Enhancing portfolio management using artificial intelligence : literature review

Sutiene, Kristina; Schwendner, Peter; Sipos, Ciprian; Lorenzo, Luis; Mirchev, Miroslav; Lameski, Petre; Kabasinskas, Audrius; Tidjani, Chemseddine; Ozturkkal, Belma; Cerneviciene, Jurgita

doi:10.3389/frai.2024.1371502

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-30520

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	Enhancing portfolio management using artificial intelligence : literature review
Authors:	Sutiene, Kristina Schwendner, Peter Sipos, Ciprian Lorenzo, Luis Mirchev, Miroslav Lameski, Petre Kabasinskas, Audrius Tidjani, Chemseddine Ozturkkal, Belma Cerneviciene, Jurgita
et. al:	No
DOI:	10.3389/frai.2024.1371502 10.21256/zhaw-30520
Published in:	Frontiers in Artificial Intelligence
Volume(Issue):	7
Issue:	1371502
Issue Date:	8-Apr-2024
Publisher / Ed. Institution:	Frontiers Research Foundation
ISSN:	2624-8212
Language:	English
Subjects:	Portfolio; Asset allocation; Artificial intelligence; Machine learning; Optimization; Rebalancing; Explainability; Regulation
Subject (DDC):	006: Special computer methods 332.6: Investment
Abstract:	Building an investment portfolio is a problem that numerous researchers have addressed for many years. The key goal has always been to balance risk and reward by optimally allocating assets such as stocks, bonds, and cash. In general, the portfolio management process is based on three steps: planning, execution, and feedback, each of which has its objectives and methods to be employed. Starting from Markowitz's mean-variance portfolio theory, different frameworks have been widely accepted, which considerably renewed how asset allocation is being solved. Recent advances in artificial intelligence provide methodological and technological capabilities to solve highly complex problems, and investment portfolio is no exception. For this reason, the paper reviews the current state-of-the-art approaches by answering the core question of how artificial intelligence is transforming portfolio management steps. Moreover, as the use of artificial intelligence in finance is challenged by transparency, fairness and explainability requirements, the case study of post-hoc explanations for asset allocation is demonstrated. Finally, we discuss recent regulatory developments in the European investment business and highlight specific aspects of this business where explainable artificial intelligence could advance transparency of the investment process.
URI:	https://digitalcollection.zhaw.ch/handle/11475/30520
Fulltext version:	Published version
License (according to publishing contract):	CC BY 4.0: Attribution 4.0 International
Departement:	School of Management and Law
Organisational Unit:	Institute of Wealth & Asset Management (IWA)
Appears in collections:	Publikationen School of Management and Law

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Sutiene, K., Schwendner, P., Sipos, C., Lorenzo, L., Mirchev, M., Lameski, P., Kabasinskas, A., Tidjani, C., Ozturkkal, B., & Cerneviciene, J. (2024). Enhancing portfolio management using artificial intelligence : literature review. Frontiers in Artificial Intelligence, 7(1371502). https://doi.org/10.3389/frai.2024.1371502

Sutiene, K. et al. (2024) ‘Enhancing portfolio management using artificial intelligence : literature review’, Frontiers in Artificial Intelligence, 7(1371502). Available at: https://doi.org/10.3389/frai.2024.1371502.

K. Sutiene et al., “Enhancing portfolio management using artificial intelligence : literature review,” Frontiers in Artificial Intelligence, vol. 7, no. 1371502, Apr. 2024, doi: 10.3389/frai.2024.1371502.

SUTIENE, Kristina, Peter SCHWENDNER, Ciprian SIPOS, Luis LORENZO, Miroslav MIRCHEV, Petre LAMESKI, Audrius KABASINSKAS, Chemseddine TIDJANI, Belma OZTURKKAL und Jurgita CERNEVICIENE, 2024. Enhancing portfolio management using artificial intelligence : literature review. Frontiers in Artificial Intelligence. 8 April 2024. Bd. 7, Nr. 1371502. DOI 10.3389/frai.2024.1371502

Sutiene, Kristina, Peter Schwendner, Ciprian Sipos, Luis Lorenzo, Miroslav Mirchev, Petre Lameski, Audrius Kabasinskas, Chemseddine Tidjani, Belma Ozturkkal, and Jurgita Cerneviciene. 2024. “Enhancing Portfolio Management Using Artificial Intelligence : Literature Review.” Frontiers in Artificial Intelligence 7 (1371502). https://doi.org/10.3389/frai.2024.1371502.

Sutiene, Kristina, et al. “Enhancing Portfolio Management Using Artificial Intelligence : Literature Review.” Frontiers in Artificial Intelligence, vol. 7, no. 1371502, Apr. 2024, https://doi.org/10.3389/frai.2024.1371502.