Wolff, Dominik ; Echterling, Fabian (2024)
Stock picking with machine learning.
In: Journal of Forecasting, 2024, 43 (1)
doi: 10.26083/tuprints-00027203
Artikel, Zweitveröffentlichung, Verlagsversion
 | Es ist eine neuere Version dieses Eintrags verfügbar. |
Kurzbeschreibung (Abstract)
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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Wolff, Dominik ; Echterling, Fabian |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Stock picking with machine learning |
| Sprache: | Englisch |
| Publikationsjahr: | 28 Mai 2024 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | Januar 2024 |
| Ort der Erstveröffentlichung: | New York |
| Verlag: | John Wiley & Sons |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Journal of Forecasting |
| Jahrgang/Volume einer Zeitschrift: | 43 |
| (Heft-)Nummer: | 1 |
| DOI: | 10.26083/tuprints-00027203 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/27203 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung DeepGreen |
| Kurzbeschreibung (Abstract): | 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. |
| Freie Schlagworte: | equity portfolio management, investment decisions, machine learning, neural networks, stock picking, stock selection |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-272037 |
| Zusätzliche Informationen: | The views expressed in this paper are those of the authors and do not necessarily reflect those of Deka Investment GmbH or its employees. |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik 300 Sozialwissenschaften > 330 Wirtschaft |
| Fachbereich(e)/-gebiet(e): | 01 Fachbereich Rechts- und Wirtschaftswissenschaften 01 Fachbereich Rechts- und Wirtschaftswissenschaften > Betriebswirtschaftliche Fachgebiete 01 Fachbereich Rechts- und Wirtschaftswissenschaften > Betriebswirtschaftliche Fachgebiete > Fachgebiet Unternehmensfinanzierung |
| Hinterlegungsdatum: | 28 Mai 2024 11:57 |
| Letzte Änderung: | 29 Mai 2024 10:06 |
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| Suche nach Titel in: | TUfind oder in Google |
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- Stock picking with machine learning. (deposited 28 Mai 2024 11:57) [Gegenwärtig angezeigt]
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