# AWARE-FX: An Auditable Knowledge-Guided AI System for Measuring Corporate Foreign-Exchange Hedging Disclosure

arXiv: https://arxiv.org/abs/2607.27611 · HTML: https://mlsanity.com/paper/2607.27611/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 61 references that are in the map.
- First posted on arXiv 30 Jul 2026.

## Builds on (most cited first, 14 of 14)

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- [Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference](https://mlsanity.com/paper/2412.13663/) (2024)
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- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts](https://mlsanity.com/paper/1307.5336/) (2013)
- [FinBERT: A Pretrained Language Model for Financial Communications](https://mlsanity.com/paper/2006.08097/) (2020)
- [Training Complex Models with Multi-Task Weak Supervision](https://mlsanity.com/paper/1810.02840/) (2018)
- [FinMTEB: Finance Massive Text Embedding Benchmark](https://mlsanity.com/paper/2502.10990/) (2025)
- [FinDABench: Benchmarking Financial Data Analysis Ability of Large Language Models](https://mlsanity.com/paper/2401.02982/) (2024)
- [Interpretable LLMs for Credit Risk: A Systematic Review and Taxonomy](https://mlsanity.com/paper/2506.04290/) (2025)

## Abstract (arXiv)

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.