Predicting Valuation Multiples: Implementing Fundamental Regression Approaches for Relative Valuation Purposes

Peder Johan Havander & Felix Vilhelm Axel Goich

Student thesis: Master thesis


This study addresses the empirical deficit that surrounds the underlying relationship between theoretically derived value drivers and valuation multiples, and whether fundamental regressions approaches can generate accurate predictions of intrinsic firm value. Even though previous literature suggests that regression analysis can be utilized to account for heterogeneity amongst comparable firms, few studies have empirically evaluated the accuracy of predicted valuation multiples based on statistical approaches. In addition, while relative valuation is seen as the most commonly applied valuation technique, regression analysis is rarely used as a primary tool for this specific purpose in practice. Instead, relative valuation processes are often permeated by subjective adjustments that commonly hold limited theoretical and statistical substance. Guided by theoretical underpinnings on relative valuation as well as prior empirical findings, the conducted study consequently develops theoretically founded regression approaches that objectively account for individual firm performance in terms of growth, profitability and risk. It is subsequently tested whether these approaches are able to generate accurate predictions of observed EV/EBITDA multiples, which constitutes the sole dependent variable of the study. Utilizing a sample of 965 publicly traded US firms obtained from the S&P Composite 1500 index, a series of multi-level regressions generate findings that vary significantly across studied sectors and industries. These results contradict the theoretical assumption that growth, profitability, and risk uniformly hold significant predictive power of the studied multiple across firms. On the other hand, it is discovered that valuation estimates based on fundamental value drivers are significant predictors of intrinsic firm value in a majority of instances. Yet, the accuracy of developed predictions is not found to be significantly superior to the accuracy of predictions based on simple peer group averages. With regards to the ultimate research question of the conducted study, a regression approach based on fundamental value drivers is concluded to be a valid methodology in predicting firm value, even though prediction accuracy should be considered limited for some of the studied sectors and industries. As such, utilizing regression approaches for the purpose of relative valuation should be seen as a complement rather than standalone tool in the search for intrinsic firm value. Overall, obtained results are argued to contribute from a holistic standpoint to the academic discourse within multiple accuracy. Apart from providing empirical evidence on the fundamental feasibility of applying a regression approach, the statistical analysis sheds light on the relative importance of value drivers across sectors and industries. Furthermore, the study demonstrates how a statistical method that is developed from theoretical underpinnings can handle differences between firms without being bound to subjective adjustments. Additionally, the research provides empirical insights to the prevalent discussion on the optimal level of analysis by adopting several definitions of peer groups.

EducationsMSc in Applied Economics and Finance, (Graduate Programme) Final Thesis
Publication date2019
Number of pages142