Journal of Primeasia
Integrative Disciplinary Research | Online ISSN 3064-9870 | Print ISSN 3069-4353
  1. You are here:
  2. Home
  3. Journals
  4. Biological Sciences
Journal of Primeasia

Volume 5 Number 1 2024

Article Contents

RESEARCH ARTICLE   (Open Access)
Contents Vol 5 (1)

The Role of AI and IT in Transforming Stock Price Analysis and Decision-Making Frameworks

Sonia Khan Papia1*, Fahim Rahman2, Sonia Nashid3, Al Akhir4, Anik Biswas5, Ariful Islam4

+ Author Affiliations

Journal of Primeasia 5 (1) 1-8 https://doi.org/10.25163/primeasia.5110376

Submitted: 08 January 2024 Revised: 14 October 2024  Published: 16 October 2024 

Abstract

Background: The methods of analyzing stock prices have typically relied on fundamental and technical methods, but the impact of Artificial Intelligence (AI) and Information Technology (IT) is reshaping the financial decision-making space. Investors are shifting toward AI-driven models and IT-enabled platforms due to the demand for ever-increasing predictive accuracy, real-time insight, and risk management.

Methods: A survey to examine the adopted and applied use of AI and IT for stock market analysis was developed and administered to 100 respondents including retail investors, financial analysts and developers of IT. Respondents received a structured questionnaire, and the data were analyzed with descriptive statistics.

Results: In respondents 72% indicated they use machine learning to predict stock prices, 65% of respondents use IT-enabled dashboards on the market, and 58% use predictive analytics. Close to 49% of respondents use natural language processing (NLP) to analyze sentiment, and 32% of respondents use block chains for its transparency. The main challenges that were reported were costs of implementation (41%), lack of technical implementation capability (38%), and data privacy concerns (29%).

Conclusion: AI and IT are obviously changing stock price analytics by improving predictive reliability, transparency, and informed decision making. Although challenging, the survey results provide definitive evidence that systematic change is taking place away from traditional decision making and toward technology enabled investment frameworks. There is likely to be a time in the near future, when AI (ML, NLP) will converge with IT (cloud computing, block chains, dashboards) in the financial markets.

Keywords: Artificial Intelligence, Information Technology, Stock Price Analysis, Decision-Making Frameworks, Predictive Analytics

References

Adus, S., Macklin, J., & Pinto, A. (2023). Exploring patient perspectives on how they can and should be engaged in the development of artificial intelligence (AI) applications in health care. BMC Health Services Research, 23(1). https://doi.org/10.1186/s12913-023-10098-2

Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A., Alrashed, M., Saleh, K. B., Badreldin, H. A., Yami, M. S. A., Harbi, S. A., & Albekairy, A. M. (2023). Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Medical Education, 23(1). https://doi.org/10.1186/s12909-023-04698-z

Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020). Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Medical Informatics and Decision Making, 20(1). https://doi.org/10.1186/s12911-020-01332-6

Anantrasirichai, N., & Bull, D. (2021). Artificial intelligence in the creative industries: a review. Artificial Intelligence Review, 55(1), 589–656. https://doi.org/10.1007/s10462-021-10039-7

Azeema, N., Nawaz, H., Gill, M. A., Khan, M. A., Miraj, J., & Lodhi, K. (2023, December 5). Impact of artificial intelligence on financial markets: Possibilities & challenges. https://www.jcbi.org/index.php/Main/article/view/304

Bawack, R. E., Wamba, S. F., Carillo, K. D. A., & Akter, S. (2022). Artificial intelligence in E-Commerce: a bibliometric study and literature review. Electronic Markets, 32(1), 297–338. https://doi.org/10.1007/s12525-022-00537-z

Buxmann, P., Hess, T., & Thatcher, J. B. (2020). AI-Based Information Systems. Business & Information Systems Engineering, 63(1), 1–4. https://doi.org/10.1007/s12599-020-00675-8

Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00411-8

Cubric, M. (2020). Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study. Technology in Society, 62, 101257. https://doi.org/10.1016/j.techsoc.2020.101257

Danese, P., & Kalchschmidt, M. (2010). The role of the forecasting process in improving forecast accuracy and operational performance. International Journal of Production Economics, 131(1), 204–214. https://doi.org/10.1016/j.ijpe.2010.09.006

Gomber, P., Koch, J., & Siering, M. (2017). Digital Finance and FinTech: current research and future research directions. Journal of Business Economics, 87(5), 537–580. https://doi.org/10.1007/s11573-017-0852-x

Gupta, S., Modgil, S., Bhattacharyya, S., & Bose, I. (2021). Artificial intelligence for decision support systems in the field of operations research: review and future scope of research. Annals of Operations Research, 308(1–2), 215–274. https://doi.org/10.1007/s10479-020-03856-6

Gupta, S., Modgil, S., Bhattacharyya, S., & Bose, I. (2021b). Artificial intelligence for decision support systems in the field of operations research: review and future scope of research. Annals of Operations Research, 308(1–2), 215–274. https://doi.org/10.1007/s10479-020-03856-6

Hangl, J., Krause, S., & Behrens, V. J. (2023). Drivers, barriers and social considerations for AI adoption in SCM. Technology in Society, 74, 102299. https://doi.org/10.1016/j.techsoc.2023.102299

Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018). Artificial intelligence in radiology. Nature Reviews. Cancer, 18(8), 500–510. https://doi.org/10.1038/s41568-018-0016-5

Jackson, I., & Ivanov, D. (2023). A beautiful shock? Exploring the impact of pandemic shocks on the accuracy of AI forecasting in the beauty care industry. Transportation Research Part E Logistics and Transportation Review, 180, 103360. https://doi.org/10.1016/j.tre.2023.103360

Jarrahi, M. H. (2018b). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007

Kaur, K., Kumar, Y., & Kaur, S. (2023). Artificial intelligence and machine learning in financial services to improve the business system. In Disruptive technologies and digital transformations for society 5.0 (pp. 3–30). https://doi.org/10.1007/978-981-99-5354-7_1

Lauriola, I., Lavelli, A., & Aiolli, F. (2021b). An introduction to Deep Learning in Natural Language Processing: Models, techniques, and tools. Neurocomputing, 470, 443–456. https://doi.org/10.1016/j.neucom.2021.05.103

Ledro, C., Nosella, A., & Pozza, I. D. (2023). Integration of AI in CRM: Challenges and guidelines. Journal of Open Innovation Technology Market and Complexity, 9(4), 100151. https://doi.org/10.1016/j.joitmc.2023.100151

Mahalakshmi, V., Kulkarni, N., Kumar, K. P., Kumar, K. S., Sree, D. N., & Durga, S. (2021). The Role of implementing Artificial Intelligence and Machine Learning Technologies in the financial services Industry for creating Competitive Intelligence. Materials Today Proceedings, 56, 2252–2255. https://doi.org/10.1016/j.matpr.2021.11.577

Milana, C., & Ashta, A. (2021). Artificial intelligence techniques in finance and financial markets: A survey of the literature. Strategic Change, 30(3), 189–209. https://doi.org/10.1002/jsc.2403

Milana, C., & Ashta, A. (2021b). Artificial intelligence techniques in finance and financial markets: A survey of the literature. Strategic Change, 30(3), 189–209. https://doi.org/10.1002/jsc.2403

Mokhtari, S., Yen, K. K., & Liu, J. (2021). Effectiveness of Artificial Intelligence in Stock Market Prediction based on Machine Learning. International Journal of Computer Applications, 183(7), 1–8. https://doi.org/10.5120/ijca2021921347

Nemati, H. R., Steiger, D. M., Iyer, L. S., & Herschel, R. T. (2002). Knowledge warehouse: an architectural integration of knowledge management, decision support, artificial intelligence and data warehousing. Decision Support Systems, 33(2), 143–161. https://doi.org/10.1016/s0167-9236(01)00141-5

Singh, A., & Weisse, B. A. (1998). Emerging stock markets, portfolio capital flows and long-term economie growth: Micro and macroeconomic perspectives. World Development, 26(4), 607–622. https://doi.org/10.1016/s0305-750x(98)00003-5

Spring, M., Faulconbridge, J., & Sarwar, A. (2022). How information technology automates and augments processes: Insights from Artificial-Intelligence-based systems in professional service operations. Journal of Operations Management, 68(6–7), 592–618. https://doi.org/10.1002/joom.1215

Vössing, M., Kühl, N., Lind, M., & Satzger, G. (2022). Designing transparency for effective Human-AI collaboration. Information Systems Frontiers, 24(3), 877–895. https://doi.org/10.1007/s10796-022-10284-3

Wamba, S. F., Guthrie, C., Queiroz, M. M., & Minner, S. (2023). ChatGPT and generative artificial intelligence: an exploratory study of key benefits and challenges in operations and supply chain management. International Journal of Production Research, 62(16), 5676–5696. https://doi.org/10.1080/00207543.2023.2294116

Weber, P., Carl, K. V., & Hinz, O. (2023). Applications of Explainable Artificial Intelligence in Finance—a systematic review of Finance, Information Systems, and Computer Science literature. Management Review Quarterly, 74(2), 867–907. https://doi.org/10.1007/s11301-023-00320-0


View Dimensions


View Plumx


View Altmetric



0
Save
0
Citation
120
View
0
Share

Stay connected