Valuation multiples : an empirical analysis of optimal multiple types and key peer group characteristics

This thesis identifies corporate valuation as a quintessential discipline within business administration that enables informed and thus effective decision-making. While there are various different methods within the field of valuation, the multiples-based approach stands out as the first choice for many practitioners due to its simplicity and low requirements for input parameters. Despite that, there is surprisingly little consensus on how to effectively apply the multiples valuation method in a standardized way. Specifically, there are no clear guidelines on the following two issues: 1. Which type of multiple should be used for the specific valuation target? 2. Based on which criteria should a peer group be assembled so that it best reflects the characteristics of the valuation target? In order to provide guidance for a more robust multiples-based valuation approach, this thesis conducts empirical analysis to answer the two questions outlined. For the first issue, an extensive dataset obtained from Bloomberg is used to predict the value of 1200 different companies based on their respective peer group, using various different types of multiples for each firm. The peergroup-derived predictions are then compared to the actual values of the companies, which enables a evaluation across all multiple types to find the most effective one. The results of the analysis are broken down into different geographic regions and industry sectors to identify variations across both dimensions. For the second research question, regression models are employed to test how the consideration of several peer group characteristics deemed important by literature might help to select better peer companies. These characteristics include the geographical headquarters location, company size, revenue growth rate, debt-to-equity ratio, operating margin, and free cash flow margin. Using the same dataset as in the previous analysis, it is tested to what extent better alignment of valuation target and peer group with respect to the mentioned characteristics leads to a better fit of valuation multiples. Overall, the EV/EBITDA multiple is found to be the best multiple type. On average, peergroup-derived values based on the EV/EBITDA multiple deviate by 34.4% from a firm’s actual value. It is closely followed by the EV/EBIT multiple and the P/E multiple, which both exhibit average deviations of 37.0%. The EV/EBITDA also performs best across all regions and 70% of the industry sectors. A further breakdown reveals that each of the six multiples analyzed outperforms the others in distinct segments, which highlights that all of them are generally relevant tools for valuation practitioners. It also emphasizes that while the EV/EBITDA multiple turns out to be the most robust on aggregate, blindly applying it to all types of companies does not lead to the most accurate results. Instead, one has to carefully consider to which industry and region the valuation target belongs and then find the best multiple type based on that particular segment. When assembling a peer group, it is of utmost importance to consider the industry sector of the valuation target. Apart from that, the linear regression analysis indicates that the evaluated characteristics typically have limited explanatory power, with Adjusted R-squared values of around or below 15%. A peer group that is aligned with the valuation target in terms of geography slightly improves the effectiveness of multiples; the same is the case for aligned operating margins and revenue growth rates. With regard to the debt-to-equity ratio and the free cash flow margin, there is no clear tendency whether better alignment of the valuation target and its peer companies helps improve the result of the multiples valuation. Surprisingly, having a peer group of companies with a similar market capitalization as the valuation target negatively impacts the effectiveness of multiples. Overall, the results indicate that to assemble the best possible peer group, the target company’s specific value drivers – which can vary greatly even within an industry – have to be thoroughly understood. The characteristics analyzed in this paper provide a rough indication of what could be potential value drivers, but they have to be further tailored to the valuation target. Generalized rules appear not to be sufficient to select peer companies effectively. In conclusion, this thesis delivers robust results on what are the most effective multiple types for evaluating firms in various sectors and regions. Valuation practitioners can directly apply these findings to value companies in a more systematic way. The analysis of the most relevant factors to consider when selecting peer companies, however, should be rather seen as the foundation for additional empirical research. Its results are not conclusive enough as to effectively guide practitioners towards a more reliable valuation approach.