Distress Risk and Corporate Failure Modelling

This book serves as an introduction to distress risk and corporate failure modelling techniques. 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.

Autorentext

Stewart Jones is Professor of Accounting at the University of Sydney Business School. He specializes in corporate financial reporting and has published extensively in the distress risk and corporate failure modelling field. His publications appear in many leading international journals including the Accounting Review, the Review of Accounting Studies, Accounting Horizons, Journal of Business Finance and Accounting, the Journal of the Royal Statistical Society, Journal of Banking and Finance and many other leading journals. Over his career has published over 150 scholarly research pieces including nearly seventy refereed articles, ten books, and numerous book chapters, working papers and short monographs. Stewart is currently Senior Editor of the prestigious international quarterly, Abacus.

Klappentext

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 which 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 linear 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.

Inhalt

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?