Connecting AI into Investment Strategies: An Empirical Study

Satarupa Misra; Indumathi Nagesh; Rama Narayanswami; Surabhi Jha

doi:doi:10.11648/j.sjbm.20261403.18

Research Article |

| Peer-Reviewed

Connecting AI into Investment Strategies: An Empirical Study

Satarupa Misra^*

, Indumathi Nagesh

, Rama Narayanswami

, Surabhi Jha

Published in Science Journal of Business and Management (Volume 14, Issue 3)

Received: 29 July 2026 Accepted: 31 August 2026 Published: 27 September 2026

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Due to its potential to revolutionize investment strategies and enhance decision-making, Artificial Intelligence (AI) has gained significant attention in the financial sector. AI technologies such as robo-advisors, algorithmic trading systems, and predictive analytics enable investors to process and analyse large volumes of market data, identify complex patterns, and generate more accurate and timely investment decisions. AI’s advantages, including its capacity to handle non-linear relationships, respond dynamically to new information, and provide personalized recommendations based on individual investor profiles. However, issues such as data privacy concerns, lack of transparency in complex AI models, potential algorithmic bias are still remain the key concern. The research focuses on understanding the adoption of AI tools in the context of personal finance and investment management. This study investigates the role of AI tools in shaping modern investment strategies by comparing AI driven approaches with traditional investment methods. The comparative analysis between AI-driven and traditional investment strategies are highlighted in this study. AI’s ability to analyse vast datasets, detect hidden patterns, and forecast market trends with greater accuracy than conventional methods is a key strength that this study investigates. The ethical implications of relying heavily on AI for financial decisions also form an important part of the analysis, as ensuring fairness, accountability, and trust in AI tools is essential for widespread adoption. This study provides valuable insights for investors, financial advisors, fintech developers, and policymakers on how AI tools can be optimized to support sound investment decisions. The findings suggest that a balanced approach that leverages both AI capabilities and human expertise is crucial for achieving robust, ethical, and effective investment strategies in today’s fast-evolving financial markets.

Published in	Science Journal of Business and Management (Volume 14, Issue 3)
DOI	10.11648/j.sjbm.20261403.18
Page(s)	114-122
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Investment Strategy, Traditional Investment, AI Tools

References

[1]	Caspari, R. (2022). Machine learning models predicting returns: Why most popular performance metrics are misleading and proposal for an efficient metric. Expert Systems with Applications, 199, 116970. https://doi.org/10.1016/j.eswa.2022.116970
[2]	Ferreira, F. G. D. C., Gandomi, A. H., & Cardoso, R. T. N. (2021). Artificial Intelligence Applied to Stock Market Trading: A Review. IEEE Access, 9, 30898–30917. https://doi.org/10.1109/ACCESS.2021.3058133
[3]	Giudici, P., & Raffinetti, E. (2023). SAFE Artificial Intelligence in finance. Finance Research Letters, 56, 104088 https://doi.org/10.1016/j.frl.2023.104088
[4]	Grudniewicz, J., & Ślepaczuk, R. (2023). Application of machine learning in algorithmic investment strategies on global stock markets. Research in International Business and Finance. https://doi.org/10.1016/j.ribaf.2023.102052
[5]	Roscoe, A. M., Lang, D., & Sheth, J. N. (1975). Follow-up Methods, Questionnaire Length, and Market Differences in Mail Surveys: In this experimental test, a telephone reminderproduced the best response rate and questionnaire length had no effect on rate of return. Journal of Marketing, 39(2), 20-27.
[6]	Khattak, B. H. A., Shafi, I., Khan, A. S., Flores, E. S., Lara, R. G., Samad, Md. A., & Ashraf, I. (n.d.). A Systematic Survey of AI Models in Financial Market Forecasting for Profitability Analysis. IEEE Access. https://doi.org/10.1109/access.2023.3330156
[7]	Olubusola, O., Mhlongo, N. Z., Daraojimba, D. O., Ajayi-Nifise, A. O., & Falaiye, T. (2024). Machine learning in financial forecasting: A US review: Exploring the advancements, challenges, and implications of AI-driven predictions in financial markets. World Journal of Advanced Research and Reviews, 21(2), 1969-1984. https://doi.org/10.30574/wjarr.2024.21.2.0442
[8]	Sharma, S., Islam, N., Singh, G., & Dhir, A. (2022). Why Do Retail Customers Adopt Artificial Intelligence (AI) Based Autonomous Decision-Making Systems? IEEE Transactions on Engineering Management, PP, 1–16. https://doi.org/10.1109/tem.2022.3157976
[9]	Singh, V., Chen, S.-S., Singhania, M., Nanavati, B., Kar, A. K., & Gupta, A. (2022). How are reinforcement learning and deep learning algorithms used for big data based decision making in financial industries-A review and research agenda. International Journal of Information Management Data Insights, 2(2), 100094. https://doi.org/10.1016/j.jjimei.2022.100094
[10]	Tabachnick, B. G., & Fidell, L. S. (2007). Experimental designs using ANOVA (Vol. 724). Belmont, CA: Thomson/Brooks/Cole.
[11]	Tabachnick, B. G., & Fidell, L. S. (1989). Using Multivariate Statistics. New York: Harper & Row, Publishers, Inc.

Cite This Article

Plain Text BibTeX RIS

APA Style

Misra, S., Nagesh, I., Narayanswami, R., Jha, S. (2026). Connecting AI into Investment Strategies: An Empirical Study. Science Journal of Business and Management, 14(3), 114-122. https://doi.org/10.11648/j.sjbm.20261403.18

Copy | Download

ACS Style

Misra, S.; Nagesh, I.; Narayanswami, R.; Jha, S. Connecting AI into Investment Strategies: An Empirical Study. Sci. J. Bus. Manag. 2026, 14(3), 114-122. doi: 10.11648/j.sjbm.20261403.18

Copy | Download

AMA Style

Misra S, Nagesh I, Narayanswami R, Jha S. Connecting AI into Investment Strategies: An Empirical Study. Sci J Bus Manag. 2026;14(3):114-122. doi: 10.11648/j.sjbm.20261403.18

Copy | Download

@article{10.11648/j.sjbm.20261403.18,
  author = {Satarupa Misra and Indumathi Nagesh and Rama Narayanswami and Surabhi Jha},
  title = {Connecting AI into Investment Strategies: An Empirical Study},
  journal = {Science Journal of Business and Management},
  volume = {14},
  number = {3},
  pages = {114-122},
  doi = {10.11648/j.sjbm.20261403.18},
  url = {https://doi.org/10.11648/j.sjbm.20261403.18},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjbm.20261403.18},
  abstract = {Due to its potential to revolutionize investment strategies and enhance decision-making, Artificial Intelligence (AI) has gained significant attention in the financial sector. AI technologies such as robo-advisors, algorithmic trading systems, and predictive analytics enable investors to process and analyse large volumes of market data, identify complex patterns, and generate more accurate and timely investment decisions. AI’s advantages, including its capacity to handle non-linear relationships, respond dynamically to new information, and provide personalized recommendations based on individual investor profiles. However, issues such as data privacy concerns, lack of transparency in complex AI models, potential algorithmic bias are still remain the key concern. The research focuses on understanding the adoption of AI tools in the context of personal finance and investment management. This study investigates the role of AI tools in shaping modern investment strategies by comparing AI driven approaches with traditional investment methods. The comparative analysis between AI-driven and traditional investment strategies are highlighted in this study. AI’s ability to analyse vast datasets, detect hidden patterns, and forecast market trends with greater accuracy than conventional methods is a key strength that this study investigates. The ethical implications of relying heavily on AI for financial decisions also form an important part of the analysis, as ensuring fairness, accountability, and trust in AI tools is essential for widespread adoption. This study provides valuable insights for investors, financial advisors, fintech developers, and policymakers on how AI tools can be optimized to support sound investment decisions. The findings suggest that a balanced approach that leverages both AI capabilities and human expertise is crucial for achieving robust, ethical, and effective investment strategies in today’s fast-evolving financial markets.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - Connecting AI into Investment Strategies: An Empirical Study
AU  - Satarupa Misra
AU  - Indumathi Nagesh
AU  - Rama Narayanswami
AU  - Surabhi Jha
Y1  - 2026/09/27
PY  - 2026
N1  - https://doi.org/10.11648/j.sjbm.20261403.18
DO  - 10.11648/j.sjbm.20261403.18
T2  - Science Journal of Business and Management
JF  - Science Journal of Business and Management
JO  - Science Journal of Business and Management
SP  - 114
EP  - 122
PB  - Science Publishing Group
SN  - 2331-0634
UR  - https://doi.org/10.11648/j.sjbm.20261403.18
AB  - Due to its potential to revolutionize investment strategies and enhance decision-making, Artificial Intelligence (AI) has gained significant attention in the financial sector. AI technologies such as robo-advisors, algorithmic trading systems, and predictive analytics enable investors to process and analyse large volumes of market data, identify complex patterns, and generate more accurate and timely investment decisions. AI’s advantages, including its capacity to handle non-linear relationships, respond dynamically to new information, and provide personalized recommendations based on individual investor profiles. However, issues such as data privacy concerns, lack of transparency in complex AI models, potential algorithmic bias are still remain the key concern. The research focuses on understanding the adoption of AI tools in the context of personal finance and investment management. This study investigates the role of AI tools in shaping modern investment strategies by comparing AI driven approaches with traditional investment methods. The comparative analysis between AI-driven and traditional investment strategies are highlighted in this study. AI’s ability to analyse vast datasets, detect hidden patterns, and forecast market trends with greater accuracy than conventional methods is a key strength that this study investigates. The ethical implications of relying heavily on AI for financial decisions also form an important part of the analysis, as ensuring fairness, accountability, and trust in AI tools is essential for widespread adoption. This study provides valuable insights for investors, financial advisors, fintech developers, and policymakers on how AI tools can be optimized to support sound investment decisions. The findings suggest that a balanced approach that leverages both AI capabilities and human expertise is crucial for achieving robust, ethical, and effective investment strategies in today’s fast-evolving financial markets.
VL  - 14
IS  - 3
ER  -

Copy | Download

Author Information

Satarupa Misra

MBA Department, Presidency College, Bengaluru, India

Contact Email

http://orcid.org/0000-0001-9429-8597
Indumathi Nagesh

MBA Department, Presidency College, Bengaluru, India

Contact Email

http://orcid.org/0000-0002-4971-0383
Rama Narayanswami

MBA Department, Presidency College, Bengaluru, India

Contact Email

http://orcid.org/0009-0007-7506-151X
Surabhi Jha

Department of Management, Dr. NSAM First Grade College, Nitte (Deemed to be University), Bengaluru, India

Contact Email

http://orcid.org/0009-0007-1202-2919

Download PDF

Submit an Article

Sections

Plain Text BibTeX RIS

APA Style

Misra, S., Nagesh, I., Narayanswami, R., Jha, S. (2026). Connecting AI into Investment Strategies: An Empirical Study. Science Journal of Business and Management, 14(3), 114-122. https://doi.org/10.11648/j.sjbm.20261403.18

Copy | Download

ACS Style

Misra, S.; Nagesh, I.; Narayanswami, R.; Jha, S. Connecting AI into Investment Strategies: An Empirical Study. Sci. J. Bus. Manag. 2026, 14(3), 114-122. doi: 10.11648/j.sjbm.20261403.18

Copy | Download

AMA Style

Misra S, Nagesh I, Narayanswami R, Jha S. Connecting AI into Investment Strategies: An Empirical Study. Sci J Bus Manag. 2026;14(3):114-122. doi: 10.11648/j.sjbm.20261403.18

Copy | Download

@article{10.11648/j.sjbm.20261403.18,
  author = {Satarupa Misra and Indumathi Nagesh and Rama Narayanswami and Surabhi Jha},
  title = {Connecting AI into Investment Strategies: An Empirical Study},
  journal = {Science Journal of Business and Management},
  volume = {14},
  number = {3},
  pages = {114-122},
  doi = {10.11648/j.sjbm.20261403.18},
  url = {https://doi.org/10.11648/j.sjbm.20261403.18},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjbm.20261403.18},
  abstract = {Due to its potential to revolutionize investment strategies and enhance decision-making, Artificial Intelligence (AI) has gained significant attention in the financial sector. AI technologies such as robo-advisors, algorithmic trading systems, and predictive analytics enable investors to process and analyse large volumes of market data, identify complex patterns, and generate more accurate and timely investment decisions. AI’s advantages, including its capacity to handle non-linear relationships, respond dynamically to new information, and provide personalized recommendations based on individual investor profiles. However, issues such as data privacy concerns, lack of transparency in complex AI models, potential algorithmic bias are still remain the key concern. The research focuses on understanding the adoption of AI tools in the context of personal finance and investment management. This study investigates the role of AI tools in shaping modern investment strategies by comparing AI driven approaches with traditional investment methods. The comparative analysis between AI-driven and traditional investment strategies are highlighted in this study. AI’s ability to analyse vast datasets, detect hidden patterns, and forecast market trends with greater accuracy than conventional methods is a key strength that this study investigates. The ethical implications of relying heavily on AI for financial decisions also form an important part of the analysis, as ensuring fairness, accountability, and trust in AI tools is essential for widespread adoption. This study provides valuable insights for investors, financial advisors, fintech developers, and policymakers on how AI tools can be optimized to support sound investment decisions. The findings suggest that a balanced approach that leverages both AI capabilities and human expertise is crucial for achieving robust, ethical, and effective investment strategies in today’s fast-evolving financial markets.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - Connecting AI into Investment Strategies: An Empirical Study
AU  - Satarupa Misra
AU  - Indumathi Nagesh
AU  - Rama Narayanswami
AU  - Surabhi Jha
Y1  - 2026/09/27
PY  - 2026
N1  - https://doi.org/10.11648/j.sjbm.20261403.18
DO  - 10.11648/j.sjbm.20261403.18
T2  - Science Journal of Business and Management
JF  - Science Journal of Business and Management
JO  - Science Journal of Business and Management
SP  - 114
EP  - 122
PB  - Science Publishing Group
SN  - 2331-0634
UR  - https://doi.org/10.11648/j.sjbm.20261403.18
AB  - Due to its potential to revolutionize investment strategies and enhance decision-making, Artificial Intelligence (AI) has gained significant attention in the financial sector. AI technologies such as robo-advisors, algorithmic trading systems, and predictive analytics enable investors to process and analyse large volumes of market data, identify complex patterns, and generate more accurate and timely investment decisions. AI’s advantages, including its capacity to handle non-linear relationships, respond dynamically to new information, and provide personalized recommendations based on individual investor profiles. However, issues such as data privacy concerns, lack of transparency in complex AI models, potential algorithmic bias are still remain the key concern. The research focuses on understanding the adoption of AI tools in the context of personal finance and investment management. This study investigates the role of AI tools in shaping modern investment strategies by comparing AI driven approaches with traditional investment methods. The comparative analysis between AI-driven and traditional investment strategies are highlighted in this study. AI’s ability to analyse vast datasets, detect hidden patterns, and forecast market trends with greater accuracy than conventional methods is a key strength that this study investigates. The ethical implications of relying heavily on AI for financial decisions also form an important part of the analysis, as ensuring fairness, accountability, and trust in AI tools is essential for widespread adoption. This study provides valuable insights for investors, financial advisors, fintech developers, and policymakers on how AI tools can be optimized to support sound investment decisions. The findings suggest that a balanced approach that leverages both AI capabilities and human expertise is crucial for achieving robust, ethical, and effective investment strategies in today’s fast-evolving financial markets.
VL  - 14
IS  - 3
ER  -

Copy | Download