The development of optimal composite multiples models for the performance of equity valuations of listed South African companies : an empirical investigation

[摘要] ENGLISH ABSTRACT: The practice of combining single-factor multiples (SFMs) into composite multiplesmodels is underpinned by the theory that various SFMs carry incremental information,which, if encapsulated in a superior value estimate, largely eliminates biases anderrors in individual estimates. Consequently, the chief objective of this study was toestablish whether combining single value estimates into an aggregate estimate willprovide a superior value estimate vis-á-vis single value estimates.It is envisaged that this dissertation will provide a South African perspective, as anemerging market, to composite multiples modelling and the multiples-based equityvaluation theory on which it is based. To this end, the study included 16 SFMs, basedon value drivers representing all of the major value driver categories, namelyearnings, assets, dividends, revenue and cash flows.The validation of the research hypothesis hinged on the results obtained from theinitial cross-sectional empirical investigation into the factors that complicate thetraditional multiples valuation approach. The main findings from the initial analysis,which subsequently directed the construction of the composite multiples models, werethe following: Firstly, the evidence suggested that, when constructing multiples, multiples whosepeer groups are based on a combination of valuation fundamentals perform moreaccurate valuations than multiples whose peer groups are based on industryclassifications. Secondly, the research results confirmed that equity-based multiplesproduce more accurate valuations than entity-based multiples. Thirdly, the researchfindings suggested that multiples models that are constructed on earnings-basedvalue drivers, especially HE, offer higher degrees of valuation accuracy compared tomultiples models that are constructed on dividend-, asset-, revenue- or cash flowbasedvalue drivers.The results from the initial cross-sectional analysis were also subjected to an industryanalysis, which both confirmed and contradicted the initial cross-sectional-basedevidence. The industry-based research findings suggested that both the choice of optimal Peer Group Variable (PGV) and the choice of optimal value driver areindustry-specific.As with the initial cross-sectional analysis, earnings-based value drivers dominatedthe top positions in all 28 sectors that were investigated, while HE was againconfirmed as the most accurate individual driver.However, the superior valuation performance of multiples whose peer groups arebased on a combination of valuation fundamentals, as deduced from the crosssectionalanalysis conducted earlier, did not hold when subjected to an industryanalysis, suggesting that peer group selection methods are industry-specific.From this evidence, it was possible to construct optimal industry-specific SFMsmodels, which could then be compared to industry-specific composite models. Theevidence suggested that composite-based modelling offered, on annual average,between 20.21% and 44.59% more accurate valuations than optimal SFMs modellingover the period 2001 to 2010.The research results suggest that equity-based composite modelling may offersubstantial 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 seemsprudent to consider the inclusion of composite models as a more accurate alternative.

[发布机构] Stellenbosch University