How to cite this paper
Shabani, M., Ghousi, R & Mohammadi, E. (2025). Exploring the evolution of scientific publication on portfolio optimization in the light of artificial intelligence: A bibliometric study.Accounting, 11(1), 71-90.
Refrences
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Ban, G.-Y., El Karoui, N., & Lim, A. E. B. (2018). Machine Learning and Portfolio Optimization. Management Science, 64(3), 1136–1154. https://doi.org/10.1287/mnsc.2016.2644
Bornmann, L., & Daniel, H. (2007). What do we know about the h index? Journal of the American Society for Information Science and Technology, 58(9), 1381–1385. https://doi.org/10.1002/asi.20609
BROOKES, B. C. (1969). Bradford’s Law and the Bibliography of Science. Nature, 224(5223), 953–956. https://doi.org/10.1038/224953a0
Castilho, D., Gama, J., Mundim, L. R., & de Carvalho, A. C. P. L. F. (2019). Improving Portfolio Optimization Using Weighted Link Prediction in Dynamic Stock Networks (pp. 340–353). https://doi.org/10.1007/978-3-030-22744-9_27
Cavalcante, R. C., Brasileiro, R. C., Souza, V. L. F., Nobrega, J. P., & Oliveira, A. L. I. (2016). Computational Intelligence and Financial Markets: A Survey and Future Directions. Expert Systems with Applications, 55, 194–211. https://doi.org/10.1016/j.eswa.2016.02.006
Egghe, L. (2006). Theory and practise of the g-index. Scientometrics, 69(1), 131–152. https://doi.org/10.1007/s11192-006-0144-7
Fernández, A., & Gómez, S. (2007). Portfolio selection using neural networks. Computers & Operations Research, 34(4), 1177–1191. https://doi.org/10.1016/j.cor.2005.06.017
GARFIELD, E. (1970). Citation Indexing for Studying Science. Nature, 227(5259), 669–671. https://doi.org/10.1038/227669a0
Goodell, J. W., Kumar, S., Lim, W. M., & Pattnaik, D. (2021a). Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance, 32, 100577. https://doi.org/10.1016/j.jbef.2021.100577
Helmbold, D. P., Schapire, R. E., Singer, Y., & Warmuth, M. K. (1998). On‐Line Portfolio Selection Using Multiplicative Updates. Mathematical Finance, 8(4), 325–347. https://doi.org/10.1111/1467-9965.00058
Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569–16572. https://doi.org/10.1073/pnas.0507655102
Hu, Y.-J., & Lin, S.-J. (2019). Deep Reinforcement Learning for Optimizing Finance Portfolio Management. 2019 Amity International Conference on Artificial Intelligence (AICAI), 14–20. https://doi.org/10.1109/AICAI.2019.8701368
Javadi, R., Ghanbari, H., & Seiti, H. (2025). A bibliometric analysis on supply chain disruptions: Current status, development, and future directions. Journal of Future Sustainability, 107–126. https://doi.org/10.5267/j.jfs.2025.4.002
Li, B., & Hoi, S. C. H. (2014). Online portfolio selection. ACM Computing Surveys, 46(3), 1–36. https://doi.org/10.1145/2512962
Li, R., Liu, B., Yuan, X., & Chen, Z. (2023). A bibliometric analysis of research on R-loop: Landscapes, highlights and trending topics. DNA Repair, 127, 103502. https://doi.org/10.1016/j.dnarep.2023.103502
Ma, Y., Han, R., & Wang, W. (2020). Prediction-Based Portfolio Optimization Models Using Deep Neural Networks. IEEE Access, 8, 115393–115405. https://doi.org/10.1109/ACCESS.2020.3003819
Ma, Y., Han, R., & Wang, W. (2021). Portfolio optimization with return prediction using deep learning and machine learning. Expert Systems with Applications, 165, 113973. https://doi.org/10.1016/j.eswa.2020.113973
Manogna, R. L., & Anand, A. (2023). A bibliometric analysis on the application of deep learning in finance: status, development and future directions. Kybernetes. https://doi.org/10.1108/K-04-2023-0637
Moral-Munoz, J. A., Arroyo-Morales, M., Herrera-Viedma, E., & Cobo, M. J. (2018). An Overview of Thematic Evolution of Physical Therapy Research Area From 1951 to 2013. Frontiers in Research Metrics and Analytics, 3. https://doi.org/10.3389/frma.2018.00013
Pao, M. L. (1985). Lotka’s law: A testing procedure. Information Processing & Management, 21(4), 305–320. https://doi.org/10.1016/0306-4573(85)90055-X
Ban, G.-Y., El Karoui, N., & Lim, A. E. B. (2018). Machine Learning and Portfolio Optimization. Management Science, 64(3), 1136–1154. https://doi.org/10.1287/mnsc.2016.2644
Bornmann, L., & Daniel, H. (2007). What do we know about the h index? Journal of the American Society for Information Science and Technology, 58(9), 1381–1385. https://doi.org/10.1002/asi.20609
BROOKES, B. C. (1969). Bradford’s Law and the Bibliography of Science. Nature, 224(5223), 953–956. https://doi.org/10.1038/224953a0
Castilho, D., Gama, J., Mundim, L. R., & de Carvalho, A. C. P. L. F. (2019). Improving Portfolio Optimization Using Weighted Link Prediction in Dynamic Stock Networks (pp. 340–353). https://doi.org/10.1007/978-3-030-22744-9_27
Cavalcante, R. C., Brasileiro, R. C., Souza, V. L. F., Nobrega, J. P., & Oliveira, A. L. I. (2016). Computational Intelligence and Financial Markets: A Survey and Future Directions. Expert Systems with Applications, 55, 194–211. https://doi.org/10.1016/j.eswa.2016.02.006
Egghe, L. (2006). Theory and practise of the g-index. Scientometrics, 69(1), 131–152. https://doi.org/10.1007/s11192-006-0144-7
Fernández, A., & Gómez, S. (2007). Portfolio selection using neural networks. Computers & Operations Research, 34(4), 1177–1191. https://doi.org/10.1016/j.cor.2005.06.017
GARFIELD, E. (1970). Citation Indexing for Studying Science. Nature, 227(5259), 669–671. https://doi.org/10.1038/227669a0
Goodell, J. W., Kumar, S., Lim, W. M., & Pattnaik, D. (2021a). Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance, 32, 100577. https://doi.org/10.1016/j.jbef.2021.100577
Helmbold, D. P., Schapire, R. E., Singer, Y., & Warmuth, M. K. (1998). On‐Line Portfolio Selection Using Multiplicative Updates. Mathematical Finance, 8(4), 325–347. https://doi.org/10.1111/1467-9965.00058
Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569–16572. https://doi.org/10.1073/pnas.0507655102
Hu, Y.-J., & Lin, S.-J. (2019). Deep Reinforcement Learning for Optimizing Finance Portfolio Management. 2019 Amity International Conference on Artificial Intelligence (AICAI), 14–20. https://doi.org/10.1109/AICAI.2019.8701368
Javadi, R., Ghanbari, H., & Seiti, H. (2025). A bibliometric analysis on supply chain disruptions: Current status, development, and future directions. Journal of Future Sustainability, 107–126. https://doi.org/10.5267/j.jfs.2025.4.002
Li, B., & Hoi, S. C. H. (2014). Online portfolio selection. ACM Computing Surveys, 46(3), 1–36. https://doi.org/10.1145/2512962
Li, R., Liu, B., Yuan, X., & Chen, Z. (2023). A bibliometric analysis of research on R-loop: Landscapes, highlights and trending topics. DNA Repair, 127, 103502. https://doi.org/10.1016/j.dnarep.2023.103502
Ma, Y., Han, R., & Wang, W. (2020). Prediction-Based Portfolio Optimization Models Using Deep Neural Networks. IEEE Access, 8, 115393–115405. https://doi.org/10.1109/ACCESS.2020.3003819
Ma, Y., Han, R., & Wang, W. (2021). Portfolio optimization with return prediction using deep learning and machine learning. Expert Systems with Applications, 165, 113973. https://doi.org/10.1016/j.eswa.2020.113973
Manogna, R. L., & Anand, A. (2023). A bibliometric analysis on the application of deep learning in finance: status, development and future directions. Kybernetes. https://doi.org/10.1108/K-04-2023-0637
Moral-Munoz, J. A., Arroyo-Morales, M., Herrera-Viedma, E., & Cobo, M. J. (2018). An Overview of Thematic Evolution of Physical Therapy Research Area From 1951 to 2013. Frontiers in Research Metrics and Analytics, 3. https://doi.org/10.3389/frma.2018.00013
Pao, M. L. (1985). Lotka’s law: A testing procedure. Information Processing & Management, 21(4), 305–320. https://doi.org/10.1016/0306-4573(85)90055-X