RNN LSTM Architecture to Improve the Accuracy of Forecasting Stock Price Moment

Authors

  • Shubhangi Lohakpure Ph.D Scholar, E&TC Department, G H Raisoni University, Amravati, Amravati City, Maharashtra, India.
  • Swati Dixit Asst Prof, E&TC Department G H Raisoni Institute of Engineering and Technology, Nagpur Maharashtra, India.

Keywords:

RNN LSTM architecture, stock prediction, RSI Indicators, accuracy

Abstract

Earning a good share market profit is difficult to achieve because of its non-linear nature and volatile moment. Nowadays, programmed algorithms with deep learning are used to predict the direction of moment and they are more accurate and efficient to provide the prediction of stock. In this work, RNN LSTM architecture and artificial neural network have been utilized to predict the stock moment and exact entry point in particular stocks. The financial data of five different companies of different sectors are used as inputs for our model. The model processes the variables like volume, Opening price, and closing price along with Bollinger Band and RSI indicators. In this work, the RNN LSTM architecture and deep learning have improved the efficiency by 80-98 percent compared to past methods.

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Published

24.03.2024

How to Cite

Lohakpure, S. ., & Dixit, S. . (2024). RNN LSTM Architecture to Improve the Accuracy of Forecasting Stock Price Moment. International Journal of Intelligent Systems and Applications in Engineering, 12(18s), 34–39. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4947

Issue

Vol. 12 No. 18s (2024)

Section

Research Article

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