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Titel:

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

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Hanauer, Matthias X.; Kononova, Marina; Rapp, Marc Steffen
Nicht-TUM Koautoren:
ja
Kooperation:
national
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 r...     »
Intellectual Contribution:
Discipline-based Research
Zeitschriftentitel:
Finance Research Letters
Journal gelistet in FT50 Ranking:
nein
Jahr:
2022
Band / Volume:
48
Jahr / Monat:
2022-08
Seitenangaben Beitrag:
102856
Volltext / DOI:
doi:10.1016/j.frl.2022.102856
Verlag / Institution:
Elsevier BV
E-ISSN:
1544-6123
Publikationsdatum:
01.04.2022
Urteilsbesprechung:
0
Key publication:
Ja
Peer reviewed:
Ja
commissioned:
not commissioned
Professional Journal:
Nein
Technology:
Ja
Interdisziplinarität:
Ja
Leitbild:
;
Ethics und Sustainability:
Nein
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