A Meta‐learning Framework for Bankruptcy Prediction

Tsai, Chih‐Fong; Hsu, Yu Feng

doi:10.1002/for.1264

Cited by 28 publications

(10 citation statements)

References 28 publications

(46 reference statements)

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“…This subject dealt with studies which analyze the classification effectiveness of such ensemble classifiers by relying on several models of base classifiers developed by Hua et al (2007), Zhang et al (2010) and Twala (2010). The use of ensemble classifiers with combining (stacking) the results of several classifiers in a single meta-classifier was discussed in studies such as those by Iturriaga and Sanz (2015), Tsai and Wu (2008) and Tsai and Hsu (2013). Furthermore, many studies are dedicated to the use of various techniques of combining the results of base model classification: such as neural networks in the form of self-organizing maps (SOMs), rough sets techniques, case-based reasoning and classifier consensus methods.…”

Section: Literature Reviewmentioning

confidence: 99%

An Ensemble Classifier-Based Scoring Model for Predicting Bankruptcy of Polish Companies in the Podkarpackie Voivodeship

Pisula

2020

JRFM

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This publication presents the methodological aspects of designing of a scoring model for an early prediction of bankruptcy by using ensemble classifiers. The main goal of the research was to develop a scoring model (with good classification properties) that can be applied in practice to assess the risk of bankruptcy of enterprises in various sectors. For the data sample, which included 1739 Polish businesses (of which 865 were bankrupt and 875 had no risk of bankruptcy), a genetic algorithm was applied to select the optimum set of 19 bankruptcy indicators, on the basis of which the classification accuracy of a number of ensemble classifier model variants (boosting, bagging and stacking) was estimated and verified. The classification effectiveness of ensemble models was compared with eight classical individual models which made use of single classifiers. A GBM-based ensemble classifier model offering superior classification capabilities was used in practice to design a scoring model, which was applied in comparative evaluation and bankruptcy risk analysis for businesses from various sectors and of different sizes from the Podkarpackie Voivodeship in 2018 (over a time horizon of up to two years). The approach applied can also be used to assess credit risk for corporate borrowers.

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Section: Literature Reviewmentioning

confidence: 99%

An Ensemble Classifier-Based Scoring Model for Predicting Bankruptcy of Polish Companies in the Podkarpackie Voivodeship

Pisula

2020

JRFM

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“…The process is also able to determine the weights of the ensemble models in the fusion schema, based on their local performance around the query. Some other recent approaches to development of metalearning frameworks can be found in Matijaš et al (2013) for the problem of electricity load forecasting, Abbasi et al (2012) for financial fraud detection or Tsai and Hsu (2013) for bankruptcy prediction.…”

Section: Notions Of Metalearningmentioning

confidence: 99%

Metalearning: a survey of trends and technologies

Lemke¹,

2013

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Metalearning attracted considerable interest in the machine learning community in the last years. Yet, some disagreement remains on what does or what does not constitute a metalearning problem and in which contexts the term is used in. This survey aims at giving an all-encompassing overview of the research directions pursued under the umbrella of metalearning, reconciling different definitions given in scientific literature, listing the choices involved when designing a metalearning system and identifying some of the future research challenges in this domain.

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“…In practical terms, none of the predictive variables or functions is completely independent. In recent years, artificial intelligence models such as artificial neural networks, association rules mining, genetic programming models, case‐based reasoning, and support vector machines (SVMs) have been regarded as alternate classification technologies that can be used instead of statistical modelling to develop business failure prediction models (Kumar & Ravi, ; Ravisankar & Ravi, ; Tsai & Hsu, ; Geng et al , ). In particular, artificial intelligence techniques have been shown to have superior performance compared with statistical techniques (Tsai, ; Sun et al , ).…”

Section: Literature Reviewmentioning

confidence: 99%

Risk prediction of product‐harm events using rough sets and multiple classifier fusion: an experimental study of listed companies in China

Wang

Zheng

et al. 2016

Expert Systems

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With the increasing of frequency and destructiveness of product-harm events, study on enterprise crisis management becomes essentially important, but little literature thoroughly explores the risk-prediction method of product-harm event. In this study, an initial index system for risk prediction was built based on the analysis of the key drivers of the product-harm event's evolution; ultimately, nine risk-forecasting indexes were obtained using rough set attribute reduction. With the four indexes of cumulative abnormal returns as the input, fuzzy clustering was used to classify the risk level of a product-harm event into four grades. In order to control the uncertainty and instability of single classifiers in risk prediction, multiple classifiers fusion was introduced and combined with self-organising data mining (SODM). Further, an SODM-based multiple classifiers fusion (SB-MCF) model was presented for the risk prediction related to a product-harm event. The experimental results based on 165 Chinese listed companies indicated that the SB-MCF model improved the average predictive accuracy and reduced variation degree simultaneously. The statistical analysis demonstrated that the SB-MCF model significantly outperformed six widely used single classification models (e.g. neural networks, support vector machine, and case-based reasoning) and other six commonly used multiple classifiers fusion methods (e.g. majority voting, Bayesian method, and genetic algorithm).Keywords product-harm; risk prediction; multiple classifiers; self-organizing data mining; rough set IntroductionProduct-harm crises can be defined as 'discrete, well publicized occurrences wherein products are found to be defective or dangerous ' (Dawar & Pillutla, 2000). The increasing complexity of products, the increased demands of customers, and the greater vigilance on the part of the media have made product-harm crises increasingly visible occurrences (Klein & Dawar, 2004). In America, the frequency of product-harm events in the Although the probability of product-harm events is relatively low for an individual enterprise, such an event, if mishandled, will not only hurt the psychology and physiology of consumers but also lead to huge economic losses and a trust crisis for the enterprise. Further, it might have undesirable social and political impacts. For instance, in an investigation conducted by Sina.com, 84% of the interviewees stated that they would no longer buy Shuanghui meat products after the 'clenbuterol' scandal, while 7.8% of the interviewees were undecided. Only 8.4% of the interviewees stated that they would continue to buy Shuanghui products, which would lead to cumulative economic losses of over 20 billion yuan for the Shuanghui group. Similarly, because of the 'melamine scandal', Sanlu Group, China's third largest dairy company, went bankrupt. Moreover, this event led to public distrust and insecurity about food safety. According to a recent market report released by AC Nielsen about China's infant milk powder, China's brand o...

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A Meta‐learning Framework for Bankruptcy Prediction

Cited by 28 publications

References 28 publications

An Ensemble Classifier-Based Scoring Model for Predicting Bankruptcy of Polish Companies in the Podkarpackie Voivodeship

An Ensemble Classifier-Based Scoring Model for Predicting Bankruptcy of Polish Companies in the Podkarpackie Voivodeship

Metalearning: a survey of trends and technologies

Risk prediction of product‐harm events using rough sets and multiple classifier fusion: an experimental study of listed companies in China

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