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

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  • You Wu
  • Mengfang Sun
  • Hongye Zheng
  • Jinxin Hu
  • Yingbin Liang
  • Zhenghao Lin

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.

Suggested Citation

  • You Wu & Mengfang Sun & Hongye Zheng & Jinxin Hu & Yingbin Liang & Zhenghao Lin, 2024. "Integrative Analysis of Financial Market Sentiment Using CNN and GRU for Risk Prediction and Alert Systems," Papers 2412.10199, arXiv.org.
    • Handle: RePEc:arx:papers:2412.10199
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    File URL: http://arxiv.org/pdf/2412.10199
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    References listed on IDEAS

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    1. Qiqin Zhou, 2024. "Portfolio Optimization with Robust Covariance and Conditional Value-at-Risk Constraints," Papers 2406.00610, arXiv.org.
    2. Yuhui Jin, 2024. "GraphCNNpred: A stock market indices prediction using a Graph based deep learning system," Papers 2407.03760, arXiv.org, revised Jul 2024.
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