Dynamics of Modeling in Data Mining: Interpretive Approach to Bankruptcy Prediction

Sung, Tae Kyung; Chang, Namsik; Lee, Gunhee

doi:10.1080/07421222.1999.11518234

Cited by 163 publications

(65 citation statements)

References 39 publications

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“…The discriminant analysis model was the one that had the best performance in the classification of firms in the sample for the third year prior to failure. This level of accuracy is higher than that obtained by Blum (1974), Aly et al (1992), Sung et al (1999), and however lower than Deakin (1972), particularly the sample for the third year prior to failure. Within the models of the artificial intelligence the results obtained with Rough Sets model were generally higher than the neural network model results.…”

Section: Rough Setsmentioning

confidence: 54%

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

Pereira¹,

Basto²,

Silva³

2014

JMS

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A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context.The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003-09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure.Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

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Section: Rough Setsmentioning

confidence: 54%

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

Pereira¹,

Basto²,

Silva³

2014

JMS

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“…For instance, the most relevant related works can be found in [10], where a Neural Network model obtained a maximum accuracy of 95%; and in [11] where a Decision Tree based model attained an accuracy of 83%. Furthermore, this study considered a large dataset, with a total of 2 288 companies.…”

Section: Conclusion and Further Workmentioning

confidence: 99%

“…Furthermore, this study considered a large dataset, with a total of 2 288 companies. In contrast, [10] used a reduced sample, with only 282 organizations, while [11] considered even a smaller number, with 72 companies.…”

Section: Conclusion and Further Workmentioning

confidence: 99%

Corporate bankruptcy prediction using data mining techniques

Santos¹,

Cortez²,

Pereira³

et al. 2006

Data Mining VII: Data, Text and Web Mining and Their Business Applications

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The interest in the prediction of corporate bankruptcy is increasing due to the implications associated with this phenomenon (e.g. economic, and social) for investors, creditors, competitors, government, although this is a classical problem in the financial literature.Two kinds of models are generally adopted for bankruptcy prediction: (i) accounting ratios based models and (ii) market based models. In the former, classical statistical techniques such as discriminant analysis or logistic regression models have been used, while in the latter the Moody's KMV model was adopted.This paper follows the first approach (i), and it is based on the analysis of the evolution of several financial indicators during a three-year period. A framework was developed, encompassing a total of 16 models. These differ in the data mining algorithm (e.g. Artificial Neural Networks or Decision Trees), the data used (all three years or just the last one) and the input attributes adopted (e.g. all accounting ratios or just the most significant ones). The experiments were conducted using the new Business Intelligence Development Studio of the Microsoft SQL Server. Very good results were achieved, with performances between 86% and 99% for all 16 models.

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“…Decision trees allow for easy generation of decision rules, making them ideal for providing insights and explanations to nontechnical users. Decision trees are especially suitable for decision problems that require the generation of human-understandable decision rules based on a mix of classification of categorical and continuous data (Sung et al, 1999). They clearly indicate the importance of individual data to the decision problem, and are therefore useful in reducing the cognitive burden for the decision maker.…”

Section: Knowledge Representation and Ontologiesmentioning

confidence: 99%

“…The performance of a particular method in modeling human decisions depends on the conformance of the method with the decision makers' mental model of the decision problem (Sung et al, 1999). Decision trees are a popular decision modeling technique with wide applicability to a variety of business problems.…”

Section: Knowledge Representation and Ontologiesmentioning

confidence: 99%

Web Service for Knowledge Management in E-Marketplaces

Singh¹,

Iyer²,

Salam³

2003

e-Service Journal

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Abstract:A common strategic initiative of organizations engaged in electronic business (e-business) is the development of synergistic relations with collaborating value-chain partners to deliver their value proposition to customers. This requires the transparent flow of problem specific knowledge to partner organizations over highly integrated information systems. Transparent exchange of information and knowledge across collaborating organizations requires technological foundations for integrating business processes using software architectures built upon industry standards. The unambiguously interpretable flow of knowledge to inform online business processes is a challenging task with significant competitive benefits for organizations that take technical initiative. Infomediary organizations can serve the e-business need for exchange of knowledge and information through value-added knowledge services to participating firms in the value chain through intelligent software systems integrated with the Web Services Architecture. We define knowledge services as the "exchange of problem domain-specific knowledge to inform decision activities of specific e-business processes, facilitated by an infomediary using intelligent software systems and the Web Services Architecture." This research presents a knowledge services framework, founded on the Web Services Architecture, to enable the transparent exchange of knowledge between intelligent software systems that manage processes of organizations engaged in ebusiness in the knowledge-based economy. The objective is to enable informed and knowledge-based discovery of business partners from among the multitude online, and to support knowledge-rich e-business processes that cut across the value chain and deliver the value proposition to the customer.

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Dynamics of Modeling in Data Mining: Interpretive Approach to Bankruptcy Prediction

Cited by 163 publications

References 39 publications

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

Corporate bankruptcy prediction using data mining techniques

Web Service for Knowledge Management in E-Marketplaces

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