Integrative Analysis of Financial Market Sentiment Using CNN and GRU for Risk Prediction and Alert Systems
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916521917284352 |
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| author | Wu, You Sun, Mengfang Zheng, Hongye Hu, Jinxin Liang, Yingbin Lin, Zhenghao |
| author_facet | Wu, You Sun, Mengfang Zheng, Hongye Hu, Jinxin Liang, Yingbin Lin, Zhenghao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_10199 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Integrative Analysis of Financial Market Sentiment Using CNN and GRU for Risk Prediction and Alert Systems Wu, You Sun, Mengfang Zheng, Hongye Hu, Jinxin Liang, Yingbin Lin, Zhenghao Machine Learning Computational Finance 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. |
| title | Integrative Analysis of Financial Market Sentiment Using CNN and GRU for Risk Prediction and Alert Systems |
| topic | Machine Learning Computational Finance |
| url | https://arxiv.org/abs/2412.10199 |