Artificial Intelligence in Investment Decision Making and Risk Management
DOI:
https://doi.org/10.69996/jn49md22Keywords:
Artificial intelligence, investment, risk, risk management, decision makingAbstract
Detailed instructions for preparing your paper submitted to IJARI are given as follows. Please The expanding impact of AI on investment and risk management is the subject of this study. It provides a comprehensive literature review, highlighting the recent developments, challenges, and promising applications of AI in financial decision-making. The report draws upon various examples and studies to demonstrate the effectiveness and limitations of AI in this context
References
[1] P. Mikalef and M. Gupta, "Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational
creativity and firm performance," Information & Management, 58(3), 2021, 103434.
[2] M. T. Skully, "Australia: Islamic Finance Down Under," in The Islamic Finance Handbook, 2014, pp. 11–22.
[3] R. Kumar, N. Grover, R. Singh, S. Kathuria, A. Kumar, and A. Bansal, "Imperative Role of Artificial Intelligence and Big Data in Finance and Banking
Sector," in 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), 2023, 523–527.
[4] Z. Deng and M. Guo, "Research on the impact of the application of artificial intelligence technology on the sustainable development of mobile e-commerce," Benchmarking: An International Journal, vol. ahead-ofprint, no. ahead-of-print, 2023.
[5] S. Ahmed, M. M. Alshater, A. E. Ammari, and H. Hammami, "Artificial intelligence and machine learning in finance: A bibliometric review," Research in International Business and Finance, 61, 2022, 101646.
[6] M. El Hajj and J. Hammoud, "Unveiling the Influence of Artificial Intelligence and Machine Learning on Financial Markets: A Comprehensive Analysis of AI
Applications in Trading, Risk Management and Financial Operations," Journal of Risk and Financial Management, 16, 2023.
[7] M. Imran Harl, M. Saeed, M. H. Saeed, S. A. Bajri, A. Alburaikan and H. Abd El-Wahed Khalifa, "Development of an Investment Sector Selector Using a
TOPSIS Method Based on Novel Distances and Similarity Measures for Picture Fuzzy Hypersoft Sets," in IEEE Access, 12, 2024, 45118-45133.
[8] F. Xiong, N. Siddique, Z. Ali and S. Yin, "Conditional Aggregation Operator Defined by the Power Information Concerning Type-2 Fuzzy Deep Learning Algorithm for Financial Investment Data Decision-Making," in IEEE Access, 12, 2024, 96672-96690.
[9] T. Almulhim, "Interval-Valued Spherical Fuzzy Extension of DEMATEL and Its Application in EarlyStage Investment," in IEEE Access, 12, 2024, 89275- 89290.
[10]J. Yang and Y. Xiang, "Deep Transfer Learning Based Surrogate Modeling for Optimal Investment Decision of Distribution Networks," in IEEE Transactions on Power Systems, 39(2), 2024, 2506-2516.
[11]V. Simic, I. Gokasar, M. Deveci and M. Isik, "Fermatean Fuzzy Group Decision-Making Based CODAS Approach for Taxation of Public Transit Investments," in IEEE Transactions on Engineering Management, 70(12), 2023, 4233-4248.
[12]Y. Zhang, X. Chen, W. Pedrycz and Y. Dong, "Minimum Cost Consensus With Altruism Utility Constraints in Social Network Group Decision Making,"
in IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(8), 2023, 5032-5045.
[13][R. Thanseena Bai, R. Jeena and Aziya Shanavas, “Sustainable Finance And Use Of Artificial Intelligence In Investment Decision Making,” Int. J. of Adv. Res.,
2024, 1212-1218.
[14]Dr. Priyaka Khanna, “Evaluating the impact of artificial intelligence on investment decision: Making in Finance,” International Journal of Research in Finance and Management, 4(1), 22021, 78-84.
[15]R. Johnson and K.Lee, “Predictive Analytics in Equities Markets: A Comparative Study of AI and Traditional Models,” Journal of Investment Research, 25(4), 2020, 61-78.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 International Journal of Advance Research and Innovation(IJARI, 2347-3258)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Fringe Global Scientific Press publishes all the papers under a Creative Commons Attribution-Non-Commercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/) license. Authors have the liberty to replicate and distribute their work. Authors have the ability to use either the whole or a portion of their piece in compilations or other publications that include their own work. Please see the licensing terms for more information on reusing the work.