doi: 10.7763/IJET.2024.V16.1271
Using Deep Learning Algorithms Prediction of the Closing Price of Stocks with Indication Features
Yoon Kong
1,*
,
Uddalok Sen
2
- 1 The Irvine High School, 4321 Walnut Ave, Irvine, CA 92604, USA
- 2 The University of Engineering and Management, Kolkata, West Bengal, India
- Manuscript received April 10, 2024
- revised May 13, 2024
- accepted June 4, 2024
- published August 9, 2024
Abstract
Stock market price forecasting is currently a hot topic for research in the artificial intelligence field. It is quite challenging to correctly forecast stock market returns because of the financial stock markets’ significant volatility and non-linearity. Programmable methods of prediction are now more accurate at predicting stock values thanks to developments in artificial intelligence and computational power. In the present study, stock price data from five different sectors with 10 years of history has been collected, and the closing price for each stock has been predicted using Long Short-Term Memory (LSTM) and Artificial Neural Network (ANN) models. The comparison between the metrics has also been shown in the following study. Two new features from the momentum indicator and long-term and short-term moving averages of the stock price have been engineered as two newly introduced indicator features in the machine learning algorithms. The closing price of stocks has been predicted in this study with the help of existing and newly introduced features.
Keywords
- stock price prediction
- bollinger band
- long-term Moving Average (MA)
- short-term Moving Average (MA)
- Long Short-Term Memory (LSTM)
- Artificial Neural Network (ANN)
How to Cite
Yoon Kong and Uddalok Sen, "Using Deep Learning Algorithms Prediction of the Closing Price of Stocks with Indication Features," International Journal of Engineering and Technology, vol. 16, no. 3, pp. 143-148, 2024. https://doi.org/ 10.7763/IJET.2024.V16.1271
Copyright & License
Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).