Boosting Agnostic Fundamental Analysis: Using Machine Learning to Identify Mispricing in European Stock Markets

Matthias X. Hanauer, Marina Kononova, Marc Steffen Rapp*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Interested in fundamental analysis and inspired by Bartram and Grinblatt (2018, 2021), we apply linear regression (LR) and tree-based machine learning (ML) methods to estimate monthly peer-implied fair values of European stocks from 21 accounting variables. Comparing LR and ML models, we document substantial heterogeneity in the importance of predictors as measured by SHAP values. Examining trading strategies based on deviations from fair values, we find ML-strategies earn substantially higher risk-adjusted returns (“alpha”) than simple LR-counterparts (48–66 vs. 11–36 bp per month for value-weighted portfolios). Our findings document the importance of allowing for non-linearities and interactions in fundamental analysis.
Original languageEnglish
Article number102856
JournalFinance Research Letters
Number of pages10
Publication statusPublished - Aug 2022


  • Fundamental analysis
  • Market efficiency
  • Stock return
  • Machine learning
  • Random forest
  • Gradient boosting
  • European markets

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