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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. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    2. Isil Erel & Léa H Stern & Chenhao Tan & Michael S Weisbach, 2021. "Selecting Directors Using Machine Learning," NBER Chapters, in: Big Data: Long-Term Implications for Financial Markets and Firms, pages 3226-3264, National Bureau of Economic Research, Inc.
    3. Yang Bao & Bin Ke & Bin Li & Y. Julia Yu & Jie Zhang, 2020. "Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach," Journal of Accounting Research, Wiley Blackwell, vol. 58(1), pages 199-235, March.
    4. Daniele Bianchi & Matthias Büchner & Tobias Hoogteijling & Andrea Tamoni, 2021. "Corrigendum: Bond Risk Premiums with Machine Learning [Bond risk premiums with machine learning]," The Review of Financial Studies, Society for Financial Studies, vol. 34(2), pages 1090-1103.
    5. Fama, Eugene F. & French, Kenneth R., 2017. "International tests of a five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 123(3), pages 441-463.
    6. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    7. Barillas, Francisco & Kan, Raymond & Robotti, Cesare & Shanken, Jay, 2020. "Model Comparison with Sharpe Ratios," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 55(6), pages 1840-1874, September.
    8. Wolfgang Drobetz & Rebekka Haller & Christian Jasperneite & Tizian Otto, 2019. "Predictability and the cross section of expected returns: evidence from the European stock market," Journal of Asset Management, Palgrave Macmillan, vol. 20(7), pages 508-533, December.
    9. Joachim Freyberger & Andreas Neuhierl & Michael Weber & Andrew KarolyiEditor, 2020. "Dissecting Characteristics Nonparametrically," Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2326-2377.
    10. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    11. Daniele Bianchi & Matthias Büchner & Andrea Tamoni, 2021. "Bond Risk Premiums with Machine Learning [Quadratic term structure models: Theory and evidence]," The Review of Financial Studies, Society for Financial Studies, vol. 34(2), pages 1046-1089.
    12. Bartram, Söhnke M. & Grinblatt, Mark, 2021. "Global market inefficiencies," Journal of Financial Economics, Elsevier, vol. 139(1), pages 234-259.
    13. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    14. Fama, Eugene F. & French, Kenneth R., 2012. "Size, value, and momentum in international stock returns," Journal of Financial Economics, Elsevier, vol. 105(3), pages 457-472.
    15. Jacobs, Heiko, 2016. "Market maturity and mispricing," Journal of Financial Economics, Elsevier, vol. 122(2), pages 270-287.
    16. Kewei Hou & Chen Xue & Lu Zhang, 2020. "Replicating Anomalies," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2019-2133.
    17. Pushpendu Ghosh & Ariel Neufeld & Jajati Keshari Sahoo, 2020. "Forecasting directional movements of stock prices for intraday trading using LSTM and random forests," Papers 2004.10178, arXiv.org, revised Jun 2021.
    18. Bartram, Söhnke M. & Grinblatt, Mark, 2018. "Agnostic fundamental analysis works," Journal of Financial Economics, Elsevier, vol. 128(1), pages 125-147.
    19. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    20. Ozgur S. Ince & R. Burt Porter, 2006. "Individual Equity Return Data From Thomson Datastream: Handle With Care!," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 29(4), pages 463-479, December.
    21. Fama, Eugene F & MacBeth, James D, 1973. "Risk, Return, and Equilibrium: Empirical Tests," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 607-636, May-June.
    22. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    23. Hanauer, Matthias X. & Lauterbach, Jochim G., 2019. "The cross-section of emerging market stock returns," Emerging Markets Review, Elsevier, vol. 38(C), pages 265-286.
    24. G. Andrew Karolyi, 2016. "Home Bias, an Academic Puzzle," Review of Finance, European Finance Association, vol. 20(6), pages 2049-2078.
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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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