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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 multidimensional 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.
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 multidimensional 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.
Precision, Consistency and Bias in Emerging Equity Markets
Nel, WS (author)
2014-05-30
doi:10.22610/jebs.v6i5.501
Journal of Economics and Behavioral Studies; Vol 6 No 5 (2014); pp. 386-399 ; 2220-6140 ; 10.22610/jebs.v6i5
Article (Journal)
Electronic Resource
English
DDC:
690
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