Articlediamond2013

Data Mining Approach to Prediction of Going Concern Using Classification and Regression Tree (CART)

16 citations0/0 files storedGlobal Journal of Management and Business Research

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Financial Distress and Bankruptcy Prediction Artificial Intelligence in Healthcare Data Mining and Machine Learning Applications

Abstract

This paper has employed a data mining approach for Going Concern Prediction (GCP) for one year ahead and has applied Classification and Regression Tree (CART) and Naive Bayes Bayesian Network (NBBN) based on feature selection method in Iranian firms listed in Tehran Stock Exchange (TSE). For this purpose, at the first step, using the Stepwise Discriminant Analysis (SDA) has opted the final variables from among of 42 variables and in the next stage, has applied 10-fold cross-validation to figure out the optimal model. McNemar test signifies that there is a significant difference between the two models in terms of prediction accuracy and CART model is able to predict going concern more accurately. The CART model reached 99.92 and 98.62 percent accuracy rates so as to training and holdout data.

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References

1980An Investigation into Auditors' Continuity and Related Qualification Judgments 2010Predicting business failure using classification and regression tree: An empirical comparison with popular classical statistical methods and top classification mining methods 1987An empirical investigation of audit qualification decisions in the presence of going concern uncertainties*2008Predicting going concern opinion with data mining 2004Going concern prediction using data mining techniques 2011Financial health prediction models using artificial neural networks, genetic algorithm and multivariate discriminant analysis: Iranian evidence 2004A hybrid genetic model for the prediction of corporate failure 1991Probit Prediction of Going and Non‐going Concerns

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