A semantic Bayesian network for automated share evaluation on the JSE

dc.contributor.advisorMoodley, Deshendran
dc.contributor.authorDrake, Rachel
dc.date.accessioned2021-07-26T13:30:53Z
dc.date.available2021-07-26T13:30:53Z
dc.date.issued2021
dc.date.updated2021-07-26T13:26:10Z
dc.description.abstractAdvances in information technology have presented the potential to automate investment decision making processes. This will alleviate the need for manual analysis and reduce the subjective nature of investment decision making. However, there are different investment approaches and perspectives for investing which makes acquiring and representing expert knowledge for share evaluation challenging. Current decision models often do not reflect the real investment decision making process used by the broader investment community or may not be well-grounded in established investment theory. This research investigates the efficacy of using ontologies and Bayesian networks for automating share evaluation on the JSE. The knowledge acquired from an analysis of the investment domain and the decision-making process for a value investing approach was represented in an ontology. A Bayesian network was constructed based on the concepts outlined in the ontology for automatic share evaluation. The Bayesian network allows decision makers to predict future share performance and provides an investment recommendation for a specific share. The decision model was designed, refined and evaluated through an analysis of the literature on value investing theory and consultation with expert investment professionals. The performance of the decision model was validated through back testing and measured using return and risk-adjusted return measures. The model was found to provide superior returns and risk-adjusted returns for the evaluation period from 2012 to 2018 when compared to selected benchmark indices of the JSE. The result is a concrete share evaluation model grounded in investing theory and validated by investment experts that may be employed, with small modifications, in the field of value investing to identify shares with a higher probability of positive risk-adjusted returns.
dc.identifier.apacitationDrake, R. (2021). <i>A semantic Bayesian network for automated share evaluation on the JSE</i>. (). ,Faculty of Science ,Department of Computer Science. Retrieved from http://hdl.handle.net/11427/33646en_ZA
dc.identifier.chicagocitationDrake, Rachel. <i>"A semantic Bayesian network for automated share evaluation on the JSE."</i> ., ,Faculty of Science ,Department of Computer Science, 2021. http://hdl.handle.net/11427/33646en_ZA
dc.identifier.citationDrake, R. 2021. A semantic Bayesian network for automated share evaluation on the JSE. . ,Faculty of Science ,Department of Computer Science. http://hdl.handle.net/11427/33646en_ZA
dc.identifier.ris TY - Master Thesis AU - Drake, Rachel AB - Advances in information technology have presented the potential to automate investment decision making processes. This will alleviate the need for manual analysis and reduce the subjective nature of investment decision making. However, there are different investment approaches and perspectives for investing which makes acquiring and representing expert knowledge for share evaluation challenging. Current decision models often do not reflect the real investment decision making process used by the broader investment community or may not be well-grounded in established investment theory. This research investigates the efficacy of using ontologies and Bayesian networks for automating share evaluation on the JSE. The knowledge acquired from an analysis of the investment domain and the decision-making process for a value investing approach was represented in an ontology. A Bayesian network was constructed based on the concepts outlined in the ontology for automatic share evaluation. The Bayesian network allows decision makers to predict future share performance and provides an investment recommendation for a specific share. The decision model was designed, refined and evaluated through an analysis of the literature on value investing theory and consultation with expert investment professionals. The performance of the decision model was validated through back testing and measured using return and risk-adjusted return measures. The model was found to provide superior returns and risk-adjusted returns for the evaluation period from 2012 to 2018 when compared to selected benchmark indices of the JSE. The result is a concrete share evaluation model grounded in investing theory and validated by investment experts that may be employed, with small modifications, in the field of value investing to identify shares with a higher probability of positive risk-adjusted returns. DA - 2021 DB - OpenUCT DP - University of Cape Town KW - Ontology KW - Bayesian Network KW - Portfolio Management KW - Share Evaluation KW - Value Investing KW - Fundamental Analysis LK - https://open.uct.ac.za PY - 2021 T1 - A semantic Bayesian network for automated share evaluation on the JSE TI - A semantic Bayesian network for automated share evaluation on the JSE UR - http://hdl.handle.net/11427/33646 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/33646
dc.identifier.vancouvercitationDrake R. A semantic Bayesian network for automated share evaluation on the JSE. []. ,Faculty of Science ,Department of Computer Science, 2021 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/33646en_ZA
dc.language.rfc3066eng
dc.publisher.departmentDepartment of Computer Science
dc.publisher.facultyFaculty of Science
dc.subjectOntology
dc.subjectBayesian Network
dc.subjectPortfolio Management
dc.subjectShare Evaluation
dc.subjectValue Investing
dc.subjectFundamental Analysis
dc.titleA semantic Bayesian network for automated share evaluation on the JSE
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationlevelMSc
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