An improved boosting based on feature selection for corporate bankruptcy prediction

Highlights

• There is no overall best method has been used in predicting corporate bankruptcy.

• A new and improved Boosting, FS-Boosting, is proposed to predict corporate bankruptcy.

• Two datasets are selected to demonstrate the effectiveness and feasibility of FS-Boosting.

• Experimental results reveal that FS-Boosting could be used as an alternative method.

Abstract

With the recent financial crisis and European debt crisis, corporate bankruptcy prediction has become an increasingly important issue for financial institutions. Many statistical and intelligent methods have been proposed, however, there is no overall best method has been used in predicting corporate bankruptcy. Recent studies suggest ensemble learning methods may have potential applicability in corporate bankruptcy prediction. In this paper, a new and improved Boosting, FS-Boosting, is proposed to predict corporate bankruptcy. Through injecting feature selection strategy into Boosting, FS-Booting can get better performance as base learners in FS-Boosting could get more accuracy and diversity. For the testing and illustration purposes, two real world bankruptcy datasets were selected to demonstrate the effectiveness and feasibility of FS-Boosting. Experimental results reveal that FS-Boosting could be used as an alternative method for the corporate bankruptcy prediction.

Introduction

Predicting corporation bankruptcy is an important management science problem and its main goal is to differentiate those corporations with a probability of distress from healthy corporations. Moreover, incorrect decision-making in financial institutions may run into financial difficulty or distress and cause many social costs affecting owners or shareholders, managers, government, etc. As a result, how to predict corporate bankruptcy has become a hot topic for both industrial application and academic research (Li et al., 2012, Olson et al., 2012, Zhou et al., 2014).

As there are no mature theories of corporate bankruptcy, studies in corporate bankruptcy have largely been based on trial and error iterative processes of selecting features and predictive models (Li and Sun, 2009, Zhou et al., 2014). With the development of statistics, artificial intelligence (AI), some statistical methods and intelligent methods have been proposed for corporate bankruptcy prediction. The statistical methods applied in corporate bankruptcy prediction mainly include Linear Discriminant Analysis (LDA), Multivariate Discriminant Analysis (MDA), Quadratic Discriminant Analysis (QDA), Logistic Regression Analysis (LRA), and Factor Analysis (FA) (Li and Sun, 2009, Zmijewski, 1984). However, the problem with applying these statistical techniques to corporate bankruptcy prediction is that some assumptions, such as the multivariate normality assumptions for independent variables, are frequently violated in the practice, which makes these techniques theoretically invalid for finite samples (Shin & Lee, 2002). In recent years, many studies have demonstrated that intelligent techniques such as Artificial Neural Networks (ANN), Decision Tree (DT), Case-Based Reasoning (CBR), Support Vector Machine (SVM) can be used as alternative methods for corporate bankruptcy prediction (Olson et al., 2012, Tsai and Wu, 2008). In contrast with statistical techniques, intelligent techniques do not assume certain data distributions and automatically extract knowledge from training samples (Wang, Ma, Huang, & Xu, 2012).

However, there is still no overall best intelligent methods used in predicting corporate bankruptcy. The performance of prediction depends on the details of the problem, the data structure, the used characteristics, the extent to which it is possible to segregate the classes by using those characteristics, and the objective of the classification (Duéñez-Guzmán & Vose, 2013). Recently, integrating multiple predictors into an aggregated output, i.e. ensemble methods, has been demonstrated to be an efficient strategy for achieving high prediction performance, especially when the component predictors have different structures that lead to independent prediction errors (Breiman, 1996, Polikar, 2006). Moreover, latest studies have shown that such ensemble techniques have performed better than single intelligent technique in financial distress prediction (Deligianni and Kotsiantis, 2012, Sun and Li, 2012). In this paper, a novel and improved Boosting, FS-Boosting, is proposed to predict corporate bankruptcy. Through injecting feature selection strategy into Boosting, FS-Booting can get better performance as base learners in FS-Boosting could get more accuracy and diversity. For the testing and illustration purposes, two real world bankruptcy datasets were selected to demonstrate the effectiveness and feasibility of FS-Boosting. Among eight methods, FS-Boosting gets the best prediction accuracy on two datasets. Experimental results reveal that FS-Boosting can be used as an alternative method for the corporate bankruptcy prediction.

The remainder of the paper is organized as follows. In Section 2, we review the related work of corporate bankruptcy prediction. In Section 3, an improved boosting, FS-Boosting, is proposed for corporate bankruptcy prediction. In Section 4, we present the details of experiment design. Sections 5 Experimental results, 6 Discussion summarize and analyze empirical results and discussion. Based on the results and observations of these experiments, Section 7 draws conclusions and future research directions.