Purpose Corporate failure remains a critical financial concern, with implications for both
firms and financial institutions; this paper aims to review the literature that proposes …

We develop a model of neural networks to study the bankruptcy of US banks, taking into
account the specific features of the recent financial crisis. We combine multilayer …

Bankruptcy prediction has been widely studied as a binary classification problem using
financial ratios methodologies. In this paper, Leave-One-Out-Incremental Extreme Learning …

We contend that corruption must be detected as soon as possible so that corrective and
preventive measures may be taken. Thus, we develop an early warning system based on a …

This paper studies the relationship between corporate failure forecasting and earnings
management variables. Using a new threshold model approach that separates samples into …

Ensemble methods based on-NN models minimise the effect of outliers in a training dataset
by searching groups of the closest data points to estimate the response of an unseen …

Economic risks faced by manufacturing enterprises are gradually increasing and risk
reduction whilst maintaining high financial performance has become key to their survival …

Extreme learning machine (ELM) has shown its good performance in regression
applications with a very fast speed. But there is still a difficulty to compromise between better …

This paper analyzes the prediction performance of human resources (HR) variables in
corporate failure modeling. We define corporate failure as a two‐phase process from …

This paper explores the properties of using a generalized additive model with embedded
variable selection for the prediction of bankruptcy. The main purpose is to explore an …