Recognizing Financial Distress Patterns Using a Neural Network Tool

1993-01-01articleFinancial Management470 citationsclosed

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Financial Distress and Bankruptcy Prediction Corporate Insolvency and Governance Forecasting Techniques and Applications

Abstract

This study builds neural networks (NNs) which estimate the future financial health of firms. A neural network is a relatively new mathematical approach for recognizing discriminating patterns in data. We use NNs here to identify financial data patterns which consistently distinguish generally healthy firms from distressed ones. The purpose is to detect early warning signals of distressful conditions in currently viable firms. Being able to form highly reliable early forecasts of the future health of firms is critical to bank lending officers, investors, market analysts, portfolio managers, insurers, and many others in the field of finance.

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References

1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY

1980Financial Ratios and the Probabilistic Prediction of Bankruptcy

1984Methodological Issues Related to the Estimation of Financial Distress Prediction Models

1985Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress

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