New evolutionary bankruptcy forecasting model based on genetic algorithms and neural networks
14 Nov 2005-pp 241-245
TL;DR: A new hybrid model (EBM: evolutionary bankruptcy model) based on genetic algorithms and artificial neural networks is proposed, which is able of selecting the best set of predictive variables and searching for the best neural network classifier and improving classification and generalization accuracies.
Abstract: One of the problem still gaining a great attention in finance is the bankruptcy forecasts. The problem of efficient bankruptcy prognosis is of great interest both to scientists and practitioners. Numerous models have been developed to forecast bankruptcy prediction from statistical models to artificial intelligence techniques. We propose, in this study, a new hybrid model (EBM: evolutionary bankruptcy model) based on genetic algorithms and artificial neural networks. Our evolutionary model is able of: selecting the best set of predictive variables, then, searching for the best neural network classifier and improving classification and generalization accuracies. Carried out experiments have shown a very promising results of EBM for bankruptcy prediction in terms of predictive accuracy and adaptability
Citations
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TL;DR: A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor (FKNN) method, where the neighborhood size k and the fuzzy strength parameter m are adaptively specified by the continuous particle swarm optimization (PSO) approach, that might serve as a new candidate of powerful early warning systems for bankruptcy prediction with excellent performance.
Abstract: Bankruptcy prediction is one of the most important issues in financial decision-making. Constructing effective corporate bankruptcy prediction models in time is essential to make companies or banks prevent bankruptcy. This study proposes a novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor (FKNN) method, where the neighborhood size k and the fuzzy strength parameter m are adaptively specified by the continuous particle swarm optimization (PSO) approach. In addition to performing the parameter optimization for FKNN, PSO is also utilized to choose the most discriminative subset of features for prediction. Adaptive control parameters including time-varying acceleration coefficients (TVAC) and time-varying inertia weight (TVIW) are employed to efficiently control the local and global search ability of PSO algorithm. Moreover, both the continuous and binary PSO are implemented in parallel on a multi-core platform. The proposed bankruptcy prediction model, named PTVPSO-FKNN, is compared with five other state-of-the-art classifiers on two real-life cases. The obtained results clearly confirm the superiority of the proposed model in terms of classification accuracy, Type I error, Type II error and area under the receiver operating characteristic curve (AUC) criterion. The proposed model also demonstrates its ability to identify the most discriminative financial ratios. Additionally, the proposed model has reduced a large amount of computational time owing to its parallel implementation. Promisingly, PTVPSO-FKNN might serve as a new candidate of powerful early warning systems for bankruptcy prediction with excellent performance.
158Â citations
01 Jul 2010
TL;DR: This paper presents a comprehensive review of hybrid and ensemble-based soft computing techniques applied to bankruptcy prediction, namely how different techniques are combined, but not on obtained results.
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.
156Â citations
TL;DR: A hybrid method for effective bankruptcy prediction is proposed, based on the combination of variable selection using decision trees and case-based reasoning using the Mahalanobis distance with variable weight, which outperforms some currently-in-use techniques.
Abstract: This paper proposes a hybrid method for effective bankruptcy prediction, based on the combination of variable selection using decision trees and case-based reasoning using the Mahalanobis distance with variable weight. Unlike the existing case-based reasoning methods using the Euclidean distance, we introduce the Mahalanobis distance in locating the nearest neighbors, which considers the covariance structure of variables in measuring the closeness. Since hundreds of financial ratio variables are available in analyzing credit management problems, the model performance is also affected by input variable selection strategies. Variables selected by the decision trees induction tend to have an interaction compared to those produced by the regression approaches. The Mahalanobis distance is a more true measure of proximity than the Euclidean distance when variables are correlated with each other. The experimental results indicate that the proposed approach outperforms some currently-in-use techniques.
102Â citations
01 Jul 2017
TL;DR: A new hybrid soft computing for bankruptcy prediction was proposed in which novel fitness function designs are presented for the GA based financial ratio selection and a fuzzy clustering algorithm was used for the classifier design.
Abstract: Display Omitted A survey of the related studies according to statistical, intelligent and hybrid approaches was given.A new hybrid soft computing for bankruptcy prediction was proposed in which novel fitness function designs are presented for the GA based financial ratio selection.A public database applied in many studies was adopted in the experiments.Comparison between the proposed financial selection method with other approaches was given.Comparison between the proposed classifier with the well-applied artificial NN approach was given. In the design of a financial bankruptcy prediction model, financial ratio selection and classifier design play major roles. Methodology based on expert opinion, statistical theory and computational intelligence technique has been widely applied. In this study, a hybrid structure integrating statistical theory and computational intelligence technique was developed using genetic algorithm (GA) with statistical measurements and fuzzy logic based fitness functions for key ratio selection. A fuzzy clustering algorithm was used for the classifier design. In the experiments, two financial ratio sets, one extracted from the suggestions of other studies and the other obtained by using the GA toolbox in the SAS statistical software package, were applied to examine the proposed ratio selection schemes. For classifier design, the developed fuzzy classifier was compared with the well known BPNN classifier frequently used in other studies. Besides, comparison between the developed hybrid structure and other well applied structures was also given. Experimental results based on one to four years of financial data prior to the occurrence of bankruptcy were used to evaluate the performance of the proposed prediction model.
100Â citations
TL;DR: An integrative model with subject weight based on neural network learning for bankruptcy prediction by combining multiple discriminant analysis, logistic regression, neural networks, and decision trees induction is introduced.
Abstract: This study proposes an integration strategy regarding how to efficiently combine the currently-in-use statistical and artificial intelligence techniques. In particular, by combining multiple discriminant analysis, logistic regression, neural networks, and decision trees induction, we introduce an integrative model with subject weight based on neural network learning for bankruptcy prediction. The strength of the proposed model stems from differentiating the weights of the source methods for each subject in the testing data set. That is, the relative weights consist of N by I matrix, where N denotes the number of subjects and I denotes the number of the source methods. The experiments using a real world financial data indicate that the proposed model can marginally increase the prediction accuracy compared to the source methods. The integration strategy can be useful for a dichotomous classification problem like bankruptcy prediction since prediction can be improved by taking advantage of existing and newly emerging techniques in the future.
66Â citations
References
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Book•
01 Sep 1988
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Abstract: From the Publisher:
This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields
Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required
52,797Â citations
TL;DR: Neural Evolution of Augmenting Topologies (NEAT) as mentioned in this paper employs a principled method of crossover of different topologies, protecting structural innovation using speciation, and incrementally growing from minimal structure.
Abstract: An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixed-topology method on a challenging benchmark reinforcement learning task. We claim that the increased efficiency is due to (1) employing a principled method of crossover of different topologies, (2) protecting structural innovation using speciation, and (3) incrementally growing from minimal structure. We test this claim through a series of ablation studies that demonstrate that each component is necessary to the system as a whole and to each other. What results is significantly faster learning. NEAT is also an important contribution to GAs because it shows how it is possible for evolution to both optimize and complexify solutions simultaneously, offering the possibility of evolving increasingly complex solutions over generations, and strengthening the analogy with biological evolution.
3,265Â citations
TL;DR: The results demonstrate that the accuracy and generalization performance of SVM is better than that of BPN as the training set size gets smaller, and the several superior points of the SVM algorithm compared with BPN are investigated.
Abstract: This study investigates the efficacy of applying support vector machines (SVM) to bankruptcy prediction problem. Although it is a well-known fact that the back-propagation neural network (BPN) performs well in pattern recognition tasks, the method has some limitations in that it is an art to find an appropriate model structure and optimal solution. Furthermore, loading as many of the training set as possible into the network is needed to search the weights of the network. On the other hand, since SVM captures geometric characteristics of feature space without deriving weights of networks from the training data, it is capable of extracting the optimal solution with the small training set size. In this study, we show that the proposed classifier of SVM approach outperforms BPN to the problem of corporate bankruptcy prediction. The results demonstrate that the accuracy and generalization performance of SVM is better than that of BPN as the training set size gets smaller. We also examine the effect of the variability in performance with respect to various values of parameters in SVM. In addition, we investigate and summarize the several superior points of the SVM algorithm compared with BPN.
728Â citations
"New evolutionary bankruptcy forecas..." refers background in this paper
...Shik in 2005 to predict Korean firms’ bankruptcy[12]....
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TL;DR: Inspired by one of the traditional credit risk models developed by Merton (1974), it is shown that the use of novel indicators for the NN system provides a significant improvement in the (out-of-sample) prediction accuracy.
Abstract: The prediction of corporate bankruptcies is an important and widely studied topic since it can have significant impact on bank lending decisions and profitability. This work presents two contributions. First we review the topic of bankruptcy prediction, with emphasis on neural-network (NN) models. Second, we develop an NN bankruptcy prediction model. Inspired by one of the traditional credit risk models developed by Merton (1974), we propose novel indicators for the NN system. We show that the use of these indicators in addition to traditional financial ratio indicators provides a significant improvement in the (out-of-sample) prediction accuracy (from 81.46% to 85.5% for a three-year-ahead forecast).
667Â citations
"New evolutionary bankruptcy forecas..." refers background in this paper
...Numerous studies have demonstrated that neural networks can be an alternative methodology for classification problem to which statistical methods have long been applied [1, 3, 9]....
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...The problem of efficient bankruptcy prognosis is of great interest both to scientists and practitioners[1]....
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TL;DR: The preliminary results show that rule extraction approach using GAs for bankruptcy prediction modeling is promising and is capable of extracting rules that are easy to understand for users like expert systems.
Abstract: Prediction of corporate failure using past financial data is a well-documented topic. Early studies of bankruptcy prediction used statistical techniques such as multiple discriminant analysis, logit and probit. Recently, however, numerous studies have demonstrated that artificial intelligence such as neural networks (NNs) can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. Although numerous theoretical and experimental studies reported the usefulness of NNs in classification studies, there exists a major drawback in building and using the model. That is, the user cannot readily comprehend the final rules that the NN models acquire. We propose a genetic algorithms (GAs) approach in this study and illustrate how GAs can be applied to bankruptcy prediction modeling. An advantage of present approach using GAs is that it is capable of extracting rules that are easy to understand for users like expert systems. The preliminary results show that rule extraction approach using GAs for bankruptcy prediction modeling is promising.
381Â citations
"New evolutionary bankruptcy forecas..." refers background or methods in this paper
...In the literature, different models are suitable to the bankruptcy prediction problem from statistical models[7, 5] to artificial intelligence techniques[13, 8, 10]....
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...The proposed models for bankruptcy prediction confirm unanimously the growing interest and the superiority of Artificial neural networks approaches over statistical techniques[13]....
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