AWARE-FX: An Auditable Knowledge-Guided AI System for Measuring Corporate Foreign-Exchange Hedging Disclosure
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摘要
AWARE-FX is an auditable AI/NLP decision-support system that converts corporate annual-report text into traceable firm-year FX hedging-disclosure measures. It 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 (2008–2025) it retrieves and scores 543,527 snippets, with a rigorous evaluation including ablations, a stratified human audit, multi-seed encoder comparisons, and temporal testing. FinBERT generally achieves higher mean F1 than Contemporary encoders across seven of eight encoder-task combinations; temporal F1 ranges from 0.702 to 0.872. Abstaining on the least-confident 20% of temporal observations raises retained-sample F1 by 0.050–0.077. Deterministic Qwen3-8B shows strength on commodity and negation evidence but weaker performance on foreign-debt and accounting-context labels, highlighting limits of general-purpose LLMs for specialized financial tasks. The strict FX score is negatively associated with linked FX exposure measures, while the generic broad score is not, providing external construct validation rather than causal hedging effects. The study contributes a tested, auditable decision-support pipeline where retrieval, status logic, classification, uncertainty handling, aggregation, and external validation remain independently auditable [page::1]
