From Models to Markets: How Generative AI is Reshaping Investment Research

Authors

  • Abhishek Upadhyay, Subhankar Panda, Harshini Gadam

Keywords:

Generative AI, Investment Research, Financial Markets, Predictive Analysis, Portfolio Management

Abstract

The incorporation of generative artificial intelligence (AI) into the field of investment research is causing a revolution in the study and comprehension of financial markets. Traditional models, which relied mostly on historical data and static assumptions, are being supplemented and, in some cases, replaced by dynamic artificial intelligence systems that are able to generate insights from big datasets that contain a wide variety of categories. Through the utilisation of generative artificial intelligence technologies, analysts have the ability to adopt a more all-encompassing and adaptable approach to investment strategy. These technologies have the capability to construct real-time financial narratives, imitate market conditions, and enhance the accuracy of predictions. This technology change is not only accelerating research processes but also fostering innovations in portfolio management and risk assessment. These innovations are being driven by the transition. Despite the fact that generative artificial intelligence opens up exciting new opportunities for the financial sector, it also faces significant challenges in terms of transparency, interpretability, and ethical application.

DOI: https://doi.org/10.17762/ijisae.v12i22s.7597

Downloads

Download data is not yet available.

References

Krauss, C., Do, X. A., & Huck, N. (2022). Financial forecasting with deep learning: A systematic literature review. Expert Systems with Applications, 200, 117045. https://doi.org/10.1016/j.eswa.2022.117045

Singh, R., & Patel, S. (2023). Generative AI in financial services: Challenges and opportunities. Journal of Financial Innovation and Technology, 6(1), 45–61. https://doi.org/10.1016/j.jfit.2023.01.005

Bhatia, A., & Jain, M. (2023). Transforming financial analytics: The impact of generative language models on investment research. Finance and AI Review, 11(2), 88–102.

IBM Institute for Business Value. (2023). Generative AI in financial services: Beyond the hype. IBM Research. https://www.ibm.com/thought-leadership/institute-business-value/report/generative-ai-finance

PwC. (2023). The economic potential of generative AI: Financial services in focus. PricewaterhouseCoopers. https://www.pwc.com/generative-ai-financialservices

Chowdhury, F., & Nakamura, Y. (2022). Ethical implications of AI in algorithmic trading. AI & Ethics, 2(4), 345–357. https://doi.org/10.1007/s43681-022-00113-y

JPMorgan Chase & Co. (2023). AI-powered investing: Real-time insights and the next frontier of asset management. JPMorgan Research Report.

Cheng Xuejun. Artificial intelligence is deeply involved in consumer finance: motivations, risks and prevention and control [J]. "Journal of Shenzhen University" (Humanities and Social Sciences Edition), 2021, 38(3): 67-76.

Ferreira FGDC, Gandomi AH, Cardoso RT N. Artificial intelligence applied to stock market trading: a review[J]. IEEE Access, 2021, 9: 30898-30917.

Goyal, Mahesh Kumar. "Synthetic Data Revolutionizes Rare Disease Research: How Large Language Models and Generative AI are Overcoming Data Scarcity and Privacy Challenges."

Lee J. Access to finance for artificial intelligence regulation in the financial services industry[ J]. European Business Organization Law Review, 2020, 21(4): 731-757.

Goyal, Mahesh Kumar. "Real-Time Supply Chain Resilience: Predictive Analytics for Global Food Security and Perishable Goods."

Liu, Bo, et al. "Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms." arXiv preprint arXiv:2312.12872 (2023).

Che, Chang, et al. "Deep learning for precise robot position prediction in logistics." Journal of Theory and Practice of Engineering Science 3.10 (2023): 36-41.

Hu, Hao, et al. "Casting product image data for quality inspection with xception and data augmentation." Journal of Theory and Practice of Engineering Science 3.10 (2023): 42-46

Che, Chang, et al. "Advancing Cancer Document Classification with R andom Forest." Academic Journal of Science and Technology 8.1 (2023): 278-280.

Downloads

Published

15.08.2024

How to Cite

Abhishek Upadhyay. (2024). From Models to Markets: How Generative AI is Reshaping Investment Research. International Journal of Intelligent Systems and Applications in Engineering, 12(22s), 2140 –. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/7597

Issue

Vol. 12 No. 22s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.