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Full Description
This book is an introduction text to distress risk and corporate failure modelling techniques. It illustrates how to apply a wide range of corporate bankruptcy prediction models and, in turn, highlights their strengths and limitations under different circumstances. It also conceptualises the role and function of different classifiers in terms of a trade-off between model flexibility and interpretability.
Jones's illustrations and applications are based on actual company failure data and samples. Its practical and lucid presentation of basic concepts covers various statistical learning approaches, including machine learning, which has come into prominence in recent years. The material covered will help readers better understand a broad range of statistical learning models, ranging from relatively simple techniques, such as linear discriminant analysis, to state-of-the-art machine learning methods, such as gradient boosting machines, adaptive boosting, random forests, and deep learning.
The book's comprehensive review and use of real-life data will make this a valuable, easy-to-read text for researchers, academics, institutions, and professionals who make use of distress risk and corporate failure forecasts.
Contents
1. The Relevance and Utility of Distress Risk and Corporate Failure Forecasts 2. Searching for the Holy Grail: Alternative Statistical Modelling Approaches 3. The Rise of the Machines 4. An Empirical Application of Modern Machine Learning Methods 5. Corporate Failure Models for Private Companies, Not-for Profits, and Public Sector Entities 6. Whither Corporate Failure Research?
