Integrative Analysis of Financial Market Sentiment Using CNN and GRU for Risk Prediction and Alert Systems

TL;DR

This paper combines CNN and GRU models to analyze stock market sentiment from text data, improving risk prediction and enabling early alerts for potential market downturns.

Contribution

It introduces an integrated CNN-GRU framework that captures local features and temporal dependencies in market sentiment analysis for the first time.

Findings

Enhanced accuracy in risk prediction

Effective early warning system for market risks

Improved understanding of sentiment evolution

Abstract

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.