Yes, they can! Machine learning models that exploit big data identify leverage determinants and predict leverage better than classical methods. By allowing for nonlinearities and complex interactions, machine learning boosts the out-of-sample R-squared from 36% to 56% over linear methods such as...

The global financial crisis has shown that many financial institutions dealing with credit derivatives were exposed to severe unexpected losses. This indicates that systematic influences are decisively underestimated particularly with regard to structured products like securitized tranches of...

This paper analyzes a group of 755 firms, with aggregate indebtedness of US$6.2 trillion, to assess the solvency risks and liquidity needs facing the U.S. corporate sector based on projections of net income, availability and cost of funding, and debt servicing flows under different stress test...

We study the impact of the COVID-19 recession on capital structure of publicly listed U.S. firms. Our estimates suggest leverage (Net Debt/Asset) decreased by 5.3 percentage points from the pre-shock mean of 19.6 percent, while debt maturity increased moderately. This de-leveraging effect is...

This paper develops a principal-agent model of financial contracting in which optimal contracts resemble a combination of debt and equity. When defaulting on debt, the firm is punished by disruption of external funding. Such contracts however, invite rivals to compete more aggressively to...