This article analyzes the manifold situations in which the efficient-market hypothesis (EMH) has influenced — or has failed to influence — federal securities regulation and state corporate law, and the prospective roles for the EMH in these contexts. In federal securities regulation, the EMH...

This paper examines the performance of two commonly applied bankruptcy prediction models, the accounting ratio-based Altman Z-Score model, and the structural Distance to Default model which currently underlies Morningstar's Financial Health Grade for public companies (Morningstar 2008)....

In credit default prediction models, the need to deal with time-varying covariates often arises. For instance, in the context of corporate default prediction a typical approach is to estimate a hazard model by regressing the hazard rate on time-varying covariates like balance sheet or stock...

We examine how the risk-return profiles of carmakers BMW and Porsche depend on whether car models are produced in the US or Europe. Using data from the US car market we combine a demand system for differentiated products with counterfactual paths to macroeconomic variables. We let prices and...

Credit risk measurement and management become more important in all financial institutions in the light of the current financial crisis and the global recession. This particularly applies to most of the complex structured financing forms whose risk cannot be quantified with com-mon rating...

Bharath and Shumway (2008) provide evidence that shows that it is the functional form of Merton’s (1974) distance to default (DD) model that makes it useful and important for predicting defaults. In this paper, we investigate whether the default predictability of the Merton DD model would be...

Firm cyclicality decreases by around 40% after the inception of credit default swap (CDS) trading. The effect is due to CDS firms’ lower asset growth-GDP growth sensitivity in good times and stronger for firms facing a more severe exacting creditor problem. The cyclicality-reducing effect of...

We propose an econometric model for predicting the share of bank debt held by bankrupt firms by combining a novel set of firm-level financial variables and macroeconomic indicators. Our firm-level data include payment remarks in the form of debt collections from private agencies and attachments...

This paper proposes a machine learning approach to estimate physical forward default intensities. Default probabilities are computed using artificial neural networks to estimate the intensities of the inhomogeneous Poisson processes governing default process. The major contribution to previous...

Probability of default prediction is one of the important tasks of rating agencies as well as of banks and other financial companies to measure the default risk of their counterparties. Knowing predictors that significantly contribute to default prediction provides a better insight into...