Private Firm Valuation in the Technology Sector: Illuminating the Interaction Between Multiple Performance and Peer Pool Setting

Abstract

Prior research, investigating the absolute performance of multiples as well as the relative superiority of different types of multiples, yields contradictory results that might be attributed to varying peer pool settings. This paper emphasizes on the technology sector and extends existing research, in its entirety being limited to trading multiples on listed companies, to transaction multiples on private firms. Employing a set of 22,967 observations on private market transactions of technology firms collected from 2000 until 2018, I examine the systematic impact of peer pooling on (i) the relative superiority of cross-sectoral multiples, (ii) the absolute superiority of sectoral multiples and, (iii) the absolute superiority of cross-sectoral multiples being segmented by various country-specific high-tech indicators. The multiples employed capture both, enterprise value and equity value multiples. The performance of the multiples in the various peer pool settings is evaluated according to bias as well as accuracy, utilizing the standard holdout routine on the transactions. The results indicate that (i) contradictory results in prior research on multiple's bias may be strongly attributed to the varying peer pools employed, (ii) the enterprise value to total assets multiple clearly dominates across all peer pools on a cross-sectoral basis, indicating that contradictory results on multiple's accuracy may not be attributed to the varying peer pools employed and, (iii) the performance of sectoral multiples depends on the value driver employed, showing only a weak relationship with the peer pool setting. Therefore, valuation analysts are recommended to utilize larger peer pools when employing cross-sectoral multiples, to emphasize on the enterprise value to total assets multiple, to further break down the high-tech sector into sub-sectors and, to employ sectoral multiples or multiples segmented according to country-specific high-tech indicators alternately.

Private Firm Valuation Using Multiples: Can Artificial Intelligence Algorithms Learn Better Peer Groups?

“…Output data were organized into five variables (value drivers), each representing a robust financial multiplier (hereinafter: FM) of the target company (real values) popular in the valuation literature: (i) EPV (enterprise value) to sales, (ii) EPV to EBITDA (earnings before interest, taxes, depreciation, and amortization), (iii) EPV to EBIT (earnings before interest and taxes), (iv) EPV to total assets, and (v) EQV (equity value) to EBT (earnings before taxes). The EPV to sales multiple and the EPV to total assets multiple are denoted by [1,41]…”

“…where the pension reserves P i are added only if the interest on pensions is not part of the cost of goods sold. Finally, the EQV to EBT multiple is denoted by [1]…”

“…Hence, the choice of an optimal peer group is one of the most crucial steps involved in company valuation with multiples. In defining peers to ensure the maximum performance of the multiples, valuators must simultaneously define the optimal peer pool [1], the optimal size of the peer group (e.g., [2,3]), the aggregation function for the peer-related multiples, the time horizon of the related firm transactions regarded [4], and the optimal set of peer selection criteria employed. Regarding the number of peer selection criteria employed, two schools on peer selection/filtering strategies are promoted, emphasizing the trade-off between a narrow and a broad market materializing in a small or Information 2024, 15, 305 2 of 16 large number of selection criteria employed (e.g., [5,6]).…”