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Stock picking with machine learning

Wolff, Dominik ; Echterling, Fabian (2024)
Stock picking with machine learning.
In: Journal of Forecasting, 2024, 43 (1)
doi: 10.26083/tuprints-00027203
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: Stock picking with machine learning
Language: English
Date: 28 May 2024
Place of Publication: Darmstadt
Year of primary publication: January 2024
Place of primary publication: New York
Publisher: John Wiley & Sons
Journal or Publication Title: Journal of Forecasting
Volume of the journal: 43
Issue Number: 1
DOI: 10.26083/tuprints-00027203
Corresponding Links:
Origin: Secondary publication DeepGreen

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.

Uncontrolled Keywords: equity portfolio management, investment decisions, machine learning, neural networks, stock picking, stock selection
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-272037
Additional Information:

The views expressed in this paper are those of the authors and do not necessarily reflect those of Deka Investment GmbH or its employees.

Classification DDC: 000 Generalities, computers, information > 004 Computer science
300 Social sciences > 330 Economics
Divisions: 01 Department of Law and Economics > Betriebswirtschaftliche Fachgebiete > Corporate finance
Date Deposited: 28 May 2024 11:57
Last Modified: 28 May 2024 11:58
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/27203
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