An easy to read by "state of the art" text containing a comprehensive review and analysis of existing corporate bankruptcy models, and their applications to real life data
Covers a broad range of statistical learning models, ranging from relatively linear techniques (e.g. linear discriminant analysis) to state-of the art machine learning methods (e.g. random forests, deep learning).
Explains the purpose, strength and limitations of respective models and frameworks, highlighting their major points of similarity and difference and would make this book a useful reference
Much of the corporate bankruptcy literature has relied on quite simplistic classification models but this book introduces a wide range of innovative corporate bankruptcy prediction models
