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Title:

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

Document type:
Zeitschriftenaufsatz
Author(s):
Hanauer, Matthias X.; Kononova, Marina; Rapp, Marc Steffen
Non-TUM Co-author(s):
ja
Cooperation:
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
Journal title:
Finance Research Letters
Journal listet in FT50 ranking:
nein
Year:
2022
Journal volume:
48
Year / month:
2022-08
Pages contribution:
102856
Fulltext / DOI:
doi:10.1016/j.frl.2022.102856
Publisher:
Elsevier BV
E-ISSN:
1544-6123
Date of publication:
01.04.2022
Judgement review:
0
Key publication:
Ja
Peer reviewed:
Ja
Commissioned:
not commissioned
Professional Journal:
Nein
Technology:
Ja
Interdisciplinarity:
Ja
Mission statement:
;
Ethics and Sustainability:
Nein
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