Predicting Stock Market Trends Using LSTM Networks: Overcoming RNN Limitations for Improved Financial Forecasting - Wang, Jiajing; Hong, Siyi; Dong, Yuxin; Li, Zichao; Hu, Jinxin (Journal of Computer Science and Software Applications, 2024)
In recent years, stocks have increasingly attracted our attention. The inherent volatility of stock prices, often caused by national and social policies, makes it challenging for investors to achieve profitable returns in the stock market. With the rapid advancement of artificial intelligence, computers have become adept at handling complex mathematical problems. Consequently, efforts have been made to leverage computers' remarkable computational capabilities to analyze and predict stock market trends. A growing number of professionals are delving into technologies related to deep learning. Two prominent applications in this field are data classification and regression. Recurrent Neural Networks (RNNs) have demonstrated superior performance in processing sequential data compared to other neural networks. However, RNNs can face issues such as gradient explosion or gradient vanishing when handling large datasets. These problems can cause RNNs to forget earlier data, resulting in inaccurate predictions. To address these issues, the Long Short-Term Memory (LSTM) model, an enhanced version of RNN, was introduced. LSTM incorporates input gates, forget gates, and output gates, effectively mitigating data forgetting and gradient explosion problems. By harnessing the computational power of computers, it becomes possible to make informed predictions about stock movements.