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

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 their 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, as well as substantial non-naïve market inefficiencies.

Keywords: Fundamental analysis, market efficiency, stock return, machine learning, random forest, gradient boo

JEL Classification: C45, C53, G11, G14, G15

Suggested Citation: Suggested Citation

Hanauer, Matthias Xaver and Kononova, Marina and Rapp, Marc Steffen, Boosting Agnostic Fundamental Analysis: Using Machine Learning to Identify Mispricing in European Stock Markets (December 5, 2021). Finance Research Letters, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3977872 or http://dx.doi.org/10.2139/ssrn.3977872