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

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  • Veronika Staňková

    (Prague University of Economics and Business, Czech Republic)

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.

Suggested Citation

  • Veronika Staňková, 2024. "(Out)smart the Peer Group in Market Comparison: Building Business Valuation Multiples by Machine Learning," European Journal of Business Science and Technology, Mendel University in Brno, Faculty of Business and Economics, vol. 10(2), pages 156-172.
    • Handle: RePEc:men:journl:v:10:y:2024:i:2:p:156-172
    DOI: 10.11118/ejobsat.2024.011
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    More about this item

    Keywords

    market comparison method; Gradient Boosting Decision Trees; industry multiple; feature importance;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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