FANAL -- Financial Activity News Alerting Language Modeling Framework
This addresses the need for timely financial intelligence for stakeholders in the financial sector, though it appears incremental as it builds on existing BERT and optimization techniques.
The paper tackles the problem of real-time financial event detection and analysis from market news by introducing FANAL, a specialized BERT-based framework that categorizes news into twelve financial categories. The result shows superior accuracy and cost efficiency compared to leading large language models like GPT-4o, Llama-3.1 8B, and Phi-3.
In the rapidly evolving financial sector, the accurate and timely interpretation of market news is essential for stakeholders needing to navigate unpredictable events. This paper introduces FANAL (Financial Activity News Alerting Language Modeling Framework), a specialized BERT-based framework engineered for real-time financial event detection and analysis, categorizing news into twelve distinct financial categories. FANAL leverages silver-labeled data processed through XGBoost and employs advanced fine-tuning techniques, alongside ORBERT (Odds Ratio BERT), a novel variant of BERT fine-tuned with ORPO (Odds Ratio Preference Optimization) for superior class-wise probability calibration and alignment with financial event relevance. We evaluate FANAL's performance against leading large language models, including GPT-4o, Llama-3.1 8B, and Phi-3, demonstrating its superior accuracy and cost efficiency. This framework sets a new standard for financial intelligence and responsiveness, significantly outstripping existing models in both performance and affordability.