Web Extension 25B Multiple Discriminant Analysis.

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Presentation transcript:

Web Extension 25B 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. Then, data from any firm can be entered into the model to assess the likelihood of future bankruptcy.

MDA Illustration Assume you have the following 2008 data for 12 companies: Current ratio Debt ratio Six of the companies (marked by Xs) went bankrupt in 2009 while six (marked by dots) remained solvent. (More... )

(More…) X X X X XBankrupt Firms Debt Ratio Solvent Firms Current RatioDiscriminant Boundary X X = Bankrupt ■ ■ ■ ■ ■ ■ ■ = Solvent

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... )

Assume the equation for the boundary line is Z = (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.

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

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.