Valuation biases, error measures, and the conglomerate discount

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URL:	https://ub-madoc.bib.uni-mannheim.de/2531
URN:	urn:nbn:de:bsz:180-madoc-25317
Document Type:	Working paper
Year of publication:	2007
The title of a journal, publication series:	Rationalitätskonzepte, Entscheidungsverhalten und ökonomische Modellierung
Volume:	07-37
Place of publication:	Mannheim
Publication language:	English
Institution:	School of Law and Economics > Sonstige - Fakultät für Rechtswissenschaft und Volkswirtschaftslehre
MADOC publication series:	Sonderforschungsbereich 504 > Rationalitätskonzepte, Entscheidungsverhalten und ökonomische Modellierung (Laufzeit 1997 - 2008)
Subject:	330 Economics
Classification:	JEL: G34 G10 M41 ,
Subject headings (SWD):	Unternehmensbewertung , Vergleich , Fehler , Statistik , Bias
Keywords (English):	Valuation , Financial Ratios , Multiples , Dividend Discount Model , Discounted Cash Flow Model , Residual Income Model
Abstract:	We investigate biases of valuation methods and document that these depend largely on the choice of error measure (percentage vs. logarithmic errors) used to compare valuation procedures. We analyze four multiple valuation methods (averaging with the arithmetic mean, harmonic mean, median, and the geometric mean) and three present value approaches (dividend discount model, discounted cash flow model, residual income model). Percentage errors generate a positive bias for most multiples, and they imply that setting company values equal to their book values often becomes the best valuation method. Logarithmic errors avoid unwanted consequences and imply that the median and the geometric mean are unbiased while the arithmetic mean is biased upward as much as the harmonic mean is biased downward. The dividend discount model dominates the DCF-model only for percentage errors, while the opposite is true for logarithmic errors. The residual income model is optimal for both error measures.
Translation of the title:	Biases and error measures : how to compare valuation methods (English)
Additional information:	Available at SSRN: http://ssrn.com/abstract