Different ways of managing risk as reported in 10-Ks: A supervised learning approach

Abstract

We use supervised learning on annual reports of publicly listed US firms (10-Ks) to build textual measures of risk management via derivatives, insurance, diversification, long-run contracts, and credit lines. Validation exercises favor these supervised learning-based measures over those based on word lists. Panel regressions (1996–2015) indicate that firms using one form of risk management are more likely to also use other forms. In contrast, intensive use of one risk management technique associates with less intensive use of other methods. Findings are consistent with a model featuring fixed costs of organizational capacity for managing risks and increasing marginal costs of hedging.