Paper: arXiv 2412.10199

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.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 3.0/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper employs advanced deep learning concepts (CNN, GRU, attention) and theoretical equations for gradient updates, but it lacks specific backtest results, dataset details, or performance metrics, focusing instead on methodology and literature review.

Research Flowchart

  flowchart TD
  A["Research Goal: Integrate CNN & GRU for Stock Market<br>Risk Prediction via Sentiment Analysis"] --> B["Data Input: Network Text Data<br>(News, Social Media)"]
  B --> C["CNN Feature Extraction<br>Identify Local Patterns/Features"]
  C --> D["GRU Temporal Processing<br>Analyze Sequential Emotional States"]
  D --> E["Output: Risk Prediction & Alert System"]
  E --> F["Key Findings:<br>Precise Market Sentiment Analysis &<br>Effective Future Risk Warnings"]