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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM32316

Novel Deep Learning Approach for Predicting Stock Market Trends

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM32316

Volume:

8 |

5 |

May 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM32316

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

AST

Akshada Subhash Tamboli

AMP

Anshuman Manoj Parmar

KNV

Kashyap Nileshbhai Visavadia

SSP

Sagar Shailesh Parmar

LK

Lukesh Kadu

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Abstract

Stock Market Prediction a Major Achievement in financial markets is precisely forecasting stock market trends, which remains a considerable hurdle. Deep learning, a subset of machine learning, has grown to be a potent tool for untangling complex relationships within data. What distinguishes deep learning is its capacity to handle large amounts of data, unwrinkle difficult characteristics, and adapt to dynamic structures, all while unearthing concealed truths. Deep learning, at its core, is predicated on artificial neural networks that simulate human brain learning and pattern recognition capabilities, making it ideal for analyzing financial data. Through novel profound neural systems, the strategy applies a varied collection of the distributed showcase information, including, but not limited to, authentic stock costs, trading volumes, and calculations extracted from news sources.

KEYWORDS

stock advertise patterns back profound learning designs authentic information design acknowledgment sentiments.

Cite This Article

Akshada Subhash Tamboli, Anshuman Manoj Parmar, Kashyap Nileshbhai Visavadia, Sagar Shailesh Parmar, Lukesh Kadu, "Novel Deep Learning Approach for Predicting Stock Market Trends," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 5, May 2024. https://doi.org/10.55041/IJSREM32316

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Publication Date

12/31/45415

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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