Year: 2024
Type: article
Abstract: Since the invention of the Artificial Neural Network (ANN), it has achieved great success in various supervised and unsupervised machine learning tasks because of its powerful ability of learning, relationship fitting, and information synthesis. However, traditional neural networks face limitations in handling sequential data, where each point is influenced by past trends—a critical aspect of stock prediction. Stock prices often reflect historical patterns, requiring models to capture these temporal dependencies. Long Short-Term Memory (LSTM) models, a type of Recurrent Neural Network (RNN), are particularly suited to this task, as they retain and integrate information over time, addressing issues like gradient vanishing and allowing precise forecasting. In this study, the LSTM model achieved a remarkably low loss of 0.00067, demonstrating its effectiveness in modeling complex, nonlinear dependencies in stock data and enhancing predictive accuracy. This robust approach provides valuable insights into future market behaviors, highlighting LSTM’s strength in financial forecasting.
Author: Junpu Hu
Institution: Beijing Normal-Hong Kong Baptist University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4405333241