A Systematic Review of AI-Driven Stock Market Prediction Models Using Historical and Alternative Data Sources

• Author(s): Ramesh B; Sajida Tabasum U; Chaya R. M.; Sruthi K. C.
• Paper ID: 1719627
• Page: 555-506
• Published Date: 07-07-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 10 Issue 1 July-2026
• DOI: https://doi.org/10.64388/IREV10I1-1719627

Abstract

There are many systematic reviews on predicting stock. However, each reveals a different portion of the hybrid AI analysis and stock prediction puzzle. The principal objective of this research was to systematically review the existing systematic reviews on Artificial Intelligence (AI) models applied to stock market prediction to provide valuable inputs for the development of strategies in stock market investments. Keywords that would fall under the broad headings of AI and stock prediction were looked up in Scopus and Web of Science databases. We screened 69 titles and read 43 systematic reviews, including more than 379 studies, before retaining 10 for the final dataset. This work revealed that support vector machines (SVM), long short-term memory (LSTM), and artificial neural networks (ANN) are the most popular AI methods for stock market prediction. In addition, the time series of historical closing stock prices are the most commonly used data source, and accuracy is the most employed performance metric of the predictive models. We also identified several research gaps and directions for future studies. Specifically, we indicate that future research could benefit from exploring different data sources and combinations, while we also suggest comparing different AI methods and techniques, as each may have specific advantages and applicable scenarios. Lastly, we recommend better evaluating different prediction indicators and standards to reflect prediction models’ actual value and impact.

Keywords

Machine Learning, Deep Learning, Support Vector Machines (SVM), Long Short-Term Memory (LSTM), Neural Networks (NN)

Citations

IRE Journals:
Ramesh B, Sajida Tabasum U, Chaya R. M., Sruthi K. C. "A Systematic Review of AI-Driven Stock Market Prediction Models Using Historical and Alternative Data Sources" Iconic Research And Engineering Journals Volume 10 Issue 1 2026 Page 555-506 https://doi.org/10.64388/IREV10I1-1719627

IEEE:
Ramesh B, Sajida Tabasum U, Chaya R. M., Sruthi K. C. "A Systematic Review of AI-Driven Stock Market Prediction Models Using Historical and Alternative Data Sources" Iconic Research And Engineering Journals, vol. 10, no. 1, Jul. 2026, doi: https://doi.org/10.64388/IREV10I1-1719627

APA:
Ramesh B, Sajida Tabasum U, Chaya R. M., Sruthi K. C. (2026). A Systematic Review of AI-Driven Stock Market Prediction Models Using Historical and Alternative Data Sources. Iconic Research And Engineering Journals, 10(1). doi: https://doi.org/10.64388/IREV10I1-1719627

MLA:
Ramesh B, Sajida Tabasum U, Chaya R. M., Sruthi K. C. "A Systematic Review of AI-Driven Stock Market Prediction Models Using Historical and Alternative Data Sources" Iconic Research And Engineering Journals, vol. 10, no. 1, Jul. 2026. Crossref, https://doi.org/10.64388/IREV10I1-1719627

BibTeX

@article{1719627,
author = {Ramesh B, Sajida Tabasum U, Chaya R. M., Sruthi K. C.},
title = {A Systematic Review of AI-Driven Stock Market Prediction Models Using Historical and Alternative Data Sources},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {1},
pages = {555-506},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1719627.pdf},
abstract = {There are many systematic reviews on predicting stock. However, each reveals a different portion of the hybrid AI analysis and stock prediction puzzle. The principal objective of this research was to systematically review the existing systematic reviews on Artificial Intelligence (AI) models applied to stock market prediction to provide valuable inputs for the development of strategies in stock market investments. Keywords that would fall under the broad headings of AI and stock prediction were looked up in Scopus and Web of Science databases. We screened 69 titles and read 43 systematic reviews, including more than 379 studies, before retaining 10 for the final dataset. This work revealed that support vector machines (SVM), long short-term memory (LSTM), and artificial neural networks (ANN) are the most popular AI methods for stock market prediction. In addition, the time series of historical closing stock prices are the most commonly used data source, and accuracy is the most employed performance metric of the predictive models. We also identified several research gaps and directions for future studies. Specifically, we indicate that future research could benefit from exploring different data sources and combinations, while we also suggest comparing different AI methods and techniques, as each may have specific advantages and applicable scenarios. Lastly, we recommend better evaluating different prediction indicators and standards to reflect prediction models’ actual value and impact.},
keywords = {Machine Learning, Deep Learning, Support Vector Machines (SVM), Long Short-Term Memory (LSTM), Neural Networks (NN)},
month = {July}
}