用AI分析企业披露内容，量化网络风险并发现其回报溢价。
Disentangling the sources of cyber risk premia
- 基于机器学习分析企业文本，识别特定网络威胁类型并生成风险评分。
- 高风险评分公司股票显著跑赢市场，且风险溢价稳定存在。
- 市场将各类网络风险视为整体，不区分具体威胁类型。
本文采用基于机器学习的分析方法，根据企业披露文件和专门构建的网络威胁语料库，量化企业的网络风险。模型能够识别与特定网络威胁类型相关的段落，并为企业分配多个关联的网络风险评分。这些评分独立于其他企业特征。高网络风险评分的股票显著优于其他股票。多空组合的网络风险因子具有正向风险溢价，对所有基准因子均保持稳健，并有助于解释股票收益。此外，我们发现市场并未区分不同类型的网络风险，而是将其视为单一的综合风险。
原文摘要 · Abstract (English)
We use a methodology based on a machine learning algorithm to quantify firms' cyber risks based on their disclosures and a dedicated cyber corpus. The model can identify paragraphs related to determined cyber-threat types and accordingly attribute several related cyber scores to the firm. The cyber scores are unrelated to other firms' characteristics. Stocks with high cyber scores significantly outperform other stocks. The long-short cyber risk factors have positive risk premia, are robust to all factors' benchmarks, and help price returns. Furthermore, we suggest the market does not distinguish between different types of cyber risks but instead views them as a single, aggregate cyber risk.
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