Summary of Integrative Analysis Of Financial Market Sentiment Using Cnn and Gru For Risk Prediction and Alert Systems, by You Wu et al.
Integrative Analysis of Financial Market Sentiment Using CNN and GRU for Risk Prediction and Alert Systems
by You Wu, Mengfang Sun, Hongye Zheng, Jinxin Hu, Yingbin Liang, Zhenghao Lin
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Finance (q-fin.CP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an innovative approach to analyzing stock market sentiment by combining Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). By leveraging the robust feature extraction capabilities of CNN, the model can identify local features and patterns in extensive network text data. The extracted feature sequences are then fed into the GRU model to understand the progression of emotional states over time and their potential impact on future market sentiment and risk. This integrated approach addresses order dependence and long-term dependencies inherent in time series data, enabling a detailed analysis of stock market sentiment and effective early warnings of future risks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper combines AI models to analyze stock market emotions and predict future risks. It uses special computer vision techniques to look at lots of text data from the internet and identifies patterns that can help predict what people will do with their money. Then, it uses a different type of model to understand how these patterns change over time and affect the market. This helps create accurate warnings about potential risks in the stock market. |
Keywords
» Artificial intelligence » Cnn » Feature extraction » Time series
