We build a global bankruptcy prediction model using a support vector machine trained only on firms' endogenous information in the form of financial ratios. The model is tested not only on entirely random unseen data but on samples taken from specific global regions and industries to test for prediction bias, achieving satisfactory prediction performance in all cases. While support vector machines are not easily interpretable, we explore variable importance and find it consistent with economic intuition.
We provide an intuitive construction of a support vector machine (SVM) and explore the motivation behind using different tools for data classification. Beginning with linear classifiers, we build intuition on the subtlety of classification in increasingly non-linear circumstances and conclude with an example of bankruptcy prediction to illustrate the effectiveness and flexibility of support vector machines.
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