Bankruptcy Prediction with Missing Data

Bankruptcy prediction have been widely studied as a binary classification problem using financial ratios methodologies. When calculating the ratios, it is common to confront missing data problem. Thus, this paper proposes a classification method Ensemble Nearest Neighbors (ENN) to solve it. ENN uses different nearest neighbors as ensemble classifiers, then make a linear combination of them. Instead of choosing k in original k-Nearest Neighbors algorithm, ENN provides weights to each classifier which makes the method more robust. Moreover, using a adapted distance metric, ENN can be used directly for incomplete data. In a word, ENN is a robust and a comparatively simple model while providing good performance with missing data. In the experiment section, four financial datasets which are publicly available are used to prove this conclusion.