 Relative Valuation with Machine LearningPaul Geertsema and Helen Lu Journal of Accounting Research, 2023, vol. 61, issue 1, 329-376 Abstract: We use machine learning for relative valuation and peer firm selection. In out‐of‐sample tests, our machine learning models substantially outperform traditional models in valuation accuracy. This outperformance persists over time and holds across different types of firms. The valuations produced by machine learning models behave like fundamental values. Overvalued stocks decrease in price and undervalued stocks increase in price in the following month. Determinants of valuation multiples identified by machine learning models are consistent with theoretical predictions derived from a discounted cash flow approach. Profitability ratios, growth measures, and efficiency ratios are the most important value drivers throughout our sample period. We derive a novel method to express valuation multiples predicted by our machine learning models as weighted averages of peer firm multiples. These weights are a measure of peer–firm comparability and can be used for selecting peer‐groups. Date: 2023 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:joares:v:61:y:2023:i:1:p:329-376 Ordering information: This journal article can be ordered from Access Statistics for this article Journal of Accounting Research is currently edited by Philip G. Berger, Luzi Hail, Christian Leuz, Haresh Sapra, Douglas J. Skinner, Rodrigo Verdi and Regina Wittenberg Moerman More articles in Journal of Accounting Research from Wiley Blackwell |
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