Relative valuation with machine learning
Geertsema, Paul Lu, Helen
Geertsema, Paul
Lu, Helen
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Publication Type
Journal article with impact factor
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Supervisor
Publication Year
2023
Journal
Journal of Accounting Research
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Publication Volume
61
Publication Issue
1
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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.
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Keywords
Relative Valuation, Peer Firms, Fundamental Analysis, Machine Learning, GBM, Gradient Boosting Machine