Exploring the evolution of scientific publication on portfolio optimization in the light of artificial intelligence: A bibliometric study
The rapid evolution of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has profoundly influenced various domains, including portfolio optimization. In today’s dynamic and interconnected global economy, understanding the development of scientific publications in this field...
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| Format: | Article |
| Language: | English |
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Growing Science
2025-01-01
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| Series: | Accounting |
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| Online Access: | http://www.growingscience.com/ac/Vol11/ac_2024_13.pdf |
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| author | Mostafa Shabani Rouzbeh Ghousi Emran Mohammadi |
| author_facet | Mostafa Shabani Rouzbeh Ghousi Emran Mohammadi |
| author_sort | Mostafa Shabani |
| collection | DOAJ |
| description | The rapid evolution of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has profoundly influenced various domains, including portfolio optimization. In today’s dynamic and interconnected global economy, understanding the development of scientific publications in this field is crucial for both academics and practitioners. This paper aims to conduct a comprehensive bibliometric study of the scientific literature on portfolio optimization, focusing on the impact of AI, ML, and DL advancements. By analyzing key trends, influential publications, and emerging research areas, this study provides valuable insights into the progression of portfolio optimization research in the context of these transformative technologies, helping to map future directions and identify knowledge gaps in the field. This paper endeavors to present an exhaustive synthesis of the most recent advancements and innovations within the domain of portfolio optimization, particularly as influenced by progressive developments in AI, ML and DL from 1996 to 2024. Employing a rigorous bibliometric analysis, this study scrutinizes the structural and global paradigms governing this field. The analytical framework integrates several dimensions, including: (1) comprehensive dataset interrogation, (2) critical evaluation of source repositories, (3) contributions of seminal authors, (4) geographical and institutional affiliations, (5) document-centric analysis, and (6) exploration of keyword dynamics. A corpus of 745 bibliographic entries, meticulously curated from the Web of Science database, forms the basis of this inquiry, which utilizes advanced Scientometric network methodologies to extrapolate substantive research insights. The discourse culminates in a robust critique of the inherent strengths and methodological limitations, while delineating strategic avenues for future research, with the objective of steering ongoing scholarly discourse in the realm of portfolio optimization. The empirical outcomes of this study enhance the understanding of prevailing intellectual trajectories, thus laying a fortified foundation for future investigative pursuits in this critically evolving discipline.
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| format | Article |
| id | doaj-art-babe7f293b034d2da44aa8fd1dd35112 |
| institution | DOAJ |
| issn | 2369-7393 2369-7407 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Growing Science |
| record_format | Article |
| series | Accounting |
| spelling | doaj-art-babe7f293b034d2da44aa8fd1dd351122025-08-20T02:40:30ZengGrowing ScienceAccounting2369-73932369-74072025-01-01111719010.5267/j.ac.2024.10.002Exploring the evolution of scientific publication on portfolio optimization in the light of artificial intelligence: A bibliometric studyMostafa ShabaniRouzbeh Ghousi Emran MohammadiThe rapid evolution of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has profoundly influenced various domains, including portfolio optimization. In today’s dynamic and interconnected global economy, understanding the development of scientific publications in this field is crucial for both academics and practitioners. This paper aims to conduct a comprehensive bibliometric study of the scientific literature on portfolio optimization, focusing on the impact of AI, ML, and DL advancements. By analyzing key trends, influential publications, and emerging research areas, this study provides valuable insights into the progression of portfolio optimization research in the context of these transformative technologies, helping to map future directions and identify knowledge gaps in the field. This paper endeavors to present an exhaustive synthesis of the most recent advancements and innovations within the domain of portfolio optimization, particularly as influenced by progressive developments in AI, ML and DL from 1996 to 2024. Employing a rigorous bibliometric analysis, this study scrutinizes the structural and global paradigms governing this field. The analytical framework integrates several dimensions, including: (1) comprehensive dataset interrogation, (2) critical evaluation of source repositories, (3) contributions of seminal authors, (4) geographical and institutional affiliations, (5) document-centric analysis, and (6) exploration of keyword dynamics. A corpus of 745 bibliographic entries, meticulously curated from the Web of Science database, forms the basis of this inquiry, which utilizes advanced Scientometric network methodologies to extrapolate substantive research insights. The discourse culminates in a robust critique of the inherent strengths and methodological limitations, while delineating strategic avenues for future research, with the objective of steering ongoing scholarly discourse in the realm of portfolio optimization. The empirical outcomes of this study enhance the understanding of prevailing intellectual trajectories, thus laying a fortified foundation for future investigative pursuits in this critically evolving discipline. http://www.growingscience.com/ac/Vol11/ac_2024_13.pdfportfolio optimizationartificial intelligencemachine learning |
| spellingShingle | Mostafa Shabani Rouzbeh Ghousi Emran Mohammadi Exploring the evolution of scientific publication on portfolio optimization in the light of artificial intelligence: A bibliometric study Accounting portfolio optimization artificial intelligence machine learning |
| title | Exploring the evolution of scientific publication on portfolio optimization in the light of artificial intelligence: A bibliometric study |
| title_full | Exploring the evolution of scientific publication on portfolio optimization in the light of artificial intelligence: A bibliometric study |
| title_fullStr | Exploring the evolution of scientific publication on portfolio optimization in the light of artificial intelligence: A bibliometric study |
| title_full_unstemmed | Exploring the evolution of scientific publication on portfolio optimization in the light of artificial intelligence: A bibliometric study |
| title_short | Exploring the evolution of scientific publication on portfolio optimization in the light of artificial intelligence: A bibliometric study |
| title_sort | exploring the evolution of scientific publication on portfolio optimization in the light of artificial intelligence a bibliometric study |
| topic | portfolio optimization artificial intelligence machine learning |
| url | http://www.growingscience.com/ac/Vol11/ac_2024_13.pdf |
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