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

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

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.

Suggested Citation

  • Hanauer, Matthias X. & Kononova, Marina & Rapp, Marc Steffen, 2022. "Boosting agnostic fundamental analysis: Using machine learning to identify mispricing in European stock markets," Finance Research Letters, Elsevier, vol. 48(C).
    • Handle: RePEc:eee:finlet:v:48:y:2022:i:c:s1544612322001465
    DOI: 10.1016/j.frl.2022.102856
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    References listed on IDEAS

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    1. Aslam, Faheem & Hunjra, Ahmed Imran & Ftiti, Zied & Louhichi, Wael & Shams, Tahira, 2022. "Insurance fraud detection: Evidence from artificial intelligence and machine learning," Research in International Business and Finance, Elsevier, vol. 62(C).
    2. Hanauer, Matthias X. & Kalsbach, Tobias, 2023. "Machine learning and the cross-section of emerging market stock returns," Emerging Markets Review, Elsevier, vol. 55(C).

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    More about this item

    Keywords

    Fundamental analysis; Market efficiency; Stock return; Machine learning; Random forest; Gradient boosting; European markets;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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