2024/09/13 by Loïc Maréchal, Maréchal, Loïc, Nathan Monnet +1
Computer Science · #FOS: Computer and information sciences #FOS: Economics and business #Information and Cyber Security #Machine Learning (cs.LG) #Portfolio Management (q-fin.PM)
paper · pdf · doi:10.48550/arxiv.2409.08728
openalex publication_date 2024/09/13 · openalex created_date 2024/10/23 · openalex updated_date 2026/07/30
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.