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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM17284
Volume:
6 |
12 |
Dec 2022
Pages:
Publication Frequency:
Monthly (12 issues per year)
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.
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
Publication Date
12/23/2022
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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