Deep learning applications in investment portfolio management: a systematic literature review

Volodymyr Novykov, Christopher Bilson, Adrian Gepp, Geoff Harris and Bruce Vanstone

Journal of Accounting Literature, 2023, vol. 47, issue 2, 245-276

Abstract: Purpose - Machine learning (ML), and deep learning in particular, is gaining traction across a myriad of real-life applications. Portfolio management is no exception. This paper provides a systematic literature review of deep learning applications for portfolio management. The findings are likely to be valuable for industry practitioners and researchers alike, experimenting with novel portfolio management approaches and furthering investment management practice. Design/methodology/approach - This review follows the guidance and methodology of Linnenlueckeet al. (2020), Massaroet al.(2016) and Fisch and Block (2018) to first identify relevant literature based on an appropriately developed search phrase, filter the resultant set of publications and present descriptive and analytical findings of the research itself and its metadata. Findings - The authors find a strong dominance of reinforcement learning algorithms applied to the field, given their through-time portfolio management capabilities. Other well-known deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN) and its derivatives, have shown to be well-suited for time-series forecasting. Most recently, the number of papers published in the field has been increasing, potentially driven by computational advances, hardware accessibility and data availability. The review shows several promising applications and identifies future research opportunities, including better balance on the risk-reward spectrum, novel ways to reduce data dimensionality and pre-process the inputs, stronger focus on direct weights generation, novel deep learning architectures and consistent data choices. Originality/value - Several systematic reviews have been conducted with a broader focus of ML applications in finance. However, to the best of the authors’ knowledge, this is the first review to focus on deep learning architectures and their applications in the investment portfolio management problem. The review also presents a novel universal taxonomy of models used.

Keywords: Portfolio management; Deep learning; Reinforcement learning; Portfolio optimisation; Portfolio construction; Investment management (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (text/html)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (application/pdf)
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eme:jalpps:jal-07-2023-0119

DOI: 10.1108/JAL-07-2023-0119

Access Statistics for this article

Journal of Accounting Literature is currently edited by Professor Martina Linnenluecke and Professor Tom Smith

More articles in Journal of Accounting Literature from Emerald Group Publishing Limited
Bibliographic data for series maintained by Emerald Support ().