European Journal of Business Science and Technology 2024, 10(2):156-172 | DOI: 10.11118/ejobsat.2024.011

(Out)smart the Peer Group in Market Comparison: Building Business Valuation Multiples by Machine Learning

Veronika Staňková¹

¹ Prague University of Economics and Business, Czech Republic

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.

Keywords: market comparison method, Gradient Boosting Decision Trees, industry multiple, feature importance

JEL classification: G12, G32

Received: May 16, 2024; Revised: July 31, 2024; Accepted: September 3, 2024; Published: December 31, 2024  Show citation