用公开数据自动生成风险预测标签，训练出可部署的精准风险概率模型。
Foresight Learning for SEC Risk Prediction
- 从年报风险描述生成带时间边界的预测问题，自动匹配后续披露结果作为标签
- 模型在概率准确性和校准度上超越GPT-5等通用大模型，仅需单块GPU运行
- 无需人工标注或外部数据，为企事业单位文档学习提供可复现的自动化路径
SEC文件中的风险披露多为定性描述，很少量化其发生概率，限制了概率分析的应用。核心障碍在于缺乏大规模、以风险级别为监督信号的数据，将披露的风险与实际结果关联。本文提出一个全自动数据生成流程，仅使用公开数据，将年报中的风险描述转化为具有时间边界的风险问题，并通过后续披露自动判定结果来标注。基于该数据集，训练了一个紧凑的大语言模型，用于估计特定风险在给定时间段内发生的概率。尽管模型规模有限，其性能显著优于预训练模型和启发式基线，且在概率准确性与校准性上超越前沿通用模型（包括GPT-5）。本研究证明，‘预见性学习’可实现仅依赖原始、时序化、领域内文本的可扩展、全自动领域专家模型训练，无需专有数据、外部语料或人工标注。所获模型达到前沿性能，同时可在单块GPU上部署。这为从企业文档中学习可校准、决策相关信号提供了通用路径。为支持透明性与可复现性，研究中使用的评估数据集已开源。
原文摘要 · Abstract (English)
Risk disclosures in SEC filings describe potential adverse events but rarely quantify their likelihood, limiting their usefulness for probabilistic analysis. A central obstacle is the absence of large-scale, risk-level supervision linking disclosed risks to realized outcomes. We introduce a fully automated data generation pipeline that converts qualitative SEC risk disclosures into temporally grounded supervision using only public data. For each filing, the pipeline generates firm-specific, time-bounded risk queries from the Risk Factors section and labels them by automatically resolving outcomes against subsequent disclosures. Using this dataset of risk queries and outcomes grounded in SEC filings, we train a compact large language model to estimate the probability that a disclosed risk will materialize within a specified horizon. Despite its modest size, the resulting model substantially improves over pretrained and heuristic baselines, and outperforms frontier general-purpose models, including GPT-5, on probabilistic accuracy and calibration. More broadly, this work demonstrates that Foresight Learning enables scalable and fully automated training of domain-specific expert models using only raw, chronological, in-domain text -- without proprietary data, external corpora, or manual annotation. The resulting models achieve frontier-level performance while remaining deployable on a single GPU. This result suggests a general pathway for learning calibrated, decision-relevant signals from naturally occurring enterprise documents. To support transparency and reproducibility, we open-source the evaluation dataset used in this study. Evaluation Data: https://huggingface.co/datasets/LightningRodLabs/sec_risk_questions_test_set Data Generation Platform: https://lightningrod.ai/ SDK: https://github.com/lightning-rod-labs/lightningrod-python-sdk
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