Product Description
by Stewart Jones (Author)
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.
Author Biography
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. He has published over 150 scholarly research pieces, including 70 refereed articles, 10 books, and numerous book chapters, working papers, and short monographs. Stewart is currently Senior Editor of the prestigious international quarterly, Abacus.
