paper: Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction
Pipeline Overview
- Data Collection
Reddit Data: r/wallstreetbets submissions and comments from Academic Torrents Price and Return Data: Via WRDS.
- Data Preprocessing
Ticker Extraction: Match posts to stocks using regex-based ticker detection Temporal Alignment: Create 20-day lookback windows before each earnings announcement date Daily Aggregation: Combine all posts about a stock per day into daily text sequences
- Feature Engineering
Text Vectorization: FinBERT embeddings for each day's aggregated text Sequence Construction: 20-day sequences for each stock in each day Labels: binary classification - top 25% of volatility considered extreme volatility (positive class) and normal volatility (negative class)
- Model Architecture
BERT Embeddings: Domain-specific financial language model (FinBERT) Sequential Modeling: Bi-directional GRU processes Temporal Attention: Learns which days matter most for prediction Prediction Head: Dense layers output earnings surprise probability
- Evaluation: Use F1 score and Recall as major evaluation metrics. Used Logistic regression (F1 0.38) and CNN (F1 0.62) as benchmarks.
Results
Average Loss: 0.6782
Overall Accuracy: 60.5697%
Risk Precision: 0.5842
Risk Recall: 0.7331
Risk F1-Score: 0.6503
Detailed Classification Report:
precision recall f1-score
Safe (0) 0.6419 0.4783 0.5481
Risk (1) 0.5842 0.7331 0.6503
accuracy 0.6057