The Library
Machine learning for active portfolio management
Tools
Tools
Tools
Bartram, Söhnke M., Branke, Jürgen, De Rossi, Giuliano and Motahari, Mehrshad (2021) Machine learning for active portfolio management. Journal of Financial Data Science, 3 (3). pp. 9-30. doi:10.3905/jfds.2021.1.071
![[img]](http://wrap.warwick.ac.uk/style/images/fileicons/application_pdf.png) |
PDF
WRAP-machine-learning-active-portfolio-management-Bartram-2021.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only until 2 August 2023. Contact author directly, specifying your specific needs. - Requires a PDF viewer. Download (11Mb) |
![[img]](http://wrap.warwick.ac.uk/style/images/fileicons/text_plain.png) |
Plain Text
153653_correspondence_June_2021.txt - Permissions Correspondence Embargoed item. Restricted access to Repository staff only Download (4Kb) |
Official URL: https://doi.org/10.3905/jfds.2021.1.071
Abstract
Machine learning (ML) methods are attracting considerable attention among academics in the field of finance. However, it is commonly believed that ML has not transformed the asset management industry to the same extent as other sectors. This survey focuses on the ML methods and empirical results available in the literature that matter most for active portfolio management. ML has asset management applications for signal generation, portfolio construction, and trade execution, and promising findings have been reported. Reinforcement learning (RL), in particular, is expected to play a more significant role in the industry. Nevertheless, the performance of a sample of active exchange-traded funds (ETF) that use ML in their investments tends to be mixed. Overall, ML techniques show great promise for active portfolio management, but investors should be cautioned against their main potential pitfalls.
| Item Type: | Journal Article | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Subjects: | H Social Sciences > HG Finance Q Science > Q Science (General) |
||||||||||
| Divisions: | Faculty of Social Sciences > Warwick Business School > Finance Group Faculty of Social Sciences > Warwick Business School |
||||||||||
| Library of Congress Subject Headings (LCSH): | Portfolio management, Portfolio management -- Computer simulation , Machine learning , Exchange traded funds | ||||||||||
| Journal or Publication Title: | Journal of Financial Data Science | ||||||||||
| Publisher: | Portfolio Management Research | ||||||||||
| ISSN: | 2640-3943 | ||||||||||
| Official Date: | 21 June 2021 | ||||||||||
| Dates: |
|
||||||||||
| Volume: | 3 | ||||||||||
| Number: | 3 | ||||||||||
| Page Range: | pp. 9-30 | ||||||||||
| DOI: | 10.3905/jfds.2021.1.071 | ||||||||||
| Status: | Peer Reviewed | ||||||||||
| Publication Status: | Published | ||||||||||
| Reuse Statement (publisher, data, author rights): | © 2021, Portfolio Management Research. This is the author's accepted manuscript of the work. It is posted here by permission of Portfolio Management Research. The Version of Record was published in 'Bartram, Söhnke M., Branke, Jürgen, De Rossi, Giuliano and Motahari, Mehrshad (2021) Machine learning for active portfolio management. Journal of Financial Data Science, 3 (3) 9-30 http://doi.org/10.3905/jfds.2021.1.071 | ||||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||||
| Copyright Holders: | © 2021 Pageant Media Ltd | ||||||||||
| Related URLs: |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
