Artikel
Stock picking with machine learning
We analyze machine learning algorithms for stock selection. Our study builds on weekly data for the historical constituents of the S&P500 over the period from January 1999 to March 2021 and builds on typical equity factors, additional firm fundamentals, and technical indicators. A variety of machine learning models are trained on the binary classification task to predict whether a specific stock outperforms or underperforms the cross‐sectional median return over the subsequent week. We analyze weekly trading strategies that invest in stocks with the highest predicted outperformance probability. Our empirical results show substantial and significant outperformance of machine learning‐based stock selection models compared to an equally weighted benchmark. Interestingly, we find more simplistic regularized logistic regression models to perform similarly well compared to more complex machine learning models. The results are robust when applied to the STOXX Europe 600 as alternative asset universe.
- Sprache
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Englisch
- Erschienen in
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Journal: Journal of Forecasting ; ISSN: 1099-131X ; Volume: 43 ; Year: 2023 ; Issue: 1 ; Pages: 81-102 ; Hoboken, NJ: Wiley
- Thema
-
equity portfolio management
investment decisions
machine learning
neural networks
stock picking
stock selection
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Wolff, Dominik
Echterling, Fabian
- Ereignis
-
Veröffentlichung
- (wer)
-
Wiley
- (wo)
-
Hoboken, NJ
- (wann)
-
2023
- DOI
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doi:10.1002/for.3021
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Wolff, Dominik
- Echterling, Fabian
- Wiley
Entstanden
- 2023
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