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.

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.

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.

Financial Statement Analysis and the Prediction of Financial Distress discusses the evolution of three main streams within the financial distress prediction literature: the set of dependent and explanatory variables used, the statistical methods of estimation, and the modeling of financial distress. Section 1 discusses concepts of financial distress. Section 2 discusses theories regarding the use of financial ratios as predictors of financial distress. Section 3 contains a brief review of the literature. Section 4 discusses the use of market price-based models of financial distress. Section 5 develops the statistical methods for empirical estimation of the probability of financial distress. Section 6 discusses the major empirical findings with respect to prediction of financial distress. Section 7 briefly summarizes some of the more relevant literature with respect to bond ratings. Section 8 presents some suggestions for future research and Section 9 presents concluding remarks.

This paper presents results from an econometric analysis of Russian bank defaults during the period 1997-2003, focusing on the extent to which publicly available information from quarterly bank balance sheets is useful in predicting future defaults. Binary choice models are estimated to construct the probability of default model. We find that preliminary expert clustering or automatic clustering improves the predictive power of the models and incor-poration of macrovariables into the models is useful. Heuristic criteria are suggested to help compare model performance from the perspectives of investors or banks supervision authorities. Russian banking system trends after the crisis 1998 are analyzed with rolling regressions.