A data mining approach to the prediction of corporate failure

Feng, Lijie; McClean, Sally

doi:10.1016/s0950-7051(01)00096-x

Cited by 104 publications

(19 citation statements)

References 23 publications

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“…Our study makes the attempt to take into account these deficiencies of the bankruptcy forecasting models indicated above in the process of discriminate analysis application. The proposed model takes into account the industrial and regional characteristics, as it is developed on the basis of the data obtained from the enterprises producing building materials in the Republic of Tatarstan (Lin & McClean, 2001). …”

Section: Methodsmentioning

confidence: 99%

Z-Score for Bankruptcy Forecasting of the Companies Producing Building Materials

Burganova¹,

Salahieva²

2015

ASS

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The article is dedicated to developing of methodological basis for the probability of enterprises' bankruptcy forecasting. The authors have identified the reasons of the existing bankruptcy forecasting models failure, the necessity of models developing for companies in various industries on the basis of the existing instruments. As a result of the multivariate discriminate analysis we offer Z-scores based on the average annual rates of change and taking into account the peculiarities of the industry companies. The model is developed for companies specializing in the production of building materials, it has a high level of productivity -90.4%.

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Section: Methodsmentioning

confidence: 99%

Z-Score for Bankruptcy Forecasting of the Companies Producing Building Materials

Burganova¹,

Salahieva²

2015

ASS

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“…They mainly focus on a quantitative analysis based on the financial data [18][19][20][21]25]. However, as pointed out in [35], qualitative analysis has its own advantages over quantitative analysis.…”

Section: Brief Review Of Combined Forecasting Models For Bfpmentioning

confidence: 99%

“…Since the pioneering work by Bates and Granger [17], many researchers have combined several forecasting methods together to improve forecasting performance [18][19][20][21][22][23]. Most of the combined forecasting methods employed well-known combined methods including equal weighted, majority voting; Borda count; and Bayesian or intelligent algorithms, such as the neural network and fuzzy algorithm, as the combination method.…”

Section: Introductionmentioning

confidence: 99%

Soft Set Theory Oriented Forecast Combination Method for Business Failure Prediction

2015

J Inf Process Syst

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This paper presents a new combined forecasting method that is guided by the soft set theory (CFBSS) to predict business failures with different sample sizes. The proposed method combines both qualitative analysis and quantitative analysis to improve forecasting performance. We considered an expert system (ES), logistic regression (LR), and support vector machine (SVM) as forecasting components whose weights are determined by the receiver operating characteristic (ROC) curve. The proposed procedure was applied to real data sets from Chinese listed firms. For performance comparison, single ES, LR, and SVM methods, the combined forecasting method based on equal weights (CFBEWs), the combined forecasting method based on neural networks (CFBNNs), and the combined forecasting method based on rough sets and the D-S theory (CFBRSDS) were also included in the empirical experiment. CFBSS obtains the highest forecasting accuracy and the second-best forecasting stability. The empirical results demonstrate the superior forecasting performance of our method in terms of accuracy and stability.

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“…From the research that examines similar classifi cation problems, like bankruptcy prediction or fraud detection, it is observed that methodologies derived from AI perform at least to the same level as statistical techniques (Fanning and Cogger, 1998;O'Leary, 1998;Lin and McLean, 2001). In the fi eld of auditing, relevant studies have only recently employed AI techniques, such as NNs (e.g.…”

Section: Prior Researchmentioning

confidence: 99%

Audit‐firm group appointment: an artificial intelligence approach

Kirkos

Spathis

Manolopoulos

2009

Intelligent Systems in Accounting, Finance and Management

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SUMMARYAuditor appointment can be regarded as a matter of pursued audit quality and is driven by several factors. The adoption of an effective auditor procurement process increases the likelihood that a company will engage the right auditor at a fair price. In this study, three techniques derived from artifi cial intelligence (AI) are used to propose models capable of discriminating between cases where companies appoint a Big 4 or a Non-Big 4 auditor. These three AI methods are then compared with the broadly used method of logistic regression. The results indicate that two of the AI techniques outperform logistic regression. In addition, one method further improves its performance by applying bagging. Finally, signifi cant factors associated with auditor appointment are revealed.

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A data mining approach to the prediction of corporate failure

Cited by 104 publications

References 23 publications

Z-Score for Bankruptcy Forecasting of the Companies Producing Building Materials

Z-Score for Bankruptcy Forecasting of the Companies Producing Building Materials

Soft Set Theory Oriented Forecast Combination Method for Business Failure Prediction

Audit‐firm group appointment: an artificial intelligence approach

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