. This thesis found updating the identity of attributes is equally important as updating the factor payoffs of attributes in applying the stepwise regression approach. 3. Nonlinearity on the JSE equity returns is found to complement the forecasting power of linear factor models. 4. In terms of artificial neural network modeling, the extended Kalman filter learning rule introduced in the thesis is found to outperform the traditional back-propagation approach. 5. This thesis found that updating the identity of attributes via a genetic algorithm in the nonlinear forecasting models is superior to the static nonlinear forecasting models. 6. Both linear and nonlinear models are found to be more adequate in identifying future outperformers than identifying future underperformers on the JSE. The results of the research provide for potential alpha generating stock selection techniques for active portfolio managers in the South African equity market using the blended linear-nonlinear approach. en_ZA
dc.language.iso eng en_ZA
dc.subject.other Finance en_ZA
dc.title Analysis of the cross-section of equity returns on the JSE Securities Exchange based on linear and nonlinear modeling techniques en_ZA
dc.type Doctoral 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 Department of Finance and Tax en_ZA
dc.type.qualificationlevel Doctoral
dc.type.qualificationname PhD en_ZA
uct.type.filetype Text
uct.type.filetype Image
dc.identifier.apacitation Hodnett, K. E. (2010). <i>Analysis of the cross-section of equity returns on the JSE Securities Exchange based on linear and nonlinear modeling techniques</i>. (Thesis). University of Cape Town ,Faculty of Commerce ,Department of Finance and Tax. Retrieved from http://hdl.handle.net/11427/10795 en_ZA
dc.identifier.chicagocitation Hodnett, Kathleen E. <i>"Analysis of the cross-section of equity returns on the JSE Securities Exchange based on linear and nonlinear modeling techniques."</i> Thesis., University of Cape Town ,Faculty of Commerce ,Department of Finance and Tax, 2010. http://hdl.handle.net/11427/10795 en_ZA
dc.identifier.vancouvercitation Hodnett KE. Analysis of the cross-section of equity returns on the JSE Securities Exchange based on linear and nonlinear modeling techniques. [Thesis]. University of Cape Town ,Faculty of Commerce ,Department of Finance and Tax, 2010 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/10795 en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Hodnett, Kathleen E AB - This research investigates the relationship between firm-specific style attributes and the cross-section of equity returns on the JSE Securities Exchange (JSE) over the period from 1 January 1997 to 31 December 2007. Both linear and nonlinear expected returns forecasting models are constructed based on the cross-section of equity returns. A blended approach combining a linear modeling technique with a nonlinear artificial neural network technique is developed to identify future potential top performing shares on the JSE. 1. Both linear and nonlinear models identify book-value-to-price and cash flow-to-price as significant styles attributes that distinguish near-term future share returns on the JSE. 2. This thesis found updating the identity of attributes is equally important as updating the factor payoffs of attributes in applying the stepwise regression approach. 3. Nonlinearity on the JSE equity returns is found to complement the forecasting power of linear factor models. 4. In terms of artificial neural network modeling, the extended Kalman filter learning rule introduced in the thesis is found to outperform the traditional back-propagation approach. 5. This thesis found that updating the identity of attributes via a genetic algorithm in the nonlinear forecasting models is superior to the static nonlinear forecasting models. 6. Both linear and nonlinear models are found to be more adequate in identifying future outperformers than identifying future underperformers on the JSE. The results of the research provide for potential alpha generating stock selection techniques for active portfolio managers in the South African equity market using the blended linear-nonlinear approach. DA - 2010 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2010 T1 - Analysis of the cross-section of equity returns on the JSE Securities Exchange based on linear and nonlinear modeling techniques TI - Analysis of the cross-section of equity returns on the JSE Securities Exchange based on linear and nonlinear modeling techniques UR - http://hdl.handle.net/11427/10795 ER - en_ZA


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