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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM56739

A Review on Machine Learning and Deep Learning Models for Forecasting Stock Market Trends

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM56739

Volume:

10 |

2 |

Feb 2026

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM56739

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

SK

Shivani Kasliwal1

PPP

Prof. Pradeep Pal2

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Abstract

Machine learning is transforming stock market prediction by leveraging vast datasets, advanced algorithms, and computational power to provide more accurate forecasts. While challenges remain, continuous advancements in artificial intelligence and deep learning are improving predictive models, making them an essential tool for traders and investors. Stock market prediction extremely challenging due to the dependence of stock prices on several financial, socio-economic and political parameters etc. For real life applications utilizing stock market data, it is necessary to predict stock market data with low errors and high accuracy. This needs design of appropriate artificial intelligence (AI) and machine learning (ML) based techniques which can analyze large and complex data sets pertaining to stock markets and forecast future prices and trends in stock prices with relatively high accuracy. This paper presents a comprehensive review on the various techniques used in recent contemporary papers for stock market forecasting.

KEYWORDS

Time Series Models Stock Market Forecasting Artificial Intelligence Artificial Neural Networks Forecasting accuracy.

Cite This Article

Shivani Kasliwal1, Prof. Pradeep Pal2, "A Review on Machine Learning and Deep Learning Models for Forecasting Stock Market Trends," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 2, Feb 2026. https://doi.org/10.55041/IJSREM56739

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Publication Date

2/19/2026

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Double-Blind Peer Review

Review Rounds: 2

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