Optimal peer selection in enterprise valuation: a product life cycle enhancement

Abstract

This research investigates the optimal selection of comparable firms in enterprise valuation, using U.S. firms from 1997 to 2021 grouped by two-digit SIC industry. I introduce a new approach in which peers are matched not only on their forecasted growth but also on their product life cycle stage, and I construct the enterprise value multiple from net operating assets to keep the multiple consistent with operating fundamentals. The study proceeds in two stages. First, I estimate annual cross-sectional regressions to identify the fundamental drivers of the multiple, finding that profitability and long-term growth explain enterprise value and that the product life cycle is clearly linked to relative valuation. Second, I compare growth rate matching against product life cycle matching across peer group sizes ranging from two firms to the entire industry cross-section, computing implied values from the harmonic mean of peers' multiples and testing accuracy through one-sided bootstrap resampling with 10,000 iterations. The results demonstrate that valuation accuracy improves sharply from two to five peers but deteriorates as the pool expands further, and that a small set of closely matched peers minimizes the systematic downward bias. The results also show that growth matching significantly outperforms product life cycle matching in every period and at every peer group size, although both methods share a common, state-dependent error pattern that points to market-wide mispricing rather than to weaknesses specific to either method.