Masters Degrees (School of Accountancy)

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Browsing Masters Degrees (School of Accountancy) by browse.metadata.advisor "Lamprecht, C."

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    Investigating the association between the reconciliation quality of EBITDA disclosure by JSE-listed companies and factors associated with opportunistic disclosure
    (Stellenbosch : Stellenbosch University, 2019-04) Mey, Mattheus Theodorus; Lamprecht, C.; Stellenbosch University. Faculty of Economic and Management Sciences. School of Accountancy.
    ENGLISH SUMMARY : This study sought to determine whether the Johannesburg Stock Exchange (JSE) and the International Accounting Standards Board (IASB) should specify explicit disclosure requirements regarding the format of reconciliations between adjusted International Financial Reporting Standards (IFRS) earnings, referred to as non-GAAP earnings, and IFRS earnings. The disclosure of non-GAAP earnings is linked to both decision-usefulness and earnings management. As a form of earnings management, company management may disclose non-GAAP earnings in such a manner as to influence users’ perceptions of company performance in order to attain their own opportunistic goals. If reconciliations between non-GAAP earnings and IFRS earnings are of a high quality, the risk of opportunistic disclosure is limited and decision-useful information enabled. Focusing on earnings before interest, tax, depreciation and amortisation (EBITDA), the following research question was addressed: Are companies less likely to disclose higher quality reconciliations between EBITDA and IFRS earnings when factors linked to opportunistic disclosure are present? The quality of reconciliations between EBITDA and IFRS earnings, as included in the Stock Exchange News Service (SENS) reports of JSE-listed companies for the financial years 2014 to 2016, were determined. Ordinary least squares estimation was used to regress the EBITDA reconciliation score on three factors linked to opportunistic disclosure, namely: whether greater emphasis is placed on EBITDA than IFRS earnings; whether EBITDA is positive when IFRS earnings are negative; and whether invalid adjustments were made in deriving EBITDA. The results showed that higher reconciliation quality is negatively associated with instances where companies reported a positive EBITDA when IFRS earnings were negative. This potentially opportunistic use of poorly reconciled information provides support for the establishment of explicit disclosure requirements to enhance decision-useful disclosure. However, the association between reconciliation quality and the remaining two opportunistic factors, that is, when EBITDA is emphasised and when invalid adjustments are made in deriving EBITDA, was positive and indicates that management had disclosed decision-useful information through higher quality reconciliations when those two factors were present. In addition, the study found great diversity in how companies define EBITDA and also that the quality of EBITDA reconciliations in many SENS reports was lacking. This study contributes to the limited body of research on non-GAAP disclosure by South African companies. It also contributes to the voluntary disclosure literature by focusing on a non-GAAP earnings measure that has been largely ignored by prior studies, namely EBITDA. The findings may be of interest to the JSE in maintaining high quality corporate disclosure and may also have policy implications for the IASB which is involved in a disclosure initiative to improve presentation and disclosure in financial reports.
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    User considerations when applying machine learning technology to accounting tasks
    (Stellenbosch : Stellenbosch University, 2018-12) Smith, Liezl; Boshoff, W. H.; Lamprecht, C.; Stellenbosch University. Faculty of Economic and Management Sciences. School of Accountancy.
    ENGLISH SUMMARY : Machine learning is a strategic technology that can have an important effect on business, as it is able to perform tasks efficiently that were previously only performed by humans. When implementing this technology in the relevant business processes and utilising it effectively, users have to understand both it as well as other aspects have to be considered. It was found that one area that is well suited to the adoption of machine learning, is accounting. In addition, prior research has shown a need for accounting users to be educated in machine learning as part of their professional training. Therefore, the aim of this study was to enhance users’ understanding of machine learning technology specifically in the performance of accounting processes. A grounded theory methodology was employed to identifying the accounting tasks machine learning could perform, to describe how machine learning functions and to identify the risks, benefits and limitations associated with the technology. Finally, steps and considerations when implementing machine learning technology in the accounting process were provided. The findings of this research are that the user has a key role to play when using machine learning technology in the accounting processes and thus has to understand the technology, the risks and limitations, as well as the benefits of the technology. The risks discussed relate not only to machine learning technology but also to all the components that enable the functioning of the technology to ensure alignment with the accounting process goals. Based on these findings, this research presents the user considerations and steps to take when implementing machine learning in selected accounting processes. These can be used to identify areas that may require attention when a business is adopting machine learning. One important consideration is the implementation of adequate data governance. This is because most of the risks identified for machine learning technology are data risks. Further research could therefore be directed at developing a data governance framework for machine learning technologies.