Machine learning-based forecasting and prediction of stock prices

Registration ID: IJNRD_197873

Published ID: IJNRD2305920

DOI: Click Here to Get

Nadendla. Tarun Venkata Sai , p.sanjeev reddy , leela manohar

LSTM, corporate trend projections, technical evaluation, artificial intelligence, and past stock prices.

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.

Published Paper   E-Certificate

Nadendla. Tarun Venkata Sai, p.sanjeev reddy, & leela manohar (May-2023). Machine learning-based forecasting and prediction of stock prices. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(5), j38-j45. https://ijnrd.org/papers/IJNRD2305920.pdf

Volume 8 Issue 5, May-2023

Pages : j38-j45

Paper Reg. ID: IJNRD_197873

Published Paper Id: IJNRD2305920

Downloads: 000121981

Research Area: Computer Engineering 

Country: Guntur, Andhra Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2305920.pdf

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