Browsing by Author "Le Roux, N. J."

Now showing 1 - 8 of 8
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    Equity- and entity-based multiples in emerging markets : evidence from the JSE Securities Exchange
    (Clute Institute, 2013-05) Nel, W. S.; Bruwer, B. W.; Le Roux, N. J.
    Analysts typically distinguish between equity- and entity-based approaches when employing the free cash flow model to perform equity valuations. However, when multiples are used to perform equity valuations, analysts often neglect to distinguish between equity- and entity-based approaches. In addition, limited empirical evidence exists on the relative valuation performance of equity- and entity-based multiples in developed capital markets and the emerging markets literature is entirely silent in this regard. In this paper the valuation accuracy of equity-based multiples is compared to that of entity-based multiples in valuing the equity of South African companies listed on the JSE Securities Exchange for the period 2001-2010. The research results reveal that equity-based multiples significantly outperform entity-based multiples, indicating a potential increase in valuation accuracy of as much as 15.37%.
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    Extending a scatterplot for displaying group structure in multivariate data : a case study
    (Operations Research Society of South Africa, 2005) Gardner, S.; Le Roux, N. J.; Rypstra, T.; Swart, J. P. J.
    ENGLISH SUMMARY : The power of canonical variate analysis (CVA) biplots, when regarded as extensions of or- dinary scatterplots to describe variation and group structure in multivariate observations, is demonstrated by presenting a case study from the South African wood pulp industry. It is shown how multidimensional standards specified by users of a product may be added to the biplot in the form of acceptance regions such that the roles of the respective variables that influence the product can be ascertained. The case study considers an alternative to CVA and multivariate analysis of variance (MANOVA) when the application of these procedures becomes questionable as a result of dealing with small sample sizes and heterogeneity of covariance matrices. It is explained how analysis of distance (AOD) analogous to analysis of variance may be performed in such cases. Biplots to accompany AOD are provided. The biplots and AOD illustrated in the case study from the wood pulp industry have the potential to be used widely where a primary product, influenced by several variables, is produced and where this product is of importance to various secondary manufacturers depending on which set of multidimensional specifications are met.
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    Monitoring gender remuneration inequalities in academia using biplots
    (Operations Research Society of South Africa, 2008) Walters, I. S.; Le Roux, N. J.
    ENGLISH SUMMARY : Gender remuneration inequalities at universities have been studied in various parts of the world. In South Africa, the responsibility largely rests with individual higher education institutions to establish levels of pay for male and female academic staff members. The multidimensional character of the gender wage gap includes gender di erentials in research output, age, academic rank and quali cations. The aim in this paper is to demonstrate the use of modern biplot methodology for describing and monitoring changes in the gender remuneration gap over time. A biplot is considered as a multivariate extension of an ordinary scatterplot. Our case study includes the permanent fulltime academic sta at Stellenbosch University for the period 2002 to 2005. We constructed canonical variate analysis (CVA) biplots with 90% alpha bags for the ve-dimensional data collected for males and females in 2002 and 2005 aggregated over faculties as well as for each faculty separately. The biplots illustrate, for our case study, that rank, age, research output and quali cations are related to remuneration. The CVA biplots show narrowing, widening and constant gender remuneration gaps in di erent faculties.
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    Precision, consistency and bias in emerging equity markets
    (International Foundation for Research & Development, 2014-05) Nel, W. S.; Bruwer, B. W.; Le Roux, N. J.
    The use of multiples is a popular approach employed by analysts to perform valuations. These multiples are based on optimal value drivers, the valuation performance of which should be underpinned by empirical findings from carefully designed, unbiased research initiatives. This paper firstly investigates the risk of biasing the design of market-based studies which aim to test the valuation performance of individual value drivers. The evidence revealed that, when testing the valuation performance of value drivers, there is an inherent risk of biasing the design of a study of this kind, and therefore, its outcome. Secondly, the paper presents evidence in support of the consistency of previous research findings regarding the valuation performance of individual value drivers in the South African market over the period 2001-2010. To this end, the paper introduces a new approach for the analysis of multi-dimensional equity valuation research data in the form of principal component analysis (PCA)-based biplots. Thirdly, the paper provides evidence that multiples-based modeling seems to be biased to the downside, which is an important consideration for analysts who choose to adjust their valuations outside of these models.
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    School results and access test results as indicators of first-year performance at university
    (Operations Research Society of South Africa, 2004) Bothma, A.; Botha, H. L.; Le Roux, N. J.
    ENGLISH SUMMARY : The goals set by the National Plan for Higher Education, the fact that many schools are still severely disadvantaged as well as far-reaching changes in the school system demand that South African universities urgently reconsider their admission procedures. Redesigning admission procedures calls for a thorough understanding of the interrelationships between school marks, results in existing access tests and first-year university performance. These interrelationships were statistically investigated in the case of the 1999, 2000 and 2001 intake groups, who were compelled to write access tests before being admitted to Stellenbosch University. The results of this investigation confirm an alarming degree of unpreparedness among many prospective students regarding what is expected of them at university. This is aggravated by school marks creating a totally unrealistic expectation of performance in the first year at university. It is emphasised that schools and authorities dealing with admission of prospective students at universities should be cognisant of the findings reported here. Furthermore, the statistical analyses demonstrate several novel techniques for investigating the interrelationship between school marks, access test results and university performance.
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    Simulation of a coal stacking process using an online X-Ray Fluorescence analyser
    (Operations Research Society of South Africa (ORSSA), 2018) Rossouw, R. F.; Coetzer, R. L. J.; Le Roux, N. J.
    The Sasol Coal Value Chain is a complex system consisting of blending, stacking and reclaiming of no fewer than six different coal sources with vastly different coal qualities. The amount and quality of the gas produced from coal depend crucially on the quality of the coal reclaimed from the coal stacking yards. In this paper the development of a real time coal quality simulation model using information from an online X-Ray Fluorescence analyser, integrated with various data sources from the Coal Supply Facility, is presented. The integration of different data sources is discussed to create a centralised and standardised data framework for input to the simulation model. The simulation of a heap profile of the coal quality for each heap stacked, together with the quality of the reclaimed coal, is discussed in detail. It is shown how the generated information from the model is utilised in the development of a reclaiming strategy.
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    Statistical properties of indicators of first-year performance at university
    (Operations Research Society of South Africa, 2004) Le Roux, N. J.; Bothma, A.; Botha, H. L.
    ENGLISH SUMMARY : Appraisal of admission procedures is a matter of urgency for South African universities, as well as for schools producing the prospective students. In this article the focus is on how various statistical procedures can be used to assess admission measures. Properties of the statistical distributions related to school results, access test results and first-year university performance are vital for decision-makers in schools preparing the prospective students and for those who wish to refine university admission procedures. These properties are scrutinised for the 1999, 2000 and 2001 intake groups required to write access tests before being admitted to Stellenbosch University. Using kernel density estimates the univariate distributions of all variables concerned are described in detail. Bagplots are proposed for visual displays of important features like location, spread, correlation, skewness, outliers and tails of bivariate distributions composed of university average performance and a school result or access test variable. Evidence is provided that certain access tests (Mathematics, Science and Numeracy Skills) have statistical distributions similar to that of average first-year university performance, but that average school marks could not be trusted to discriminate between potentially successful and unsuccessful university students.
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    Variable contribution identification and visualization in multivariate statistical process monitoring
    (Elsevier, 2020-01) Rossouw, R. F.; Coetzer, R. L. J.; Le Roux, N. J.
    Multivariate statistical process monitoring (MSPM) has received book-length treatments and wide spread application in industry. In MSPM, multivariate data analysis techniques such as principal component analysis (PCA) are commonly employed to project the (possibly many) process variables onto a lower dimensional space where they are jointly monitored given a historical or specified reference set that is within statistical control. In this paper, PCA and biplots are employed together in an innovative way to develop an efficient multivariate process monitoring methodology for variable contribution identification and visualization. The methodology is applied to a commercial coal gasification production facility with multiple parallel production processes. More specifically, it is shown how the methodology is used to specify the optimal principal component combinations and biplot axes for visualization and interpretation of process performance, and for the identification of the critical variables responsible for performance deviations, which yielded direct benefits for the commercial production facility.