INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
For many years, investors' primary concern was the stock market. Stock trend indicators have a high demand given that they aid in the rapid addition of profits. More exact outcomes increase the likelihood of earning greater amounts of cash. Stock market patterns are influenced by politics, economics, or social factors. Basic or technical analysis can be used to assess stock trend movements. In this scenario, the financial aspects of the business are evaluated, as well as strategic efforts, small-scale signs, and consumer behaviour. It is the process of analysing historical and current prices with the goal to estimate possible future prices. There are numerous machine learning, deep learning.
It has been shown to produce usually reliable outcomes. In order to create predictions, prior publications concentrated on different models and their components. They sought to provide the best parameter values feasible for the projections. The goal of this study is to provide investors with models that can manage data efficiently when the right values for parameters are employed. The LSTM models are supplied because of their capacity to produce decent outcomes via technical data analysis. algorithms in order to choose the optimal one for collecting financial data in the current scenario. The data originates from the firms' previous stock prices, encompassing wide, near, substantial, and low values.
Keywords:
LSTM, corporate trend projections, technical evaluation, artificial intelligence, and past stock prices.
Cite Article:
"Machine learning-based forecasting and prediction of stock prices", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 5, page no.j38-j45, May-2023, Available :http://www.ijnrd.org/papers/IJNRD2305920.pdf
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ISSN:
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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