Genetic programming for the prediction of insolvency in non-life insurance companies

Salcedo‐Sanz, Sancho; Fernández-Villacañas, J. L.; Vargas, María Jesús Segovia; Bousoño-Calzón, Carlos

doi:10.1016/j.cor.2003.08.015

Cited by 61 publications

(24 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the literature we can find a couple of hybrid approaches that combine GP with another method, such as rough sets [35] and neural networks [36]. Some authors have used GP on its own: Vieira and coauthors [37] used linear GP and have compared its performance to support vector machines and neural networks, finding that SVMs offer better performance on unbalanced datasets, while linear GP outperforms the other methods on balanced datasets; Lensberg et al [38] also used GP to predict bankruptcy on a database of Norwegian companies; Lee [39] presented an application of GP to the problem of predicting insolvency for electronic firms in Taiwan; Salcedo-Sanz et al [40] used GP for prediction of insolvency in non-life insurance companies, a particular case, and finally, grammatical evolution, a form of grammarbased genetic programming, was used in [41] to solve several financial problems, corporate failure prediction among them. In general, these methods try to minimize only the general error rate (except Vieira's, which takes into account type I and type II errors).…”

Section: Introductionmentioning

confidence: 98%

Comparing multiobjective evolutionary ensembles for minimizing type I and II errors for bankruptcy prediction

Alfaro-Cid

Valdivieso

Esparcia

et al. 2008

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

View full text Add to dashboard Cite

In many real world applications type I (false positive) and type II (false negative) errors have to be dealt with separately, which is a complex problem since an attempt to minimize one of them usually makes the other grow. In fact, a type of error can be more important than the other, and a trade-off that minimizes the most important error type must be reached. In the case of the bankruptcy prediction problem the error type II is of greater importance, being unable to identify that a company is at risk causes problems to creditors and slows down the taking of measures that may solve the problem. Despite the importance of type II errors, most bankruptcy prediction methods take into account only the global classification error. In this paper we propose and compare two methods to optimize both error types in classification: artificial neural networks and function trees ensembles created through multiobjective optimization. Since the multiobjective optimization process produces a set of equally optimal results (Pareto front) the classification of the test patterns in both cases is based on the non-dominated solutions acting as an ensemble. The experiments prove that, although the best classification rates are obtained using the artificial neural network, the multiobjective genetic programming model is able to generate comparable results in the form of an analytical function.

show abstract

Section: Introductionmentioning

confidence: 98%

Comparing multiobjective evolutionary ensembles for minimizing type I and II errors for bankruptcy prediction

Alfaro-Cid

Valdivieso

Esparcia

et al. 2008

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

View full text Add to dashboard Cite

show abstract

“…Mais recentemente, trabalhos como Segovia-Vargas et al (2004), Salcedo-Sanz et al (2005 e Hsiao & Whang (2009) apresentaram outras métricas para a estimação do risco de insolvência. Segovia-Vargas et al (2004) e Salcedo-Sanz et al (2005 proporam métricas aplicadasà companhias de seguro, cujas abordagens estão baseadas no mecanismo SVM (Support Vector Machine) proposto por Burges (1998) e em algoritimos genéricos, respectivamente.…”

Section: Introductionunclassified

RiD: Uma Nova Abordagem para o Cálculo do Risco de Insolvência

Sanfins

Monte-mor

2014

Braz. Rev. Fin.

View full text Add to dashboard Cite

Given the recent international crises and the increasing number of defaults, several researchers have attempted to develop metrics that calculate the probability of insolvency with higher accuracy. The approaches commonly used, however, do not consider the credit risk nor the severity of the distance between receivables and obligations among different periods. In this paper we mathematically present an approach that allow us to estimate the insolvency risk by considering not only future receivables and obligations, but the severity of the distance between them and the quality of the respective receivables. Using Monte Carlo simulations and hypothetical examples, we show that our metric is able to estimate the insolvency risk with high accuracy. Moreover, our results suggest that in the absence of a smooth distribution between receivables and obligations, there is a non-null insolvency risk even when the present value of receivables is larger than the present value of the obligations.

show abstract

“…Table 8 in Appendix A provides a brief description of the databases sorted in ascending order by the total number of samples (N ), and also reports the number of attributes (D) and the references to the papers that have conducted experiments over each database. In a significant number of studies, it is possible to observe that most data sets consist of a very small number of examples (N < 200), such as the case of the 160 electronics companies listed by the Taiwan Stock Exchange Corporation (Chen 2013), the Spanish non-life insurance database with 72 firms (Salcedo-Sanz et al 2005), the 118 bankrupt and non-bankrupt examples of Greek industries (Tsakonas et al 2006), the database of Jordanian commercial banks with 140 personal loan applications (Eletter et al 2010), or the financial data collected from 105 small companies in Romania (Cimpoeru 2011). Figure 3 shows the number of papers per year as a function of the size of the data sets used in the experiments.…”

Section: Data Set Sizementioning

confidence: 99%

An insight into the experimental design for credit risk and corporate bankruptcy prediction systems

2014

View full text Add to dashboard Cite

Over the last years, it has been observed an increasing interest of the finance and business communities in any application tool related to the prediction of credit and bankruptcy risk, probably due to the need of more robust decision-making systems capable of managing and analyzing complex data. As a result, plentiful techniques have been developed with the aim of producing accurate prediction models that are able to tackle these issues. However, the design of experiments to assess and compare these models has attracted little attention so far, even though it plays an important role in validating and supporting the theoretical evidence of performance. The experimental design should be done carefully for the results to hold significance; otherwise, it might be a potential source of misleading and contradictory conclusions about the benefits of using a particular prediction system. In this work, we review more than 140 papers published in refereed journals within the period 2000-2013, putting the emphasis on the bases of the experimental design in credit scoring and bankruptcy prediction applications. We provide some caveats and guidelines for the usage of databases, data splitting methods, performance evaluation metrics and hypothesis testing procedures in order to converge on a systematic, consistent validation standard.

show abstract

Genetic programming for the prediction of insolvency in non-life insurance companies

Cited by 61 publications

References 10 publications

Comparing multiobjective evolutionary ensembles for minimizing type I and II errors for bankruptcy prediction

Comparing multiobjective evolutionary ensembles for minimizing type I and II errors for bankruptcy prediction

RiD: Uma Nova Abordagem para o Cálculo do Risco de Insolvência

An insight into the experimental design for credit risk and corporate bankruptcy prediction systems

Contact Info

Product

Resources

About