Articlegreen1995

Predicting Corporate Failure Using a Neural Network Approach

J. Efrim Boritz · Duane B. Kennedy · Augusto de Miranda e Albuquerque

100 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 Corporate Insolvency and Governance Forecasting Techniques and Applications

Abstract

Abstract This paper investigates the performance of Artificial Neural Networks for the classification and subsequent prediction of business entities into failed and non‐failed classes. Two techniques, back‐propagation and Optimal Estimation Theory (OET), are used to train the neural networks to predict bankruptcy filings. The data are drawn from Compustat data tapes representing a cross‐section of industries. The results obtained with the neural networks are compared with other well‐known bankruptcy prediction techniques such as discriminant analysis, probit and logit, as well as against benchmarks provided by directly applying the bankruptcy prediction models developed by Altman (1968) and Ohlson (1980) to our data set. We control the degree of ‘disproportionate sampling’ by creating ‘training’ and ‘testing’ populations with proportions of bankrupt firms ranging from 1% to 50%. For each population, we apply each technique 50 times to determine stable accuracy rates in terms of Type I, Type II and Total Error. We show that the performance of various classification techniques, in terms of their classification errors, depends on the proportions of bankrupt firms in the training and testing data sets, the variables used in the models, and assumptions about the relative costs of Type I and Type II errors. The neural network solutions do not achieve the ‘magical’ results that literature in this field often promises, although there are notable 'pockets' of superior performance by the neural networks, depending on particular combinations of proportions of bankrupt firms in training and testing data sets and assumptions about the relative costs of Type I and Type II errors. However, since we tested only one architecture for the neural network, it will be necessary to investigate potential improvements in neural network performance through systematic changes in neural network architecture.

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References

1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 1980Financial Ratios and the Probabilistic Prediction of Bankruptcy 1966Financial Ratios As Predictors of Failure 1992Managerial Applications of Neural Networks: The Case of Bank Failure Predictions 1983Corporate financial distress : a complete guide to predicting, avoiding, and dealing with bankruptcy 1992Neural Networks: A New Tool for Predicting Thrift Failures*1968Market Prices, Financial Ratios, and the Prediction of Failure 1987CURRENT TECHNIQUES IN BANKRUPTCY PREDICTION

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2000Predicting bankruptcy using recursive partitioning and a realistically proportioned data set 2000The efficacy of red flags in predicting the SEC's targets: an artificial neural networks approach 2015Predicting corporate failure: a systematic literature review of methodological issues 2007An integrative model with subject weight based on neural network learning for bankruptcy prediction 2006Financial distress prediction by a radial basis function network with logit analysis learning

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