João Carvalho das Neves

Improving bankruptcy prediction with Hidden Layer Learning Vector Quantization

Abstract A Hidden Layer Learning Vector Quantization (HLVQ), neural network-learning algorithm is used for correcting the outputs of Multilayer Perceptrons (MLP) for predicting corporate bankruptcy. We call this method HLVQ-C, and it is shown that it outperforms both discriminant analysis and traditional neural networks while significantly reducing type I error, which is the type of error that has the highest costs for banks. Moreover, our approach gives an estimation of the prediction robustness thus providing a useful measure of credit risk, which is of great interest for banks, insurance companies and creditors in general. We also show that unbalanced samples, containing more financially sound firms than bankrupt firms, place a strong bias on the classifiers thus leading to a deterioration of type I error accuracy. Although many studies have been published on bankruptcy prediction using neural networks or discriminant analysis, they used mainly US or UK samples of very limited size. Our study is based on industrial French firms, uses a data-set of 583 bankrupt firms over the period 1998–2000 and tests the effects of different proportions of non-bankrupt firms in the sample. Attention was also given to feature selection to reduce the dimensionality of the problem.

Using data envelopment analysis to select strategies that improve the performance of hotel companies

Purpose – The aim of this paper is to illustrate the value of data envelopment analysis (DEA) for strategic analysis and performance management in the hotel industry.Design/methodology/approach – The paper uses a world‐wide sample of hotel companies and two cases to illustrate how DEA can be used to develop strategic guidelines to improve organizational performance.Findings – The study shows that DEA can be used for strategic design and performance management through the analysis of two cases. Additionally, for the sample of 83 hotel companies, there are three main conclusions: a focused strategy performs better than a diversification strategy; for the bulk of the sample, the scale efficiency is higher than the pure technical efficiency, hence hotel managers should concentrate on productivity improvements (that is how to transform inputs into outputs) and not on scale issues (such as increases or decreases in the size of operations); and the majority of the hotel companies in the sample are operating unde...

A genetic algorithm-based approach to cost-sensitive bankruptcy prediction

The prediction of bankruptcy is of significant importance with the present-day increase of bankrupt companies. In the practical applications, the cost of misclassification is worthy of consideration in the modeling in order to make accurate and desirable decisions. An effective prediction system requires the integration of the cost preference into the construction and optimization of prediction models. This paper presents an evolutionary approach for optimizing simultaneously the complexity and the weights of learning vector quantization network under the symmetric cost preference. Experimental evidences on a real-world data set demonstrate the proposed algorithm leads to significant reduction of features without the degradation of prediction capability.

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.

Learning manifolds for bankruptcy analysis

We apply manifold learning to a real data set of distressed and healthy companies for proper geometric tunning of similarity data points and visualization. While Isomap algorithm is often used in unsupervised learning our approach combines this algorithm with information of class labels for bankruptcy prediction. We compare prediction results with classifiers such as Support Vector Machines (SVM), Relevance Vector Machines (RVM) and the simple k-Nearest Neighbor (KNN) in the same data set and we show comparable accuracy of the proposed approach.

A stable credit rating model based on learning vector quantization

Credit rating is involved in many financial applications to estimate the creditworthiness of corporations or individuals. In addition to building accurate credit rating models, the stability of models is of significant importance to economic performance. In this work we propose a methodology based on learning vector quantization (LVQ) to develop a credit rating model. This model is applied to a French database of private companies over a period of several years. LVQ is trained and calibrated in a supervised way using data from 2006 and then applied to the remaining years. We analyze one year transition matrix and show that the model is capable to create robust and stable classes to rank companies.

Financial distress model prediction using SVM

Financial distress prediction is of great importance to all stakeholders in order to enable better decision-making in evaluating firms. In recent years, the rate of bankruptcy has risen and it is becoming harder to estimate as companies become more complex and the asymmetric information between banks and firms increases. Although a great variety of techniques have been applied along the years, no comprehensive method incorporating an holistic perspective had hitherto been considered. Recently, SVM+ a technique proposed by Vapnik [17] provides a formal way to incorporate privileged information onto the learning models improving generalization. By exploiting additional information to improve traditional inductive learning we propose a prediction model where data is naturally separated into several groups according to the size of the firm. Experimental results in the setting of a heterogeneous data set of French companies demonstrated that the proposed model showed superior performance in terms of prediction accuracy in bankruptcy prediction and misclassification cost.

On the performance of learning machines for bankruptcy detection

Predicting the financial health of companies is a problem of great importance to various stakeholders in the increasingly globalized economy. We apply several learning machines methods to the problem of bankruptcy prediction of private companies. Financial data obtained from Diana, a database containing 780,000 financial statements of French companies, are used to perform experiments. Classification accuracy is evaluated with respect to artificial neural networks, linear genetic programming and support vector machines. We analyze both type I (bankrupted companies misclassified as healthy) and type II (healthy companies misclassified as bankrupted) errors on three datasets containing balanced and unbalanced class distribution. Linear genetic programming has the best accuracy in the balanced data while support vector machines is more stable for the unbalanced dataset. Our results, though preliminary in nature, demonstrate the tremendous potential of using learning machines in solving important economics problems such as predicting bankruptcy with accuracy

Cost-Sensitive Learning Vector Quantization for Financial Distress Prediction

Financial distress prediction is of crucial importance in credit risk analysis with the increasing competition and complexity of credit industry. Although a variety of methods have been applied in this field, there are still some problems remained. The accurate and sensitive prediction in presence of unequal misclassification costs is an important one. Learning vector quantization (LVQ) is a powerful tool to solve financial distress prediction problem as a classification task. In this paper, a cost-sensitive version of LVQ is proposed which incorporates the cost information in the model. Experiments on two real data sets show the proposed approach is effective to improve the predictive capability in cost-sensitive situation.

Accurate prediction of financial distress of companies with machine learning algorithms

Prediction of financial distress of companies is analyzed with several machine learning approaches. We used Diane, a large database containing financial records from small and medium size French companies, from the year of 2002 up to 2007. It is shown that inclusion of historical data, up to 3 years priori to the analysis, increases the prediction accuracy and that Support Vector Machines are the most accurate predictor.