Editorial Type:
Article Category: Research Article
 | 
Online Publication Date: Dec 01, 2015

Valuation Using Industry Multiples: How to Choose the Most Relevant Multiples

SFAF and
SFAF
Page Range: 173 – 183
Save
Download PDF

Our research focuses specifically on the methodology to be applied to improve the relevance of the multiples-based valuation method regarding the identification of the most relevant multiples (i.e., that reduce the relative absolute valuation error within any industry-based peer group). In line with prior empirical studies, our results confirm that Enterprise Value multiples based on EBIT and EBITDA perform better, compared to Sales and Capital Employed, and that multiples based on forward-looking EBIT and EBITDA are more relevant compared to corresponding actual earnings. In the absence of forward-looking earnings, available at the date of valuation, our study shows that EBITDA multiples provide better estimates than EBIT multiples do. Beyond these general results, the approach implemented in our research can be easily reproduced by practitioners (e.g., financial analysts, M&A advisors, independent appraisers) to identify case-by-case the multiples that are the most relevant within any industry-based peer group.

  • Download PDF
Copyright: © 2015, American Society of Appraisers
Figure 1
Figure 1

Distribution of Relative Absolute Valuation Errors (RAVE), European Industry Peer Groups, March 31, 2008. Notes: The lower (higher) bound of the vertical line corresponds to the 1st (3rd) quartile of the RAVE distribution. When the rectangle in the middle of the line is gray, its bottom is the median and the top is the mean of the RAVE distribution; when it is dark, it is the opposite.


Figure 2
Figure 2

Distribution of Relative Absolute Valuation Errors (RAVE), Luxury Industry Peer Group, April 30, 2011. Notes: The lower (higher) bound of the vertical line corresponds to the 1st (3rd) quartile of the RAVE distribution. When the rectangle in the middle of the line is gray, its bottom is the median and the top is the mean of the RAVE distribution; when it is dark, it is the opposite.


Figure 3
Figure 3

Distribution of Relative Absolute Valuation Errors (RAVE), Cosmetics Industry Peer Group, April 30, 2011. Notes: The lower (higher) bound of the vertical line corresponds to the 1st (3rd) quartile of the RAVE distribution. When the rectangle in the middle of the line is gray, its bottom is the median and the top is the mean of the RAVE distribution; when it is dark, it is the opposite.


Figure A1
Figure A1

Distribution of Relative Absolute Valuation Errors (RAVE), European Industry Peer Groups, March 31, 2008. Notes: The lower (higher) bound of the vertical line corresponds to the 1st (3rd) quartile of the RAVE distribution. When the rectangle in the middle of the line is gray, its bottom is the median and the top is the mean of the RAVE distribution; when it is dark, it is the opposite.


Contributor Notes

Edouard Chastenet is an associate professor at the Business Administration Institute (IAE) of Université de Lyon, France. Alain Marion is a professor at the Business Administration Institute (IAE) of Université de Lyon, France.