Bankruptcy prediction with neural logic networks by means of grammar-guided genetic programming

Tsakonas, Athanasios; Dounias, Georgios; Doumpos, Michael; Zopounidis, Constantin

doi:10.1016/j.eswa.2005.10.009

Cited by 82 publications

(27 citation statements)

References 25 publications

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“…They concluded that in the ANN option-pricing model, the Grey-GJR-GARCH volatility provides higher predictability than other volatility approaches. Other examples of this type of comparisons are done by [220][221][222][223][224][225][226][227][228][229][230][231]. Table 16 presents the brief results of these comparisons.…”

Section: Financial Prediction and Planningmentioning

confidence: 98%

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Bahrammirzaee

2010

Neural Comput & Applic

442

192

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Nowadays, many current real financial applications have nonlinear and uncertain behaviors which change across the time. Therefore, the need to solve highly nonlinear, time variant problems has been growing rapidly. These problems along with other problems of traditional models caused growing interest in artificial intelligent techniques. In this paper, comparative research review of three famous artificial intelligence techniques, i.e., artificial neural networks, expert systems and hybrid intelligence systems, in financial market has been done. A financial market also has been categorized on three domains: credit evaluation, portfolio management and financial prediction and planning. For each technique, most famous and especially recent researches have been discussed in comparative aspect. Results show that accuracy of these artificial intelligent methods is superior to that of traditional statistical methods in dealing with financial problems, especially regarding nonlinear patterns. However, this outperformance is not absolute.

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Section: Financial Prediction and Planningmentioning

confidence: 98%

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Bahrammirzaee

2010

Neural Comput & Applic

442

192

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“…When testing on one data set, the authors have found that the classifier was more accurate than SVM or MLP. Tsakonas et al (2006) used GA to evolve a bankruptcy prediction system based on the so-called neural logic networks. An elementary neural logic network consists of a set of input nodes and an output node.…”

Section: Genetic Algorithms In Hybrid Techniquesmentioning

confidence: 99%

Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey

et al. 2009

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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.

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“…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

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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.

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Bankruptcy prediction with neural logic networks by means of grammar-guided genetic programming

Cited by 82 publications

References 25 publications

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey

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

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