Evolution of Portfolio Selection Literature: A Bibliometric Study with Emphasis on AI, ML, and DEA Models

Abir Jendoubi () and Said Gattoufi ()
Additional contact information
Abir Jendoubi: Université de Tunis, Institut Supérieur de Gestion de Tunis, LaboratoireSMART LR11ES03
Said Gattoufi: Université de Tunis, Institut Supérieur de Gestion de Tunis, LaboratoireSMART LR11ES03

A chapter in Advanced Data Analytics, Machine Learning and AI in Business, 2026, pp 119-132 from Springer

Abstract: Abstract This study examines how portfolio selection has evolved toward intelligent, real-time decision-making by combining Artificial Intelligence (AI), Machine Learning (ML), and nonparametric models such as Data Envelopment Analysis (DEA). Although traditional portfolio theory and standalone AI techniques are well researched, few studies provide an integrated view linking these approaches or identifying gaps for next-generation portfolio optimization. To address this limitation, the study performs a large-scale bibliometric analysis of 749 peer-reviewed publications from 1991 to 2026 using the Bibliometrix package in R. The results show a sharp growth in research activity after 2012, peaking in 2024. Machine learning has become a dominant theme, appearing in 1173 of 1281 AI-related portfolio studies. Clustering analyses further highlight the strong relationships between ML methods such as LSTM, SVM, and genetic algorithms and financial tasks including prediction, risk evaluation, and asset allocation. The study’s novelty lies in synthesizing three decades of research to demonstrate how integrating ML with inverse DEA models can support real-time, data-driven, and adaptable portfolio decisions. Overall, it provides a roadmap for developing hybrid, interpretable, and scalable approaches that can enhance both academic research and practical portfolio management.

Keywords: Portfolio Selection; Artificial Intelligence; Machine Learning; Data Envelopment Analysis; Real-Time Optimization; Predictive Analytics (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:spr:lnopch:978-3-032-23493-3_7

Ordering information: This item can be ordered from
http://www.springer.com/9783032234933

DOI: 10.1007/978-3-032-23493-3_7

Access Statistics for this chapter

More chapters in Lecture Notes in Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().