We develop a theoretical model quantifying how firm-level pandemic exposure and sentiment, as informational shocks, affect a firm’s credit spread and default risk. Consistent with model predictions, we find significantly positive impacts on single-name credit default swap (CDS) spreads from...

As the COVID-19 pandemic adversely affects the financial markets, a better understanding of the lending dynamics of a successful marketplace is necessary under the conditions of financial distress. Using the loan book database of Mintos (Latvia) and employing logit regression method, we provide...

We apply computational linguistic text mining (TM) analysis to extract and quantify relevant Chinese financial news in an attempt to further develop the classical early warning models of financial distress. Extending the work of Demers and Vega (2011), we propose a measure of the degree of...

I examine how the asymmetric persistence of good versus bad news earnings shapes the relation between a firm's default probability and the informativeness of its earnings for firm valuation. I extend earnings-persistence-based valuation theory to develop the study's key prediction that the...

We examine how equity-market frictions that restrict pessimistic trading, such as short-sale constraints, affect assessments of default risk. We find that these frictions decrease the usefulness of equity-market variables for identifying defaulting firms but increase their usefulness for...

We document the negative effect of stock liquidity on default risk for a sample of 46 countries. We further find that default risk declines following the introduction of the Directive on Markets in Financial Instruments (MiFID)—an exogenous shock that increases liquidity. The effect of...