Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey

Abstract

This paper presents a comprehensive review of hybrid and ensemble-based soft computing techniques applied to bankruptcy prediction. A variety of soft computing techniques are being applied to bankruptcy prediction. Our focus is on techniques, namely how different techniques are combined, but not on obtained results. Almost all authors demonstrate that the technique they propose outperforms some other methods chosen for the comparison. However, due to different data sets used by different authors and bearing in mind the fact that confidence intervals for the prediction accuracies are seldom provided, fair comparison of results obtained by different authors is hardly possible. Simulations covering a large variety of techniques and data sets are needed for a fair comparison. We call a technique hybrid if several soft computing approaches are applied in the analysis and only one predictor is used to make the final prediction. In contrast, outputs of several predictors are combined, to obtain an ensemble-based prediction.

Chinese companies distress prediction: an application of data envelopment analysis

“…Beaver (1966) was the first author to introduce financial ratios into bankruptcy prediction. In recent decades there have been a great number of bankruptcy prediction studies based on financial ratios using different statistical and machine-learning techniques, these are reviewed in Altman (1993), Balcaen and Ooghe (2006), Kumar and Ravi (2007), Bahrammirzaee (2010), Verikas et al (2010). Recent papers (e.g.…”

An insight into the experimental design for credit risk and corporate bankruptcy prediction systems

“…Ravi Kumar and Ravi (2007) present an extensive analysis of statistical and intelligent methods applied to the prediction of corporate bankruptcy risk in the period , highlighting the source of data, financial ratios and country of origin. Verikas et al (2010) focus their review on how to combine different soft computing techniques to derive hybrid and ensemble-based bankruptcy prediction models. Lin et al (2012) provide a statistical survey of machine learning papers published between 1995 and 2010 in the realm of credit scoring and bankruptcy prediction, summing up the data sets and comparing the performance of several methods with baseline classifiers.…”

Ensemble Predictions of Recovery Rates

“…However, these studies focused on bankruptcy prediction and credit scoring. The reader is referred to Verikas et al (2010) for a survey of the literature. In the following sections, I explore the effectiveness of an ensemble strategy for forecasting recovery rates using data from Moody's Ultimate Recovery Database.…”