This study provides a rigorous empirical comparison of structural and reduced-form credit risk frameworks. As major difference we focus on the discriminative modeling of default time. In contrast to previous literature, we calibrate both approaches to bond and equity prices. By using same input...

The paper investigates the impact of jumps in forecasting co-volatility, accommodating leverage effects. We modify the jump-robust two time scale covariance estimator of Boudt and Zhang (2013)such that the estimated matrix is positive definite. Using this approach we can disentangle the...

A firm's current leverage ratio is one of the core characteristics of credit quality used in statistical default prediction models. Based on the capital structure literature, which shows that leverage is mean-reverting to a target leverage, we forecast future leverage ratios and include them in...

A firm's current leverage ratio is one of the core characteristics of credit quality used in statistical default prediction models. Based on the capital structure literature, which shows that leverage is mean-reverting to a target leverage, we forecast future leverage ratios and include them in...

We analyse interactions of simultaneous shifts in comprehensive balance sheet items annually and identify common (latent) factors, which are consistent across years. Five factors are interpreted to reflect five major decisions in businesses: Financial Flexibility, Short-term Credit, Long-term...

This paper addresses the theoretical foundations of corporate failure prediction, using the neo-classical theory of capital structure as a starting point. The paper intends to demonstrate the feasibility of such an approach in a simple setting, i.e. by using a simple theoretical model and a...

We examine a simple measure of operating leverage: the ratio of fixed costs (measured by depreciation and amortization plus selling, general, and administrative expenses) to the market (or book) value of assets. We find that this measure of operating leverage positively predicts returns....

Using a random sample of 60% of our cross-sectional data on U.S. stocks from 1964 to 2012, we trained four machine learning algorithms to forecast debt paydown over a one-year horizon. An evaluation of these candidate models on half of the hold-out sample (20% of the original dataset) showed...

We estimate operating synergies from a unique hand-collected dataset on management's forecasts of merger incremental cash flows. We find that forecasted synergies induce acquirers to make leverage-increasing acquisitions. Institutional investors and bidders' credit rating enhance that relation...