Distress Risk and Corporate Failure Modelling

The State of the Art

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'This book provides a comprehensive and highly informative review of corporate distress and bankruptcy modelling literature. The book traces the early development of this literature from linear discriminant models that dominated bankruptcy research of the 1960s and 1970s to modern machine learning methods (such as gradient boosting machines and random forests) which have become more prevalent today. The book also provides a comprehensive illustration of different machine learning methods (such as gradient boosting machines and random forests) as well as several pointers in how to interpret and apply these models using a large international corporate bankruptcy dataset. A helpful book for all empirical researchers in academia as well as in business.'

Iftekhar Hasan, E. Gerald Corrigan Chair in International Business and Finance, Gabelli School of Business, Fordham University in New York, USA

'The corporate bankruptcy prediction literature has made rapid advances in recent years. This book provides a comprehensive and timely review of empirical research in the field. While the bankruptcy literature tends to be quite dense and mathematical, this book is very easy to read and follow. It provides a thorough but intuitive overview of a wide range of statistical learning methods used in corporate failure modelling, including multiple discriminant analysis, logistic regression, probit models, mixed logit and nested logit models, hazard models, neural networks, structural models of default and a variety of modern machine learning methods. The strengths and limitations of these methods are well illustrated and discussed throughout. This book will be a very useful compendium to anyone interested in distress risk and corporate failure modellin.'

Andreas Charitou, Professor of Accounting and Finance and Dean, School of Economics and Management, The University of Cyprus

'This is a very timely book that provides excellent coverage of the bankruptcy literature. Importantly, the discussion on machine learning methods is instructive, contemporary and relevant, given the increasingly widespread use of these methods in bankruptcy prediction and in finance and business more generally.'

Jonathan Batten, Professor of Finance, RMIT University

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This book is an introduction text to corporate bankruptcy prediction modelling techniques and applications. The book illustrates how to apply a wide range of corporate bankruptcy prediction models and in turn, highlights their strengths and limitations under different circumstances. Les mer

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This book is an introduction text to corporate bankruptcy prediction modelling techniques and applications. The book 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.


The book's illustrations and applications which are based on actual company failure data and samples helps the understanding of the continual development of more robust predictive models and framework. Its comprehensive review and use of real-life data will make this a valuable, easy-to-read text for anyone interested in corporate bankruptcy models and applications.

Detaljer

Forlag
Routledge
Innbinding
Innbundet
Språk
Engelsk
Sider
230
ISBN
9781138652491
Utgivelsesår
2022
Format
23 x 16 cm

Om forfatteren

Stewart Jones is Professor at the School of Business, University of Sydney. He specializes in corporate financial reporting. Over the past decade he has published over 100 scholarly research pieces in financial reporting/accounting, including nearly sixty refereed articles, ten books, and numerous book chapters, working papers and short monographs. Stewart is currently Editor-in-Chief of the prestigious international quarterly, Abacus. Stewart's industry experience includes the interpretation of accounting standards; financial analysis and regulation; credit risk modelling and corporate performance analysis.

Anmeldelser

«

'This book provides a comprehensive and highly informative review of corporate distress and bankruptcy modelling literature. The book traces the early development of this literature from linear discriminant models that dominated bankruptcy research of the 1960s and 1970s to modern machine learning methods (such as gradient boosting machines and random forests) which have become more prevalent today. The book also provides a comprehensive illustration of different machine learning methods (such as gradient boosting machines and random forests) as well as several pointers in how to interpret and apply these models using a large international corporate bankruptcy dataset. A helpful book for all empirical researchers in academia as well as in business.'

Iftekhar Hasan, E. Gerald Corrigan Chair in International Business and Finance, Gabelli School of Business, Fordham University in New York, USA

'The corporate bankruptcy prediction literature has made rapid advances in recent years. This book provides a comprehensive and timely review of empirical research in the field. While the bankruptcy literature tends to be quite dense and mathematical, this book is very easy to read and follow. It provides a thorough but intuitive overview of a wide range of statistical learning methods used in corporate failure modelling, including multiple discriminant analysis, logistic regression, probit models, mixed logit and nested logit models, hazard models, neural networks, structural models of default and a variety of modern machine learning methods. The strengths and limitations of these methods are well illustrated and discussed throughout. This book will be a very useful compendium to anyone interested in distress risk and corporate failure modellin.'

Andreas Charitou, Professor of Accounting and Finance and Dean, School of Economics and Management, The University of Cyprus

'This is a very timely book that provides excellent coverage of the bankruptcy literature. Importantly, the discussion on machine learning methods is instructive, contemporary and relevant, given the increasingly widespread use of these methods in bankruptcy prediction and in finance and business more generally.'

Jonathan Batten, Professor of Finance, RMIT University

»

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