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Web Extension 22A

Web Extension 22A. Multiple Discriminant Analysis. What is MDA, and how can it be used to predict bankruptcy?. Multiple discriminant analysis (MDA) is a statistical technique similar to multiple regression. It identifies the characteristics of firms that went bankrupt in the past.

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Web Extension 22A

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  1. Web Extension 22A Multiple Discriminant Analysis

  2. What is MDA, and how can it be used to predict bankruptcy? • Multiple discriminant analysis (MDA) is a statistical technique similar to multiple regression. • It identifies the characteristics of firms that went bankrupt in the past. • Then, data from any firm can be entered into the model to assess the likelihood of future bankruptcy.

  3. MDA Illustration • Assume you have the following 2009 data for 12 companies: • Current ratio • Debt ratio • Six of the companies (marked by Xs) went bankrupt in 2010 while six (marked by dots) remained solvent. (More...)

  4. Current Ratio ■ Discriminant Boundary ■ Solvent Firms X ■ ■ X ■ X Bankrupt Firms X ■ X X ■= Solvent Debt Ratio X = Bankrupt (More…)

  5. The discriminant boundary, or Z line, statistically separates the bankrupt and solvent companies. • Note that two companies have been misclassified by the MDA program: One bankrupt company falls on the solvent (left) side and one solvent company falls on the bankrupt (right) side. (More...)

  6. Assume the equation for the boundary line is • Z = -2 + 1.5(Current ratio) - 5.0(Debt ratio). • Furthermore, if Z = -1 to +1, the future of the company is uncertain. If Z > 1,bankruptcy is unlikely; if Z < -1, bankruptcy is likely to occur.

  7. Using MDA To Predict Bankruptcy • Suppose Firm S has CR = 4.0 and DR = 0.40. Then, Z = -2 + 1.5(4.0) - 5.0(0.40) = +2.0, and firm is unlikely to go bankrupt. • Suppose Firm B has CR = 1.5 and DR = 0.75. Then, Z = -2 + 1.5(1.5) - 5.0(0.75) = -3.5, and firm is likely to go bankrupt.

  8. Some Final Points • The most well-known bankruptcy prediction model is Edward Altman’s five factor model. • Such models tend to work relatively well, but only for the near term. • The more similar the historical sample to the firm being evaluated, the better the prediction.

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