用BERT模型实时分析金融新闻，精准分类并预警市场事件。
FANAL -- Financial Activity News Alerting Language Modeling Framework
- 基于BERT改进框架，结合XGBoost与概率校准优化。
- 在12类金融事件分类中准确率超越GPT-4o等大模型。
- 适合金融风控、量化交易等需快速响应的场景。
在快速变化的金融领域，及时准确地解读市场新闻对利益相关者至关重要。本文提出FANAL（金融活动新闻警报语言建模框架），一种基于BERT的专用框架，用于实时检测和分析金融事件，并将新闻分为十二类。FANAL利用经过XGBoost处理的银标数据，采用先进的微调技术，并引入ORBERT（几率比BERT）——一种通过几率比偏好优化（ORPO）微调的BERT变体，实现更优的类别概率校准与金融事件相关性对齐。我们评估了FANAL在GPT-4o、Llama-3.1 8B和Phi-3等领先大模型上的表现，证明其在准确率和成本效益上均显著优于现有模型。该框架为金融智能与响应能力设立了新标准。
原文摘要 · Abstract (English)
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.
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