Masters Degrees (School of Accountancy)

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Browsing Masters Degrees (School of Accountancy) by Author "Anna Elizabeth (Nannette), Botha"

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    Effective governance through implementation of appropriate algorithms in share trading
    (Stellenbosch : Stellenbosch University, 2018-12) Anna Elizabeth (Nannette), Botha; Sahd, Lize-Mari; Stellenbosch University. Faculty of Economic and Management Sciences. School of Accountancy.
    ENGLISH SUMMARY : Advancement in computer technology enabled an evolution in share trading. This brought such an increase in available data that manual analysis can no longer provide accurate, timeous results. Many share traders have found a solution in the implementation of algorithms. To effectively govern algorithms and ensure the control objectives of validity, accuracy and completeness are met, the life cycle of an algorithm must be considered: the input data, analysis and results must be governed. The choice of algorithm is fundamental to effectively govern its analysis and results, since an algorithm is not always appropriate for implementation. The algorithm must be appropriate for the available data, the requirements of the analysis, as well as the required algorithm result in order to meet the control objectives. To investigate the applicability of algorithms, this research provides an understanding of the evolution in the share trading industry, algorithms and the enabling technologies of big data and machine learning. The study considers both qualitative and quantitative algorithms: statistical characteristics of predictive algorithms are identified, which indicate if the algorithm is appropriate for implementation based on the nature of the data available, the required analysis as well as the results the algorithm can achieve. The research will also investigate how nonpredictive algorithms’ outcome determine if it will be useful and appropriate to the data scientist. Based on the investigation, an applicability model was designed to map the investigated statistical characteristics with the indicators found. This model will provide guidance to data scientists and other users to assess their data and algorithm needs to what the available algorithms can provide, therefore determining which algorithm characteristics will be most appropriate for implementation.