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An investigation of clean surplus value-added pricing models using time series methods for the UK 1983-1996

Peter Johnson


In this paper a family of clean-surplus models are developed from standard accounting and financial identities. The models rely on the use of non-traditional performance measures of clean surplus in relation to value-added, and growth in value-added, in order to establish market value to value-added ratios. These measures are relevant both to business strategy and to industrial organisation. They provide an explicit and robust means to link strategy formulation to industrial context and valuation, avoiding problematic aspects of traditional economic-value-added (EVA) measures. The time-series behaviour of the ratio of residual surpluses to value-added is modelled as simple ARIMA (1, 0, 0), (0, 0, 1), (0, 1, 1) and (1, 1 0) processes resulting in four families of valuation model. Using data on publicly quoted British companies available from Datastream to test the models, evidence is provided to support the value-relevance of the performance measures. The models suffer from problems of negative value predictions and excess sensitivity. Adjustment of the empirical data to mitigate these effects yields statistically significant results for three of the four specific models developed, suggesting that further testing of the models on other data sets is warranted.

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