Official code for ''RAG Meets Temporal Graphs: Time-Sensitive Modeling and Retrieval for Evolving Knowledge''.
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Updated
Feb 25, 2026 - Python
Official code for ''RAG Meets Temporal Graphs: Time-Sensitive Modeling and Retrieval for Evolving Knowledge''.
End-to-End Python implementation of Wu et al.'s (2025) ICAIF'25 paper. It translates unstructured earnings press releases into quantifiable market signals. Implements oLDA topic modeling, Transformer embeddings (BERT/FinBERT/MPNET), GPT-4o interpretability, and rigorous econometric analysis.
A custom spaCy-based Named Entity Recognition (NER) model designed for financial texts, capable of identifying companies, stock symbols, market indexes, and stock exchanges for use in news analytics and trading insights.
📈 Analyze press releases to predict earnings announcement returns using structured data and natural language processing techniques.
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