Using Alternative Data to Understand Risk

This thesis leverages diverse alternative data sources, including high-frequency stock volatility measures, news analytics, a census of internet-connected devices, and open-source software repositories, to enhance comprehension of cyber risk and stock volatility. We demonstrate the potential of alternative data to measure firm-level cyber risk in data-scarce environments and explore the likelihood of future breaches. Subsequently, we utilize news analytics and robust panel data methods to investigate the underappreciated relationships between public news, information leakage and asset volatility. Overall, this research employs data-driven methodologies and advances understanding of risk in the cyber domain and uncovers insights into how information is processed in the market.