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Recognizing Financial Distress Patterns Using a Neural Network Tool

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Author Info
Pamela K. Coats
L. Franklin Fant
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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Publisher Info
Article provided by Financial Management Association in its journal Financial Management.

Volume (Year): 22 (1993)
Issue (Month): 3 (Fall)
Pages:
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Handle: RePEc:fma:fmanag:coats93

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  1. Greta Falavigna, 2008. "Nouveaux instruments d’évaluation pour le risque financier d’entreprise," CERIS Working Paper 200801, Institute for Economic Research on Firms and Growth - Moncalieri (TO). [Downloadable!]
  2. Haider A. Khan, 2002. "Can Banks Learn to Be Rational?," CIRJE F-Series CIRJE-F-151, CIRJE, Faculty of Economics, University of Tokyo. [Downloadable!]
  3. Khurshid Kiani & Terry Kastens, 2008. "Testing Forecast Accuracy of Foreign Exchange Rates: Predictions from Feed Forward and Various Recurrent Neural Network Architectures," Computational Economics, Springer, vol. 32(4), pages 383-406, November. [Downloadable!] (restricted)
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This page was last updated on 2009-12-10.


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