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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM32272

Survey on Stock Market Prediction using Deep Learning

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM32272

Volume:

8 |

5 |

May 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM32272

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

P

Prof.V.M.Sardeshamukh

SHG

Saurabh Hanumant Gund

SRN

Santhipriyan Ramchandran Nair

PM

Prathamesh Mhalsekar

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Abstract

The share market is a very unpredictable entity that is extremely sensitive to different sorts of criteria. Substantial research has been done in the field of financial markets projection to discover the many characteristics and elements connected with price volatility. The routine movements of the financial markets is exceedingly turbulent and dynamic. It is impacted by a large number of underlying elements and intricate interactions, resulting in a substantial percentage of non linearity. According to certain beliefs on the behavior of the financial markets, stock exchange progression is entirely unpredictable. Therefore, to achieve effective prediction of the stock markets through the use of machine learning approaches a number of related works have been analyzed. This analysis has allowed for the development of a useful and highly effective methodology that will be discussed in the upcoming editions of this research article.

KEYWORDS

K Nearest Neighbors Long Short Term Memory Stock Market Prediction.

Cite This Article

Prof.V.M.Sardeshamukh, Saurabh Hanumant Gund, Santhipriyan Ramchandran Nair, Prathamesh Mhalsekar, "Survey on Stock Market Prediction using Deep Learning," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 5, May 2024. https://doi.org/10.55041/IJSREM32272

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Publication Date

12/31/45421

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Double-Blind Peer Review

Review Rounds: 2

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