Analyse des fondamentaux du multiple VE/EBITDA des entreprises cotées sur l'Euronext Bruxelles

Abstract

Executive Summary EBITDA multiples can be misleading in valuing a business. Too often, owners hear that a friend or competitor sold their business for "eight times EBITDA" and assume that metric can be applied to their businesses. This is not to say that there is no information in a multiple; rather, there is so much information imbedded in this one ratio that the shortcut calculation masks an intricate valuation thinking process that, once unwound, illustrates how difficult it is to apply a given multiple to another business (Jay Schembs, 2014). Based on this context, we study the main economic and financial fundamentals that impact the EBITDA multiple variability in Belgium context. Our samples of 78 companies in 2014, 83 companies in 2015 and 85 companies in 2016 are completely formed in October 2017. Using the multiple linear regression model, the empirical analysis provides results that demonstrate the importance of theoretical value fundamentals such as Risk, Growth and Investment requirement, in selecting companies “peers” group and in evaluating company thanks to EBITDA multiple. The empirical analysis also emphasizes the differentiated effects of the theoretical value fundamentals, given above, on the enterprise value or on the EBITDA multiple value. This analysis shows that the differentiated effects of theoretical value fundamentals on the EBITDA multiple value are due to the instability of enterprises values and to the volatility of data year by year in each sample. The analysis shows also that the instability of enterprises values and the volatility of data are impacted by the evolving investor feelings and the differentiated behaviours of the agents involved in the transaction as explained by Finalysis news (2014). Focused on all these reasons, the results allow us to retain for greater accuracy that it would be better to use the linear regression model and draw conclusions at the precise moment of the firm evaluation. More specifically, stabilizing a mathematical formula derived from the regression model requires to study to what extent the addition of a variable reflecting investors’ feelings would lead to a more reliable prediction model over time.