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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 financial distress to bankruptcy, so that we can determine the prediction power of HR variables along a firm's phase in the financial deterioration process. We demonstrate the use of HR variables and their application to a two‐phase corporate failure model, providing first evidence for the predictive power of HR variables. The experimental results, based on real‐world datasets from Belgium, show that HR variables used in conjugation with accounting‐based information improve the accuracy of prediction modeling. However, the predictive power of HR variables varies in different phases of corporate failure with better prediction accuracy during the initial symptoms of corporate failure (i.e., financial distress). Findings show that our proposed model predicted financial distress with 84.1%, whereas the accuracy decreased to 83.3% when predicting bankruptcy. Besides, they also show that, on average, the inclusion of HR variables improves the global accuracy of the prediction models of 3.8% and allows to decrease Type I error of 5%.

Financial information has been widely used to design bankruptcy prediction models. All research works that have studied such models assume that financial statements are reliable. However, reality is a bit different. Indeed, firms may tend to present their financial accounts depending on particular circumstances, especially when seeking to change the perception of the risk incurred by their partners, and thus distort or alter some of them. Consequently, one may wonder to what extent such “manipulations”, called earnings management, may influence any model that relies on accounting data. This is why we study how earnings management may affect financial variables and how it can indirectly distort predictions made by failure models. For this purpose, we used a measure that makes it possible to assess potential account manipulations, and not effective manipulations. Our results show that when these distortions are measured and used with other financial variables, models are more accurate than those that solely rely on pure financial data. They also show that the improvement of model accuracy is essentially due to a reduction of type-I error—the costliest error in economic terms.

This paper presents two new clustering techniques based on Extreme Learning Machine (ELM). These clustering techniques can incorporate a priori knowledge (of an expert) to define the optimal structure for the clusters, i.e. the number of points in each cluster. Using ELM, the first proposed clustering problem formulation can be rewritten as a Traveling Salesman Problem and solved by a heuristic optimization method. The second proposed clustering problem formulation includes both a priori knowledge and a self-organization based on a predefined map (or string). The clustering methods are successfully tested on 5 toy examples and 2 real datasets.

This paper proposes a methodology for identifying data samples that are likely to be mislabeled in a c-class classification problem (dataset). The methodology relies on an assumption that the generalization error of a model learned from the data decreases if a label of some mislabeled sample is changed to its correct class. A general classification model used in the paper is OP-ELM; it also provides a fast way to estimate the generalization error by PRESS Leave-One-Out. It is tested on two toy datasets, as well as on real life datasets for one of which expert knowledge about the identified potential mislabels has been sought.