Relative Valuation with Machine Learning
99 Pages Posted: 17 Dec 2020 Last revised: 24 Apr 2021
Date Written: December 1, 2020
We use a decision-tree-based machine learning approach to perform relative valuation. Stocks are valued using market-to-book, enterprise-value-to-assets and enterprise-value-to-sales multiples. Our machine learning models reduce median absolute valuation errors by between 17% and 50% relative to traditional models. The identified valuation drivers are consistent with theoretical predictions derived from a discounted cash flow approach. Accounting variables related to profitability, growth, efficiency and financial soundness are important valuation drivers. The valuations produced by machine learning models behave like fundamental values. A value-weighted strategy that buys (sells) undervalued (overvalued) stocks generates highly significant abnormal returns. When we use models trained on listed firms to value IPOs, machine learning models outperform traditional models in valuation accuracy and can identify mispriced IPOs.
Keywords: Relative Valuation, Fundamental Analysis, Comparable Companies, IPOs, Machine Learning, Gradient Boosting Machines
JEL Classification: G12, G14, M41, C5
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