Stock market prediction and analysis using LSTM Neural Networks

kothapally srujani; Bhavagna Uppala; K. Jaya Laxmi

Stock market prediction and analysis using LSTM Neural Networks

March 2023

Vol-9, Issue-2

Paper ID: 19343

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

Information technology

Keywords

LSTM Neural Network Stock Prediction Gradient Descent Market Shares Accurate Graphical Outcomes.

Abstract

The prediction of stock value is a complex task that needs a robust algorithm background in order to compute long-term share prices. Stock prices are correlated within the nature of the market; hence it will be difficult to predict the costs. Prior studies concentrated on the factors that can affect investors' emotions. The researchers completed studies based on social media, the period of the stock market, and the use of various models to extract the feature of stocks. To accurately anticipate the stock, they initially used NLP and GBDT, which primarily focus on emotion and select information from the news (which didn’t give accurate predictions). The proposed algorithm uses the market data to predict the share price using machine learning techniques like recurrent neural networks named Long Short-Term Memory (LSTM), in that process weights are corrected for each data point using stochastic gradient descent. This system will provide accurate outcomes in comparison to currently available stock price predictor algorithms. The network is trained and evaluated with various input data sizes to urge the graphical results.

License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author Information

#	Name	Institute / Affiliation
1	kothapally srujani	B V Raju institute of technology
2	Bhavagna Uppala	B V Raju institute of technology
3	K. Jaya Laxmi	B V Raju institute of technology

How to Cite

Use the following formats to cite this article in your research.

APA Style

srujani, kothapally, Uppala, Bhavagna, & Laxmi, K. Jaya (2023). Stock market prediction and analysis using LSTM Neural Networks. International Journal of Advance Research and Innovative Ideas In Education, 9(2), 446-451.

MLA Style

srujani, kothapally, et al. "Stock market prediction and analysis using LSTM Neural Networks." International Journal of Advance Research and Innovative Ideas In Education, vol. 9, no. 2, 2023, pp. 446-451.

IEEE Style

kothapally srujani, Bhavagna Uppala, and K. Jaya Laxmi, "Stock market prediction and analysis using LSTM Neural Networks," International Journal of Advance Research and Innovative Ideas In Education, vol. 9, no. 2, pp. 446-451, 2023.

Vancouver Style

srujani kothapally, Uppala Bhavagna, Laxmi K. Jaya. Stock market prediction and analysis using LSTM Neural Networks. International Journal of Advance Research and Innovative Ideas In Education. 2023;9(2):446-451.

Harvard Style

srujani, kothapally, Uppala, Bhavagna, & Laxmi, K. Jaya (2023) 'Stock market prediction and analysis using LSTM Neural Networks', International Journal of Advance Research and Innovative Ideas In Education, 9(2), pp. 446-451.

Chicago Style

srujani, kothapally, Bhavagna Uppala, and K. Jaya Laxmi. "Stock market prediction and analysis using LSTM Neural Networks." International Journal of Advance Research and Innovative Ideas In Education 9, no. 2 (2023): 446-451.

Turabian Style

srujani, kothapally, Bhavagna Uppala, and K. Jaya Laxmi. "Stock market prediction and analysis using LSTM Neural Networks." International Journal of Advance Research and Innovative Ideas In Education 9, no. 2 (2023): 446-451.