Distress Risk and Corporate Failure Modelling
The State of the Art
What it is.
Distress Risk and Corporate Failure Modelling brings together the state of the art in predicting when and why companies fail. It is Professor Stewart Jones's survey of the field he has spent a career building, from the classic statistical models to the machine-learning methods that now sit at the centre of credit and distress analysis.
The book is rigorous about what works and what does not: the data, the methods, and the trade-offs between them, including why a model that looks strong in-sample can still mislead when the economic regime shifts.
For anyone trying to understand the risk that a company does not make it, this connects the academic research to the way distress is scored in practice, including the lineage that informs Alpha360.
Written to be used.
- Credit analysts and risk managers modelling default and distress.
- Investors who want to understand what really drives failure risk.
- Researchers and students working in bankruptcy and credit-risk prediction.
- Anyone evaluating the models behind a distress or credit score.
From the publisherA complete, practical guide to distress risk and corporate failure modelling. The book shows how to apply a wide range of corporate bankruptcy prediction models, and where each one holds up or breaks down, spanning the full spread of statistical learning methods from linear discriminant analysis through to gradient boosting machines, adaptive boosting, random forests, and deep learning. Every model is illustrated on real company failure data, which makes it as useful to practitioners forecasting distress as to researchers and academics working in the field.
Get the book.
The reference on how corporate failure is modelled, from the classic methods to machine learning.
View at Routledge