Corporate agility – the ability to respond quickly and effectively to changing business conditions – is crucial for firms' success. While important, this concept is difficult to measure and use in quantitative research. By applying machine learning techniques, we develop reliable measures of agility and analyse how agile firms manage exposure to monetary policy uncertainty, a significant and frequently occurring form of threat. Agile firms' stocks are significantly less exposed to this uncertainty as they proactively apply risk management techniques to reduce their exposure. This has real consequences: agile firms' investments are less affected by monetary policy tightening episodes.

Our study evaluates the return sensitivity of cryptocurrencies to various measures of uncertainty (uncertainty beta). We identify that crypto returns react primarily to financial uncertainty, which is the unforecastable component of multiple financial indicators. However, crypto returns are not sensitive to other forms of uncertainty such as macro, real, or policy uncertainty, as well as VIX, and inflation. The portfolio analysis yields a significant financial uncertainty premium of around 21% per month, which is driven by the outperformance (underperformance) of cryptocurrencies with a negative (positive) uncertainty beta. The portfolio returns are more potent in coins with speculative, rather than transactional, features such as proof-of-work, non-token, and mineable. Our findings suggest that large investors exhibit a willingness to pay higher premiums for cryptocurrencies with positive uncertainty betas, as these assets can be used as a hedging tool within a larger financial portfolio.