  We extract firms' cyber risk with a machine learning algorithm measuring the
proximity between their disclosures and a dedicated cyber corpus. Our approach
outperforms dictionary methods, uses full disclosure and not devoted-only
sections, and generates a cyber risk measure uncorrelated with other firms'
characteristics. We find that a portfolio of US-listed stocks in the high cyber
risk quantile generates an excess return of 18.72% p.a. Moreover, a long-short
cyber risk portfolio has a significant and positive risk premium of 6.93% p.a.,
robust to all factors' benchmarks. Finally, using a Bayesian asset pricing
method, we show that our cyber risk factor is the essential feature that allows
any multi-factor model to price the cross-section of stock returns.
