SIMULATION AND FINANCE: A BIBLIOMETRIC REVIEW
AUTHORS:
Mentor
Dr Richa Kalpesh Saxena
Affiliation
BBA, NMIMS, Anil Surendra Modi School of Commerce, Mumbai Campus, India
DOI: 
SHARE THIS ARTICLE
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
DOWNLOAD ARTICLE
REVIEW REPORT
CITE THIS ARTICLE
Abstract
This study is a systematic literature review and bibliometric analysis of simulation in financial research. Journal articles and reviews indexed in Scopus and published between 2011 and 2026 were retrieved and screened using the PRISMA framework, giving a final sample of 3,704 records. Performance analysis and science mapping were carried out in Bibliometrix and Biblioshiny, and keyword co-occurrence was mapped in VOSviewer. The 2026 literature alone contains 405 publications, and keyword co-occurrence analysis identifies five clusters, of which three are examined in depth: finance, decentralised finance and behavioural decision-making; learning systems, prediction and simulation; and investment, risk and climate-linked simulation. Across the dataset, annual output grew at 8.65%, which points to a maturing rather than an explosive field. China and the United States produce the largest share of country output, and only 29.14% of authorships are international. The reviewed studies show that simulation can improve financial decision-making and test complex systems under varied conditions, but they rely heavily on small datasets, self-built models and assumptions that are rarely tested on live markets. The main gaps are the distance between simulated success and empirical verification, the separation of research streams, and the limited presence of emerging economies such as India. The study closes with six recommendations for future research.
Keywords
Simulation; Finance; Bibliometric Analysis; Decentralised Finance; Machine Learning; Portfolio Optimisation; Risk Assessment; Green Finance; PRISMA; Biblioshiny
Article Information
PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 10
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Oct 24 2026
Article Metrics
Article Views
4
PDF Downloads
0
HOW TO CITE
Copy
Gupta, V., Kamalia, V., chokshi, V. & Chowdary, D. (2026). Simulation and Finance: A Bibliometric Review. International Journal of Science, Strategic Management and Technology, 02(10), 1-9. https://doi.org/10.55041/ijsmt.v2i10.087
Gupta, Vanshika, et al.. "Simulation and Finance: A Bibliometric Review." International Journal of Science, Strategic Management and Technology, vol. 02, no. 10, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijsmt.v2i10.087.
Gupta, Vanshika,Ved Kamalia,Vishwa chokshi, and Dhanunjay Chowdary. "Simulation and Finance: A Bibliometric Review." International Journal of Science, Strategic Management and Technology 02, no. 10 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijsmt.v2i10.087.
References
Bertucci, C., Bertucci, L., Delaunay, M. G., Guéant, O., & Lesbre, M. (2026). Agents' behavior and interest rate model optimization in DeFi lending. Mathematical Finance, 36(2), 374–396. https://doi.org/10.1111/mafi.70002
Calandra, F., Rossi, F. P., Fabris, F., & Bernardo, M. (2026). Learning from Terra-Luna: A simulation-based study on stabilizing algorithmic stablecoins. Blockchain: Research and Applications, 7(4). https://doi.org/10.1016/j.bcra.2025.100364
Chen, F., & Ding, C. (2026). GTH-Net: A dynamic game-theoretic hypernetwork for non-stationary financial time series forecasting. Applied Sciences, 16(7). https://doi.org/10.3390/app16073294
Chen, J., Liu, P., Lin, B., Zhou, H., & Papachristos, G. (2026). Optimizing green finance policies to promote the diffusion of prefabricated construction technology: An agent-based and exploratory scenario simulation. Engineering, Construction and Architectural Management, 1–21. https://doi.org/10.1108/ECAM-03-2025-0421
Firouzi, K. (2026). A tokenized sovereign debt conversion mechanism for dynamic public debt reduction. Statistics, Optimization and Information Computing, 16(1), 389–402. https://doi.org/10.19139/soic-2310-5070-3108
Huang, J., Liu, J., & Yang, W. (2026). Blockchain-based inventory pledge financing: An evolutionary game perspective. Kybernetes, 55(5), 1801–1827. https://doi.org/10.1108/K-10-2024-2736
Joseph, D., Joseph, A., James, B., & Dass, K. (2026). Behavioral biases as drivers of complexity in stock markets: An agent-based modeling approach. Northeast Journal of Complex Systems, 8(1). https://doi.org/10.63562/2577-8439.1143
Kato, H., Tahara, Y., Ohsuga, A., & Sei, Y. (2026). Agent-based analysis of systemic risk in Japanese financial institutions under ransomware attacks. IEICE Transactions on Information and Systems, E109.D(6), 714–725. https://doi.org/10.1587/transinf.2025ICP0001
Oyewola, D. O., Omotehinwa, T. O., Kehinde, T. O., & Hammawa, Y. (2026). IAOA-LSTM: A hybrid model for stock portfolio optimization. Computational Statistics, 41(2). https://doi.org/10.1007/s00180-025-01709-z
Reis, P., Serra, A. P., & Gama, J. (2026). A deep learning framework for forecasting medium-term covariance in multiasset portfolios. Journal of Forecasting, 45(4), 1797–1828. https://doi.org/10.1002/for.70110
Calandra, F., Rossi, F. P., Fabris, F., & Bernardo, M. (2026). Learning from Terra-Luna: A simulation-based study on stabilizing algorithmic stablecoins. Blockchain: Research and Applications, 7(4). https://doi.org/10.1016/j.bcra.2025.100364
Chen, F., & Ding, C. (2026). GTH-Net: A dynamic game-theoretic hypernetwork for non-stationary financial time series forecasting. Applied Sciences, 16(7). https://doi.org/10.3390/app16073294
Chen, J., Liu, P., Lin, B., Zhou, H., & Papachristos, G. (2026). Optimizing green finance policies to promote the diffusion of prefabricated construction technology: An agent-based and exploratory scenario simulation. Engineering, Construction and Architectural Management, 1–21. https://doi.org/10.1108/ECAM-03-2025-0421
Firouzi, K. (2026). A tokenized sovereign debt conversion mechanism for dynamic public debt reduction. Statistics, Optimization and Information Computing, 16(1), 389–402. https://doi.org/10.19139/soic-2310-5070-3108
Huang, J., Liu, J., & Yang, W. (2026). Blockchain-based inventory pledge financing: An evolutionary game perspective. Kybernetes, 55(5), 1801–1827. https://doi.org/10.1108/K-10-2024-2736
Joseph, D., Joseph, A., James, B., & Dass, K. (2026). Behavioral biases as drivers of complexity in stock markets: An agent-based modeling approach. Northeast Journal of Complex Systems, 8(1). https://doi.org/10.63562/2577-8439.1143
Kato, H., Tahara, Y., Ohsuga, A., & Sei, Y. (2026). Agent-based analysis of systemic risk in Japanese financial institutions under ransomware attacks. IEICE Transactions on Information and Systems, E109.D(6), 714–725. https://doi.org/10.1587/transinf.2025ICP0001
Oyewola, D. O., Omotehinwa, T. O., Kehinde, T. O., & Hammawa, Y. (2026). IAOA-LSTM: A hybrid model for stock portfolio optimization. Computational Statistics, 41(2). https://doi.org/10.1007/s00180-025-01709-z
Reis, P., Serra, A. P., & Gama, J. (2026). A deep learning framework for forecasting medium-term covariance in multiasset portfolios. Journal of Forecasting, 45(4), 1797–1828. https://doi.org/10.1002/for.70110
Ethics and Compliance
✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
Indexed In
Similar Articles
GRAPHENE
(2026)
DOI: 10.55041/ijsmt.v2i7.092
A Study of Working Capital Management:Risk Management & Strategic Recommendations
(2026)
DOI: 10.55041/ijsmt.v2i5.518
Omnichannel Integration: Enhancing Wellness Product Adoption Across Generations
(2026)
DOI: 10.55041/ijsmt.v2i4.440
Previous Article
