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

Veronika Staňková
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Veronika Staňková: Prague University of Economics and Business, Czech Republic

European Journal of Business Science and Technology, 2024, vol. 10, issue 2, 156-172

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.

Keywords: market comparison method; Gradient Boosting Decision Trees; industry multiple; feature importance (search for similar items in EconPapers)
JEL-codes: G12 G32 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:men:journl:v:10:y:2024:i:2:p:156-172

DOI: 10.11118/ejobsat.2024.011

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