Prediction of corporate financial distress: an application of the America banking industry

Financial distress prediction is an important and widely researched issue because of its potential significant influence on bank lending decisions and profitability. Since the 1970s, many mathematical and statistical researchers have proposed prediction models on such issues. Given the recent vigorous growth of artificial intelligence (AI) and data mining techniques, many researchers have begun to apply those techniques to the problem of bankruptcy prediction. Among these techniques, the support vector machine (SVM) has been applied successfully and obtained good performance with other AI and statistical method comparisons. Particle swarm optimization (PSO) has been increasingly employed in conjunction with AI techniques and has provided reliable optimization capability. However, researches addressing PSO and SVM integration are scarce, although there is great potential for useful applications in this field. This paper proposes an adaptive inertia weight (AIW) method for improving PSO performance and integrates SVM in two aspects: feature subset selection and parameter optimization. The experiments collected 54 listed companies as initial samples from American bank datasets. The proposed adaptive PSO-SVM approach could be a more suitable methodology for predicting potential financial distress. This approach also proves its capability to handle scalable and non-scalable function problems.     

Personal bankruptcy prediction by mining credit card data

A personal bankruptcy prediction system running on credit card data is proposed. Personal bankruptcy, which usually results in significant losses to creditors, is a rapidly increasing yet little understood phenomenon. The most commonly used methods in personal bankruptcy prediction are credit scoring models. Some data mining models have also been investigated in this domain. Neither the scoring models nor the existing data mining methods adequately take sequence information in credit card data into account. In our system, sequence patterns, obtained by developing sequence mining techniques and applying them to credit card data from one major Canadian bank, are employed as main predictors. The mined sequence patterns, which we refer to as bankruptcy features, are represented in low-dimensional vector space. From the new feature space, which can be extended with some existing prediction-capable features (e.g., credit score), a support vector machine (SVM) classifier is built to combine these mined and already existing features. Our system is readily comprehensible and demonstrates promising prediction performance.     

Parameter determination of support vector machine and feature selection using simulated annealing approach

Support vector machine (SVM) is a novel pattern classification method that is valuable in many applications. Kernel parameter setting in the SVM training process, along with the feature selection, significantly affects classification accuracy. The objective of this study is to obtain the better parameter values while also finding a subset of features that does not degrade the SVM classification accuracy. This study develops a simulated annealing (SA) approach for parameter determination and feature selection in the SVM, termed SA-SVM.To measure the proposed SA-SVM approach, several datasets in UCI machine learning repository are adopted to calculate the classification accuracy rate. The proposed approach was compared with grid search which is a conventional method of performing parameter setting, and various other methods. Experimental results indicate that the classification accuracy rates of the proposed approach exceed those of grid search and other approaches. The SA-SVM is thus useful for parameter determination and feature selection in the SVM.     

Applying enhanced data mining approaches in predicting bank performance: A case of Taiwanese commercial banks

The prediction of bank performance is an important issue. The bad performance of banks may first result in bankruptcy, which is expected to influence the economics of the country eventually. Since the early 1970s, many researchers had already made predictions on such issues. However, until recent years, most of them have used traditional statistics to build the prediction model. Because of the vigorous development of data mining techniques, many researchers have begun to apply those techniques to various fields, including performance prediction systems. However, data mining techniques have the problem of parameter settings. Therefore, this study applies particle swarm optimization (PSO) to obtain suitable parameter settings for support vector machine (SVM) and decision tree (DT), and to select a subset of beneficial features, without reducing the classification accuracy rate. In order to evaluate the proposed approaches, dataset collected from Taiwanese commercial banks are used as source data. The experimental results showed that the proposed approaches could obtain a better parameter setting, reduce unnecessary features, and improve the accuracy of classification significantly.     

Enhanced default risk models with SVM+

Default risk models have lately raised a great interest due to the recent world economic crisis. In spite of many advanced techniques that have extensively been proposed, no comprehensive method incorporating a holistic perspective has hitherto been considered. Thus, the existing models for bankruptcy prediction lack the whole coverage of contextual knowledge which may prevent the decision makers such as investors and financial analysts to take the right decisions. Recently, SVM+ provides a formal way to incorporate additional information (not only training data) onto the learning models improving generalization. In financial settings examples of such non-financial (though relevant) information are marketing reports, competitors landscape, economic environment, customers screening, industry trends, etc. By exploiting additional information able to improve classical inductive learning we propose a prediction model where data is naturally separated into several structured groups clustered by the size and annual turnover of the firms. Experimental results in the setting of a heterogeneous data set of French companies demonstrated that the proposed default risk model showed better predictability performance than the baseline SVM and multi-task learning with SVM.     

A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor method

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.     

Using partial least squares and support vector machines for bankruptcy prediction

The evaluation of corporate financial distress has attracted significant global attention as a result of the increasing number of worldwide corporate failures. There is an immediate and compelling need for more effective financial distress prediction models. This paper presents a novel method to predict bankruptcy. The proposed method combines the partial least squares (PLS) based feature selection with support vector machine (SVM) for information fusion. PLS can successfully identify the complex nonlinearity and correlations among the financial indicators. The experimental results demonstrate its superior predictive ability. On the one hand, the proposed model can select the most relevant financial indicators to predict bankruptcy and at the same time identify the role of each variable in the prediction process. On the other hand, the proposed model’s high levels of prediction accuracy can translate into benefits to financial organizations through such activities as credit approval, and loan portfolio and security management.     

An improved boosting based on feature selection for corporate bankruptcy prediction

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.     

Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patients

This paper proposes a modified binary particle swarm optimization (MBPSO) method for feature selection with the simultaneous optimization of SVM kernel parameter setting, applied to mortality prediction in septic patients. An enhanced version of binary particle swarm optimization, designed to cope with premature convergence of the BPSO algorithm is proposed. MBPSO control the swarm variability using the velocity and the similarity between best swarm solutions. This paper uses support vector machines in a wrapper approach, where the kernel parameters are optimized at the same time. The approach is applied to predict the outcome (survived or deceased) of patients with septic shock. Further, MBPSO is tested in several benchmark datasets and is compared with other PSO based algorithms and genetic algorithms (GA). The experimental results showed that the proposed approach can correctly select the discriminating input features and also achieve high classification accuracy, specially when compared to other PSO based algorithms. When compared to GA, MBPSO is similar in terms of accuracy, but the subset solutions have less selected features.     

Automatic detection of erythemato-squamous diseases using PSO–SVM based on association rules

In this paper, we develop a diagnosis model based on particle swarm optimization (PSO), support vector machines (SVMs) and association rules (ARs) to diagnose erythemato-squamous diseases. The proposed model consists of two stages: first, AR is used to select the optimal feature subset from the original feature set; then a PSO based approach for parameter determination of SVM is developed to find the best parameters of kernel function (based on the fact that kernel parameter setting in the SVM training procedure significantly influences the classification accuracy, and PSO is a promising tool for global searching). Experimental results show that the proposed AR_PSO–SVM model achieves 98.91% classification accuracy using 24 features of the erythemato-squamous diseases dataset taken from UCI (University of California at Irvine) machine learning database. Therefore, we can conclude that our proposed method is very promising compared to the previously reported results.     

A distributed PSO–SVM hybrid system with feature selection and parameter optimization

This study proposed a novel PSO–SVM model that hybridized the particle swarm optimization (PSO) and support vector machines (SVM) to improve the classification accuracy with a small and appropriate feature subset. This optimization mechanism combined the discrete PSO with the continuous-valued PSO to simultaneously optimize the input feature subset selection and the SVM kernel parameter setting. The hybrid PSO–SVM data mining system was implemented via a distributed architecture using the web service technology to reduce the computational time. In a heterogeneous computing environment, the PSO optimization was performed on the application server and the SVM model was trained on the client (agent) computer. The experimental results showed the proposed approach can correctly select the discriminating input features and also achieve high classification accuracy.     

HYBRID GENETIC PROGRAMMING-BASED SEARCH ALGORITHMS FOR ENTERPRISE BANKRUPTCY PREDICTION

Bankruptcy is an extremely significant worldwide problem that affects the economic well- being of all countries. The high social costs incurred by various stakeholders associated with bankrupt firms imply the need to search for better theoretical understanding and prediction quality. The main objective of this paper is to apply genetic programming with orthogonal least squares (GP/OLS) and with simulated annealing (GP/SA) algorithms to build models for bankruptcy prediction. Utilizing the hybrid GP/OLS and GP/SA techniques, generalized relationships are obtained to classify samples of 136 bankrupt and nonbankrupt Iranian corporations based on financial ratios. Another important contribution of this paper is to identify the effective predictive financial ratios based on an extensive bankruptcy prediction literature review and a sequential feature selection (SFS) analysis. A comparative study on the classification accuracy of the GP/OLS- and GP/SA-based models is also conducted. The observed agreement between the predictions and the actual values indicates that the proposed models effectively estimate any enterprise with regard to the aspect of bankruptcy. According to the results, the proposed GP/SA model has better performance than the GP/OLS model in bankruptcy prediction.     

Data depth based support vector machines for predicting corporate bankruptcy

In financial distress analysis, the diagnosis of firms at risk for bankruptcy is crucial in preparing to hedge against any financial damage the at-risk firms stand to inflict. Some pre-alarm signals that indicate a potential financial crisis exist when a firm faces a default risk. Early studies on corporate bankruptcy prediction include parametric and nonparametric approaches, such as artificial intelligence (AI), for detecting pre-alarm signals. Among nonparametric techniques, the methods involving support vector machine (SVM) have shown potential in predicting corporate bankruptcy. We propose a hybrid method that combines data depths and nonlinear SVM for the prediction of corporate bankruptcy. We employed data depth functions to condense multivariate financial data with nonlinear and non-normal characteristics into one-dimensional space. The SVM method was introduced to classify the data points on a depth versus depth plot (DD-plot). Based on data set that records failed and non-failed manufacturing firms in Korea over 10 years, the empirical results demonstrated that the proposed method offers a higher level of accuracy in corporate bankruptcy prediction than existing methods. The proposed method is expected to provide a guidance in corporate investing for investors or other interested parties.     

An iterative algorithm for simulation error based identification of polynomial input–output models using multi-step prediction

Effective identification of polynomial input–output models for applications requiring long-range prediction or simulation performance relies on both careful model selection and accurate parameter estimation. The simulation error minimisation (SEM) approach has been shown to provide significant advantages in the model selection phase by ruling out candidate models with good short-term prediction capabilities but unsuitable long-term dynamics. However, SEM-based parameter estimation has been generally avoided due to excessive computational effort. This article extends to the nonlinear case a computationally efficient approach for this task, that was previously developed for linear models, based on the iterative estimation of predictors with increasing prediction horizon. Conditions for the applicability of the approach to various model classes are also discussed. Finally, some examples are provided to show the effectiveness and computational convenience of the proposed algorithm for polynomial input–output identification, as well as the improvements achievable by enforcing SEM parameter estimation. A benchmark for nonlinear identification is also analysed, with encouraging results.     

Predicting business failure using support vector machines with straightforward wrapper: A re-sampling study

Business failure prediction (BFP) is an effective tool to help financial institutions and relevant people to make the right decision in investments, especially in the current competitive environment. This topic belongs to a classification-type task, one of whose aims is to generate more accurate hit ratios. Support vector machine (SVM) is a statistical learning technique, whose advantage is its high generalization performance. The objective of this context is threefold. Firstly, SVM is used to predict business failure by utilizing a straightforward wrapper approach to help the model produce more accurate prediction. The wrapper approach is fulfilled by employing a forward feature selection method, composed of feature ranking and feature selection. Meanwhile, this work attempts to investigate the feasibility of using linear SVMs to select features for all SVMs in the wrapper since non-linear SVMs yield to over-fit the data. Finally, a robust re-sampling approach is used to evaluate model performances for the task of BFP in China. In the empirical research, performances of linear SVM, polynomial SVM, Gaussian SVM, and sigmoid SVM with the best filter of stepwise MDA, and wrappers respectively using linear SVM and non-linear SVMs as evaluating functions are to be compared. The results indicate that the non-linear SVM with radial basis function kernel and features selected by linear SVM compare significantly superiorly to all the other SVMs. Meanwhile, all SVMs with features selected by linear SVM produce at least as good performances as SVMs with other optimal features.     

An application of support vector machines in bankruptcy prediction model

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.     

A support vector machine based MSM model for financial short-term volatility forecasting

Financial time series forecasting has become a challenge because of its long-memory, thick tails and volatility persistence. Multifractal process has recently been proposed as a new formalism for this problem. An iterative Markov-Switching Multifractal (MSM) model was introduced to the literature. It is able to capture many of the important stylized features of the financial time series, including long-memory in volatility, volatility clustering, and return outliers. The model delivers stronger performance both in- and out-of-sample than GARCH-type models in long-term forecasts. To enhance MSM’s short-term prediction accuracy, this paper proposes a support vector machine (SVM) based MSM approach which exploits MSM model to forecast volatility and SVM to model the innovations. To verify the effectiveness of the proposed approach, two stock indexes in the Chinese A-share market are chosen as the forecasting targets. Comparing with some existing state-of-the-art models, the proposed approach gives superior results. It indicates that the proposed model provides a promising alternative to financial short-term volatility prediction.     

Using support vector machine with a hybrid feature selection method to the stock trend prediction

In this paper, we developed a prediction model based on support vector machine (SVM) with a hybrid feature selection method to predict the trend of stock markets. This proposed hybrid feature selection method, named F-score and Supported Sequential Forward Search (F_SSFS), combines the advantages of filter methods and wrapper methods to select the optimal feature subset from original feature set. To evaluate the prediction accuracy of this SVM-based model combined with F_SSFS, we compare its performance with back-propagation neural network (BPNN) along with three commonly used feature selection methods including Information gain, Symmetrical uncertainty, and Correlation-based feature selection via paired t-test. The grid-search technique using 5-fold cross-validation is used to find out the best parameter value of kernel function of SVM. In this study, we show that SVM outperforms BPN to the problem of stock trend prediction. In addition, our experimental results show that the proposed SVM-based model combined with F_SSFS has the highest level of accuracies and generalization performance in comparison with the other three feature selection methods. With these results, we claim that SVM combined with F_SSFS can serve as a promising addition to the existing stock trend prediction methods.     

基于均匀设计的支持向量机参数优化方法

在实际应用中,支持向量机的性能依赖于参数的选择。针对支持向量机的参数选择问题进行了研究和分析,提出了基于均匀设计的支持向量机参数优化方法。与基于网格搜索、粒子群算法、遗传算法等支持向量机参数优化方法进行了比较与分析,采用多个不同规模的标准的分类数据集进行测试,比较了四种方法的分类正确率和运行时间。仿真实验表明,四种方法都能找到最优参数,使支持向量机的分类正确率接近或超过分类数据集的理论精度,本文方法具有寻参时间短的特点。     

Joint feature re-extraction and classification using an iterative semi-supervised support vector machine algorithm

The focus of this paper is on joint feature re-extraction and classification in cases when the training data set is small. An iterative semi-supervised support vector machine (SVM) algorithm is proposed, where each iteration consists both feature re-extraction and classification, and the feature re-extraction is based on the classification results from the previous iteration. Feature extraction is first discussed in the framework of Rayleigh coefficient maximization. The effectiveness of common spatial pattern (CSP) feature, which is commonly used in Electroencephalogram (EEG) data analysis and EEG-based brain computer interfaces (BCIs), can be explained by Rayleigh coefficient maximization. Two other features are also defined using the Rayleigh coefficient. These features are effective for discriminating two classes with different means or different variances. If we extract features based on Rayleigh coefficient maximization, a large training data set with labels is required in general; otherwise, the extracted features are not reliable. Thus we present an iterative semi-supervised SVM algorithm embedded with feature re-extraction. This iterative algorithm can be used to extract these three features reliably and perform classification simultaneously in cases where the training data set is small. Each iteration is composed of two main steps: (i) the training data set is updated/augmented using unlabeled test data with their predicted labels; features are re-extracted based on the augmented training data set. (ii) The re-extracted features are classified by a standard SVM. Regarding parameter setting and model selection of our algorithm, we also propose a semi-supervised learning-based method using the Rayleigh coefficient, in which both training data and test data are used. This method is suitable when cross-validation model selection may not work for small training data set. Finally, the results of data analysis are presented to demonstrate the validity of our approach. Editor: Olivier Chapelle.