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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM17284

JPX Japan Stock Exchange Prediction

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM17284

Volume:

6 |

12 |

Dec 2022

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

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

ZM

Zarinabegam Mundargi

CR

Chetanya Rathi

HD

Harsh Dhabekar

HM

Harshit Mundhra

PH

Pushkar Helge

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Abstract

It is crucial to make an extremely accurate forecast of a future trend in a market that is financially volatile, such as the stock market. In the context of the economic crisis and the need to make money, a trustworthy estimate of stock values is crucial. Advanced machine learning methods are needed to predict another model. Thousands of people make stock market investments every business day. Some of these investors experience financial gains or losses. On any given trading day, the gain or loss is unexpected. Stock market research is required because of the high demand for stock price forecasts. Effective forecasting systems help traders in an indirect way by providing useful information like price position in the future. In this study, a stacked LSTM-based system for stock forecasting is built. Finding sound investments is a requirement for success in any financial sector. These financial decisions were done manually by experts, technology has opened up new possibilities for retail investors. Numerous quantitative trading initiatives are currently being employed to study financial markets and develop investment plans. Such a technique needs real-time and historical data, which is difficult to come by, especially for individual investors. We have considered both of these factors in this model based on the dataset provided by Japan Exchange Group, Inc. (JPX).

KEYWORDS

LSTM JPX ML Real time prediction.

Cite This Article

Zarinabegam Mundargi, Chetanya Rathi, Harsh Dhabekar, Harshit Mundhra, Pushkar Helge, "JPX Japan Stock Exchange Prediction," International Journal of Scientific Research in Engineering and Management, vol. 6, no. 12, Dec 2022.

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Publication Date

12/23/2022

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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5

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Conflict of Interest

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

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