Article1994

A Comparative Analysis of Artificial Neural Networks Using Financial Distress Prediction

82 citations0/0 files storedIntelligent systems in accounting, finance and management/Intelligent systems in accounting, finance & management

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Financial Distress and Bankruptcy Prediction Neural Networks and Applications Forecasting Techniques and Applications

Abstract

Abstract This paper examines the efficiency of a generalized adaptive neural network algorithm (GANNA) processor in comparison to earlier model‐based methods, a back‐propagation artificial neural network, and logistic regression approaches to data classification. The research uses the binary classification problem of discriminating between failing and non‐failing firms to compare the methods. The results indicate the potential in time savings and the successful classification results available from a GANNA processor.

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1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 1983Limited-dependent and qualitative variables in econometrics 1980Financial Ratios and the Probabilistic Prediction of Bankruptcy 1984Methodological Issues Related to the Estimation of Financial Distress Prediction Models 1977ZETATM analysis A new model to identify bankruptcy risk of corporations 1972A Discriminant Analysis of Predictors of Business Failure 1992Managerial Applications of Neural Networks: The Case of Bank Failure Predictions 2007Financial statement analysis

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2004Artificial neural networks in analytical review procedures 2000A Comparison of Selected Artificial Neural Networks that Help Auditors Evaluate Client Financial Viability 2011A tuning method for the architecture of neural network models incorporating GAM and GA as applied to bankruptcy prediction 2007A comparison of nearest neighbours, discriminant and logit models for auditing decisions 2007Prediction of corporate financial health by Artificial Neural Network

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