Year: 2025
Type: article
Abstract: Stock market prediction is a highly challenging task due to the inherent volatility and non-linear nature of financial markets, often rendering traditional forecasting methods ineffective. To overcome these limitations, this paper explores the application of advanced deep learning techniques, specifically Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) models, for predicting future stock prices. The study evaluates the predictive accuracy of these models and examines the effect of varying training epochs on their performance, using American Airlines stock data as a case study.
Source: Finance & Economics
DOI: 10.61173/qb8n8v02
Author: Xiubin Cui
Institution: China University of Geosciences
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4409650155