(Out)smart the Peer Group in Market Comparison: Building Business Valuation Multiples by Machine Learning
Year: 2024
Type: article
Abstract: Traditionally, market comparison requires identifying a peer group, which still poses unresolved practical difficulties today. This research seeks to provide valuable insights into the practicality, efficiency, and accuracy of machine learning in valuing a company. It employs a state-of-the-art machine learning technique, Gradient Boosting Decision Trees (GBDT), to predict the valuation multiple directly. A yearly dataset of U.S. public companies from 1980–2021 was used. The most common multiples (EV/EBITDA, EV/EBIT, P/E, and EV/Sales) were tested. The performance of GBDT was assessed against an industry-based method. GBDT consistently outperformed the alternative method with an average 24 percentage point decrease in the median average percentage error. The results support GBDT’s potential as a supplementary tool in valuation practice.
Author: Veronika Staňková
Institution: Prague University of Economics and Business
Language: English
Subfield: Strategy and Management
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4405968856