This study introduces deep learning models for corporate bankruptcy forecasting using
textual disclosures. Although textual data are common, it is rarely considered in the financial …

We investigate the relative importance of various bankruptcy predictors commonly used in
the existing literature by applying a variable selection technique, the least absolute …

Predicting corporate bankruptcy is one of the fundamental tasks in credit risk assessment. In
particular, since the 2007/2008 financial crisis, it has become a priority for most financial …

We study bankruptcy prediction over the international market using Compustat Global
database. First, we apply a popular variable selection method, adaptive LASSO (least …

A frequent problem in binary classification is class imbalance between a minority and a
majority class such as defaults and non-defaults in default prediction. In this article, we …

This study proposes to address the economic significance of unpaid taxes by using an
automatic system for predicting a tax default. Too little attention has been paid to tax default …

We propose and study properties of maximum likelihood estimators in the class of
conditional transformation models. Based on a suitable explicit parameterization of the …

We develop a Bayesian network (LASSO‐BN) model for firm bankruptcy prediction. We
select financial ratios via the Least Absolute Shrinkage Selection Operator (LASSO) …

We model multi-period cumulative and forward corporate default probabilities using
machine learning methods and introduce a novel hybrid econometric-machine learning …

We investigate the relationship between a firm's innovation performance and its probability
of bankruptcy. Estimating the discrete hazard model with a comprehensive set of …