This article investigates the impact of the observation that managers can use cash to defer bankruptcy on default risk and corporate financial policies. I show that with managerial cash use to defer default, the impact of cash on default risk depends on two opposing channels. While cash provides managers with a buffer against bankruptcy during difficult times, it also reduces equityholders’ willingness to contribute funds to the firm, which increases bankruptcy risk. The total impact of cash on default risk is driven by firm and industry characteristics that affect the relative importance of these two channels. As managers’ propensity for excess cash holdings depends on this total impact, the model explains observed excess cash levels, their determinants, and a wide range of empirical regularities of corporate cash holdings properties.

This article develops a model to explore the implications of nondistressed debt renegotiation on debt prices and corporate policies. The model incorporates the empirical observation that creditors can influence firms also outside corporate distress through debt covenant renegotiation and not only in distress. We find that considering both distressed and nondistressed creditor interventions is key to investigating how creditor governance affects firms. The model explains cross-sectional patterns of control premiums and credit spreads that traditional debt renegotiation models do not capture. We also derive novel implications for the impact of firm characteristics associated with renegotiation on debt prices and corporate policies.

This study investigates the role of asymmetric information for the pricing, issuance volume, and design of innovative securities. By analyzing the information that structured product issuers provide to the investors of those products, we can identify specific sources of asymmetric information between the issuers and investors in this market. We show that issuers exploit this information friction to offer products to investors that appear more profitable for the issuer. In addition, we find that the friction induces issuers to design products with higher information asymmetry. Our results suggest that product issuers’ behavior increases information frictions in the financial system.

Using a dynamic model of financing, investment, and macroeconomic risk, we investigate when firms sell assets to fund investments (financing asset sales) across the business cycle. Equity financed investment transfers wealth from equity to debt because asset volatility declines and earnings increase when firms invest. Financing asset sales reduce asset collateral and, hence, transfer wealth back from debt to equity. Exploring the dynamics of the heretofore overlooked “asset sale versus external equity” financing margin across business cycles helps explain novel stylized facts about asset sales and their business cycle patterns that cannot be rationalized by traditional motives for selling assets.

This paper investigates how individual attention triggers influence financial risk-taking based on a large sample of trading records from a brokerage service that sends standardized push messages on stocks to retail investors. By exploiting the data in a difference-in-differences (DID) setting, we find attention triggers increase investors’ risk-taking. Our DID coefficient implies attention trades carry, on average, a 19 percentage-point-higher leverage than non-attention trades. We provide a battery of cross-sectional analyses to identify the groups of investors and stocks for which this effect is stronger.

This paper develops a structural equilibrium model with intertemporal macroeconomic risk, incorporating the fact that firms are heterogeneous in their asset composition. Compared with firms that are mainly composed of invested assets, firms with growth options have higher costs of debt because they are more volatile and have a greater tendency to default during recession when marginal utility is high and recovery rates are low. Our model matches empirical facts regarding credit spreads, default probabilities, leverage ratios, equity premiums, and investment clustering. Importantly, it also makes predictions about the cross section of all these features.