AI-based stock price prediction

• Main Author: Lim, Yong
• Other Authors: Alex Chichung Kot
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2023
• Subjects: Engineering::Electrical and electronic engineering
• Online Access: https://hdl.handle.net/10356/166990
• Institution: Nanyang Technological University

The rise of retail investors in recent years has quietly created an opportunity for anyone to evaluate the stock market via their mobile device, tablet, or desktop and potentially grow their wealth. With excessive volatility in the stock market influenced by sentiment and inflation, the art of stock market forecasting has become a topic of global interest. This study aims to predict stock values based on the LSTM model while examining the RNN and XGBoost models. Hence, by analyzing data from Yahoo! Finance and Twitter, the study provides an in-depth examination of the performance evaluation of the three models. The results show that the LSTM and RNN models outperform the XGBoost model in predicting short-term stock volatility.