用AI精准提取企业外汇对冲披露信息，可审计且结果可信。
AWARE-FX: An Auditable Knowledge-Guided AI System for Measuring Corporate Foreign-Exchange Hedging Disclosure
- 结合专业词典与逻辑规则，从年报中自动识别对冲证据。
- 在超2.4万家企业年份数据中提取54万条文本片段，准确率达0.872。
- 适合金融监管、财报分析及需要可追溯AI决策的研究者使用。
企业年报中关于外汇风险管理、衍生品使用、自然对冲及明确不使用的信息呈弱结构化特征。本研究构建了AWARE-FX系统，一种可审计的AI/NLP决策支持工具，将报告文本转化为可追踪的企业年度对冲披露度量。系统融合专业来源词典、否定与会计状态逻辑、渠道特定财务编码器、精确证据门控、保守聚合策略及审计日志。在2008-2025年间覆盖24,909个香港企业年份，成功检索并评分543,527条文本片段。通过消融实验、分层300条样本人工审计、三种种子模型（FinBERT-ModernBERT）对比、严格2023-2025年时间测试、概率校准、选择性预测及固定提示生成模型基准，验证其可靠性。FinBERT在八项任务中的七项平均F1更高，时间序列F1范围为0.702至0.872；对置信度最低20%样本弃权后，保留样本F1提升0.050至0.077。确定性Qwen3-8B在商品和否定证据上表现良好，但在外债与会计语境标签上表现差，说明通用大模型无法完全替代领域约束。严格外汇得分与关联基线及压力期外汇暴露负相关，而通用宽泛得分无此关联，提供外部构念效度支持，非对冲有效性因果估计。AWARE-FX贡献了一个经验证的决策支持架构，其中检索、状态逻辑、分类、不确定性处理、聚合与外部验证均保持独立可审计。
原文摘要 · Abstract (English)
Corporate annual reports contain weakly structured evidence about foreign-exchange risk management, derivative use, natural hedging, and explicit non-use. This study develops AWARE-FX, an auditable AI/NLP decision-support system that converts report text into traceable firm-year hedging-disclosure measures. The system combines a professional-source lexicon, negation and accounting-status logic, channel-specific financial encoders, exact evidence gates, conservative aggregation, and an audit ledger. Across 24,909 Hong Kong firm-years from 2008-2025, it retrieves and scores 543,527 snippets. Reliability is evaluated through ablations, a stratified 300-snippet human audit, three-seed FinBERT-ModernBERT comparisons, strict 2023-2025 temporal tests, probability calibration, selective prediction, and fixed-prompt generative-model benchmarks. FinBERT has the higher mean F1 in seven of eight encoder task-split comparisons; its temporal F1 ranges from 0.702 to 0.872. Abstaining on the 20% least-confident temporal observations raises retained-sample F1 by 0.050-0.077. Deterministic Qwen3-8B performs strongly on commodity and negation evidence but poorly on foreign-debt and accounting-context labels, showing that a general-purpose LLM does not uniformly replace domain constraints. The strict FX score is negatively associated with linked baseline and stress-period FX exposure, whereas the generic broad score is not. These associations provide external construct validation, not causal estimates of hedging effectiveness. AWARE-FX contributes a tested decision-support architecture in which retrieval, status logic, classification, uncertainty handling, aggregation, and external validation remain separately auditable.
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