Doctoral Degrees (School of Accountancy)

Permanent URI for this collection

https://scholar.sun.ac.za/handle/10019.1/703

Browse

Browsing Doctoral Degrees (School of Accountancy) by browse.metadata.advisor "Le Roux, Niel J."

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    The development of optimal composite multiples models for the performance of equity valuations of listed South African companies : an empirical investigation
    (Stellenbosch : Stellenbosch University, 2014-10-09) Nel, Willem Soon; Bruwer, Barbara Wilhelmina; Le Roux, Niel J.; Stellenbosch University. Faculty of Economic and Management Sciences. School of Accounting.
    ENGLISH ABSTRACT: The practice of combining single-factor multiples (SFMs) into composite multiples models is underpinned by the theory that various SFMs carry incremental information, which, if encapsulated in a superior value estimate, largely eliminates biases and errors in individual estimates. Consequently, the chief objective of this study was to establish whether combining single value estimates into an aggregate estimate will provide a superior value estimate vis-á-vis single value estimates. It is envisaged that this dissertation will provide a South African perspective, as an emerging market, to composite multiples modelling and the multiples-based equity valuation theory on which it is based. To this end, the study included 16 SFMs, based on value drivers representing all of the major value driver categories, namely earnings, assets, dividends, revenue and cash flows. The validation of the research hypothesis hinged on the results obtained from the initial cross-sectional empirical investigation into the factors that complicate the traditional multiples valuation approach. The main findings from the initial analysis, which subsequently directed the construction of the composite multiples models, were the following: Firstly, the evidence suggested that, when constructing multiples, multiples whose peer groups are based on a combination of valuation fundamentals perform more accurate valuations than multiples whose peer groups are based on industry classifications. Secondly, the research results confirmed that equity-based multiples produce more accurate valuations than entity-based multiples. Thirdly, the research findings suggested that multiples models that are constructed on earnings-based value drivers, especially HE, offer higher degrees of valuation accuracy compared to multiples models that are constructed on dividend-, asset-, revenue- or cash flowbased value drivers. The results from the initial cross-sectional analysis were also subjected to an industry analysis, which both confirmed and contradicted the initial cross-sectional-based evidence. The industry-based research findings suggested that both the choice of optimal Peer Group Variable (PGV) and the choice of optimal value driver are industry-specific. As with the initial cross-sectional analysis, earnings-based value drivers dominated the top positions in all 28 sectors that were investigated, while HE was again confirmed as the most accurate individual driver. However, the superior valuation performance of multiples whose peer groups are based on a combination of valuation fundamentals, as deduced from the crosssectional analysis conducted earlier, did not hold when subjected to an industry analysis, suggesting that peer group selection methods are industry-specific. From this evidence, it was possible to construct optimal industry-specific SFMs models, which could then be compared to industry-specific composite models. The evidence suggested that composite-based modelling offered, on annual average, between 20.21% and 44.59% more accurate valuations than optimal SFMs modelling over the period 2001 to 2010. The research results suggest that equity-based composite modelling may offer substantial gains in precision over SFMs modelling. These gains are, however, industry-specific and a carte blanche application thereof is ill advised. Therefore, since investment practitioners’ reports typically include various multiples, it seems prudent to consider the inclusion of composite models as a more accurate alternative.