Boosting agnostic fundamental analysis: Using machine learning to identify mispricing in European stock markets

Matthias X. Hanauer, Marina Kononova and Marc Steffen Rapp

Finance Research Letters, 2022, vol. 48, issue C

Abstract: 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.

Keywords: Fundamental analysis; Market efficiency; Stock return; Machine learning; Random forest; Gradient boosting; European markets (search for similar items in EconPapers)
JEL-codes: C45 C53 G11 G14 G15 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:48:y:2022:i:c:s1544612322001465

DOI: 10.1016/j.frl.2022.102856

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