Abstract
In this paper, we proposed a hybrid system to predict corporate bankruptcy. The whole procedure consists of the following four stages: first, sequential forward selection was used to extract the most important features; second, a rule-based model was chosen to fit the given dataset since it can present physical meaning; third, a genetic ant colony algorithm (GACA) was introduced; the fitness scaling strategy and the chaotic operator were incorporated with GACA, forming a new algorithm—fitness-scaling chaotic GACA (FSCGACA), which was used to seek the optimal parameters of the rule-based model; and finally, the stratifiedK-fold cross-validation technique was used to enhance the generalization of the model. Simulation experiments of 1000 corporations’ data collected from 2006 to 2009 demonstrated that the proposed model was effective. It selected the 5 most important factors as “net income to stock broker’s equality,” “quick ratio,” “retained earnings to total assets,” “stockholders’ equity to total assets,” and “financial expenses to sales.” The total misclassification error of the proposed FSCGACA was only 7.9%, exceeding the results of genetic algorithm (GA), ant colony algorithm (ACA), and GACA. The average computation time of the model is 2.02 s.
Highlights
Corporate bankruptcy is of great importance in economic phenomena
As genetic algorithm (GA) is well known to the readers, we discussed the basic concepts of ant colony algorithm (ACA) and genetic ant colony algorithm (GACA)
Combining the advantages of the algorithms, the GACA was proposed by Zhang and Wu [3], and it can be divided into two stages
Summary
Corporate bankruptcy is of great importance in economic phenomena. The health and success of the businesses are of widespread concern to policy makers, industry participants, investors, managers, and consumers [1]. The high individual, economic, and social costs as a consequence of corporate failures or bankruptcies have spurred searches for better understanding and prediction capabilities [4]. Bankruptcy prediction is the technique of predicting bankruptcy and various measures of financial distress of public firms [5]. It is a vast area of finance and accounting research. The quantity of research is a function of the availability of data; for public firms which went bankrupt or did not, numerous accounting ratios that might indicate danger can be calculated, and numerous other potential explanatory variables are available. Numerous methods have been developed for predicting bankruptcy. Research has shown that artificial intelligence such as feedforward neural networks (FNNs) can be an alternative methodology for classification problems to which traditional statistical methods have long been applied [9]
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