The Cyber Risk Premium

38 Pages Posted: 15 Jul 2020

See all articles by Hao Jiang

Hao Jiang

Michigan State University

Naveen Khanna

Michigan State University

Qian Yang

Michigan State University

Date Written: June 28, 2020

Abstract

This paper studies how stock markets perceive and price cyber risk. We estimate the ex-ante likelihood for a firm to experience a data breach using logistic LASSO regressions combined with cross-validation. Ranking firms based on this proxy for cyber risk, we find that it influences both investor portfolio choices and stock prices. In particular, institutional investors tend to sell stocks with high cyber risk and buy those with low cyber risk; this tendency is stronger during periods with higher data breach concerns. We show that a one-standard deviation increase in cyber risk is associated with a premium of 3.41% per annum.

Keywords: Cyber Risk, Cybersecurity, Risk Premium, Machine Learning, LASSO, Cross-Validation

JEL Classification: G11, G12, G14, G17

Suggested Citation

Jiang, Hao and Khanna, Naveen and Yang, Qian, The Cyber Risk Premium (June 28, 2020). Available at SSRN: https://ssrn.com/abstract=3637142 or http://dx.doi.org/10.2139/ssrn.3637142

Hao Jiang

Michigan State University ( email )

315 Eppley Center
Department of Finance
East Lansing, MI 48824
United States

HOME PAGE: http://sites.google.com/site/haojiangfinance/

Naveen Khanna

Michigan State University ( email )

East Lansing, MI 48824-1121
United States
517-353-1853 (Phone)
517-432-1080 (Fax)

Qian Yang (Contact Author)

Michigan State University ( email )

MI 48823
United States

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