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The Impact of incorporating the cost of errors into bankruptcy prediction models

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This study examines the ability of a bankruptcy prediction model to outperform a naive model, in the context of a bank lending funds, after incorporating the costs of incorrect forecasts into both models. The study uses a sample of bankrupt versus non-bankrupt firms that is representative of the true population proportions, compares the prediction and naive models based on the net profit each would generate on a subsequent time period, and examines the sensitivity of the results to alternative sample time periods. The results indicate that a bankruptcy prediction model will outperform a naive model of lending to all firms only when type I errors (lending to firms which go bankrupt) are costly (25 times as large for the sample) relative to type II errors (failing to lend to firms which do not go bankrupt). This implies the usefulness of bankruptcy prediction models cannot be assessed independently of the costs of forecast errors

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en

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application/pdf

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http://flora.insead.edu/fichiersti_wp/inseadwp1996/96-27.pdf

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Copyright INSEAD. All rights reserved