Bankruptcy prediction is one of the most important research areas in corporate finance. Bankruptcies are an indispensable element of the functioning of the market economy, and at the same time generate significant losses for stakeholders. Hence, this book was established to collect the results of research on the latest trends in predicting the bankruptcy of enterprises. It suggests models developed for different countries using both traditional and more advanced methods. Problems connected with predicting bankruptcy during periods of prosperity and recession, the selection of appropriate explanatory variables, as well as the dynamization of models are presented. The reliability of financial data and the validity of the audit are also referenced. Thus, I hope that this book will inspire you to undertake new research in the field of forecasting the risk of bankruptcy.

A comprehensive look at the enormous growth and evolution of distressed debt, corporate bankruptcy, and credit risk default This Third Edition of the most authoritative finance book on the topic updates and expands its discussion of corporate distress and bankruptcy, as well as the related markets dealing with high-yield and distressed debt, and offers state-of-the-art analysis and research on the costs of bankruptcy, credit default prediction, the post-emergence period performance of bankrupt firms, and more.

Probabilistic Methods for Financial and Marketing Informatics aims to provide students with insights and a guide explaining how to apply probabilistic reasoning to business problems. Rather than dwelling on rigor, algorithms, and proofs of theorems, the authors concentrate on showing examples and using the software package Netica to represent and solve problems. The book contains unique coverage of probabilistic reasoning topics applied to business problems, including marketing, banking, operations management, and finance. It shares insights about when and why probabilistic methods can and cannot be used effectively. This book is recommended for all R&D professionals and students who are involved with industrial informatics, that is, applying the methodologies of computer science and engineering to business or industry information. This includes computer science and other professionals in the data management and data mining field whose interests are business and marketing information in general, and who want to apply AI and probabilistic methods to their problems in order to better predict how well a product or service will do in a particular market, for instance. Typical fields where this technology is used are in advertising, venture capital decision making, operational risk measurement in any industry, credit scoring, and investment science. - Unique coverage of probabilistic reasoning topics applied to business problems, including marketing, banking, operations management, and finance - Shares insights about when and why probabilistic methods can and cannot be used effectively - Complete review of Bayesian networks and probabilistic methods for those IT professionals new to informatics.

A thorough compendium of credit risk modelling approaches, including several new techniques that extend the horizons of future research and practice. Models and techniques are illustrated with empirical examples and are accompanied by a careful explanation of model derivation issues. An ideal resource for academics, practitioners and regulators.

A comprehensive look at the enormous growth and evolution of distressed debt markets, corporate bankruptcy, and credit risk models This Fourth Edition of the most authoritative finance book on the topic updates and expands its discussion of financial distress and bankruptcy, as well as the related topics dealing with leveraged finance, high-yield, and distressed debt markets. It offers state-of-the-art analysis and research on U.S. and international restructurings, applications of distress prediction models in financial and managerial markets, bankruptcy costs, restructuring outcomes, and more.

During the last decades the globalization, the intensified competition and the rapid changes in the socio-economic and technological environment had a major impact on the global economic, financial and business environments. Within this environment, it is clear that banking institutions worldwide face new challenges and increasing risks, as well as increasing business potentials. The recent experience shows that achieving a sustainable development of the banking system is not only of interest to the banking institutions themselves, but it is also directly related to the development of the whole business and economic environment, both at regional and international level. The variety of new banking products that is constantly being developed to accommodate the increased customer needs (firms, organizations, individuals, etc.) provides a clear indication of the changes that the banking industry has undergone during the last two decades. The establishment of new products of innovative processes and instruments for their requires the implementation efficient management. The implementation of such processes and instruments is closely related to a variety of disciplines, advanced quantitative analysis for risk management, information technology, quality management, etc. The implementation ofthese approaches in banking management is in accordance with the finding that empirical procedures are no longer adequate to address the increasing complexity of the banking industry.

This practical and easy-to-follow text explores the theoretical underpinnings of decision forests, organizing the vast existing literature on the field within a new, general-purpose forest model. Topics and features: with a foreword by Prof. Y. Amit and Prof. D. Geman, recounting their participation in the development of decision forests; introduces a flexible decision forest model, capable of addressing a large and diverse set of image and video analysis tasks; investigates both the theoretical foundations and the practical implementation of decision forests; discusses the use of decision forests for such tasks as classification, regression, density estimation, manifold learning, active learning and semi-supervised classification; includes exercises and experiments throughout the text, with solutions, slides, demo videos and other supplementary material provided at an associated website; provides a free, user-friendly software library, enabling the reader to experiment with forests in a hands-on manner.