Integrative Analysis of Financial Market Sentiment Using CNN and GRU for Risk Prediction and Alert Systems
This work addresses risk prediction for financial markets, but it is incremental as it combines existing methods without introducing a new paradigm.
The paper tackled stock market risk prediction by integrating CNN and GRU models to analyze sentiment from text data, achieving effective early warnings for future risks.
This document presents an in-depth examination of stock market sentiment through the integration of Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU), enabling precise risk alerts. The robust feature extraction capability of CNN is utilized to preprocess and analyze extensive network text data, identifying local features and patterns. The extracted feature sequences are then input into the GRU model to understand the progression of emotional states over time and their potential impact on future market sentiment and risk. This approach addresses the order dependence and long-term dependencies inherent in time series data, resulting in a detailed analysis of stock market sentiment and effective early warnings of future risks.