Bankruptcy prediction using connectionist and symbolic learning algorithms

Martineli, Edmar; Diniz, Helio; Carvalho, André C. P. L. F. de; Rezende, Solange Oliveira

doi:10.1109/ijcnn.1998.682276

Cited by 7 publications

(6 citation statements)

References 8 publications

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“…Since these early works there has been a large number of works based on machine-learning techniques [15,17,20]. The most successful have been based on decision trees [11,14,16,23] and neural networks [3,6,10,18,22]. Typically, all these works use different datasets and different sets of features, depending on the dataset.…”

Section: Related Workmentioning

confidence: 99%

Firms Default Prediction with Machine Learning

Aliaj

Anagnostopoulos

Piersanti

2020

Mining Data for Financial Applications

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Academics and practitioners have studied over the years models for predicting firms bankruptcy, using statistical and machinelearning approaches. An earlier sign that a company has financial difficulties and may eventually bankrupt is going in default, which, loosely speaking means that the company has been having difficulties in repaying its loans towards the banking system. Firms default status is not technically a failure but is very relevant for bank lending policies and often anticipates the failure of the company. Our study uses, for the first time according to our knowledge, a very large database of granular credit data from the Italian Central Credit Register of Bank of Italy that contain information on all Italian companies' past behavior towards the entire Italian banking system to predict their default using machine-learning techniques. Furthermore, we combine these data with other information regarding companies' public balance sheet data. We find that ensemble techniques and random forest provide the best results, corroborating the findings of Barboza et al. (Expert Syst. Appl., 2017).

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Section: Related Workmentioning

confidence: 99%

Firms Default Prediction with Machine Learning

Aliaj

Anagnostopoulos

Piersanti

2020

Mining Data for Financial Applications

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“…Fernández and Olmeda (1995) also empirically tested combining some of these techniques, with promising results. In contrast to these studies, Martinelli et al (1999) found that See4.5 outperformed ANNs. Furthermore, Laitinen and Kankaanpää (1999) showed that RPA 544 A.…”

Section: Frydman Et Al's Rpamentioning

confidence: 99%

Business failure prediction using decision trees

Gepp

Kumar

Bhattacharya

2009

Journal of Forecasting

118

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Accurate business failure prediction models would be extremely valuable to many industry sectors, particularly financial investment and lending. The potential value of such models is emphasised by the extremely costly failure of high-profile companies in the recent past. Consequently, a significant interest has been generated in business failure prediction within academia as well as in the finance industry. Statistical business failure prediction models attempt to predict the failure or success of a business. Discriminant and logit analyses have traditionally been the most popular approaches, but there are also a range of promising non-parametric techniques that can alternatively be applied. In this paper, the relatively new technique of decision trees is applied to business failure prediction. The numerical results suggest that decision trees could be superior predictors of business failure as compared to discriminant analysis. Copyright © 2009 John Wiley & Sons, Ltd.

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“…Martinelli et al [29] compared between two decision tree algorithms, C4.5 and CN2, and NN on a database of Brazilian firms. C4.5 outperform the other methods.…”

Section: B Neural-network (Nn) Approachesmentioning

confidence: 99%

Bankruptcy prediction for credit risk using neural networks: A survey and new results

Atiya

2001

IEEE Trans. Neural Netw.

561

243

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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, 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).Index Terms-Asset-based model, bankruptcy prediction, corporate distress, corporate failure prediction, credit risk, default prediction, financial ratios, financial statement data, multilayer networks.

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Bankruptcy prediction using connectionist and symbolic learning algorithms

Cited by 7 publications

References 8 publications

Firms Default Prediction with Machine Learning

Firms Default Prediction with Machine Learning

Business failure prediction using decision trees

Bankruptcy prediction for credit risk using neural networks: A survey and new results

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