Performance Evaluation of Neural Network Decision Models

Bharat A. Jain · Barin N. Nag

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Financial Distress and Bankruptcy Prediction Neural Networks and Applications Imbalanced Data Classification Techniques

Abstract

:Recently, promising results with neural networks have been reported for two-group classification problems such as bankruptcy prediction and thrift failures. Such applications are usually characterized by unequal frequencies of the two states of interest. This creates a major obstacle to effective performance evaluation of various decision models. Critical issues affecting the comparison include training sample design and the use of an appropriate performance metric. This paper addresses these two issues by comparing the performance of neural networks with that of statistical models for the decision problem of identifying successful new ventures.

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1976Theory of the firm: Managerial behavior, agency costs and ownership structure 1980Financial Ratios and the Probabilistic Prediction of Bankruptcy 1977INFORMATIONAL ASYMMETRIES, FINANCIAL STRUCTURE, AND FINANCIAL INTERMEDIATION 1977Informational Asymmetries, Financial Structure, and Financial Intermediation 1991The Long‐Run Performance of initial Public Offerings 1984Methodological Issues Related to the Estimation of Financial Distress Prediction Models 1986Investment banking, reputation, and the underpricing of initial public offerings 1990Initial Public Offerings and Underwriter Reputation

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