As the pandemic urged further investigations on the prediction of firms' financial distress, this study develops and tests an alternative measure to the alert system elaborated by the NCCAAE which combines the benefits of the Z-score's multivariate discriminant model with the background employed to develop the NCCAAE' predictors. Using a sample of 43 viable and 43 non-viable Italian SMEs, we first compare the financial distress predictive accuracy of the NCCAAE's alert system to that of the traditional Z-score over the period 2015-2019. On the basis of the results, we elaborate and compare the revised versions of both approaches which align the traditional Z-score to the current socio-economic conditions and provide an alternative measure to the NCCAAE's alert system which embeds a Z-score calculated using the ratios elaborated by the NCCAAE for the alert system. The analysis of the two baseline approaches showed complementary results as the Z-score overperformed the alert system when predicting the status of non-viable firms whereas the opposite emerged as regards viable firms. The revised version of both approaches pointed out an enhanced predictive accuracy with respect to baseline models. In particular, the complementary role of the Z-score has been integrated into the new alert system as major contribute to its enhancement which pointed it out as the best measure employed. We, therefore, contribute to the literature studying the financial distress prediction developments by elaborating an alternative measure to the alert system developed by the NCCAAE which combines the benefits of the Z-score's multivariate discriminant function with the background employed to develop the NCCAAE' predictors. Our analysis enriches the post-pandemic debate on refined financial distressed prediction methods by pointing out the limits of the alert system as designed by the NCCAAE and suggests an alternative and better performing measure that may be used by third-party bodies to predict financial distress.

"A Wiley-Interscience publication."Includes index. Bibliography: p. 355-361.

The first full analysis of the latest advances in managing credit risk. "Against a backdrop of radical industry evolution, the authors of Managing Credit Risk: The Next Great Financial Challenge provide a concise and practical overview of these dramatic market and technical developments in a book which is destined to become a standard reference in the field." -Thomas C. Wilson, Partner, McKinsey & Company, Inc. "Managing Credit Risk is an outstanding intellectual achievement. The authors have provided investors a comprehensive view of the state of credit analysis at the end of the millennium." -Martin S. Fridson, Financial Analysts Journal. "This book provides a comprehensive review of credit risk management that should be compulsory reading for not only those who are responsible for such risk but also for financial analysts and investors. An important addition to a significant but neglected subject." -B.J. Ranson, Senior Vice-President, Portfolio Management, Bank of Montreal. The phenomenal growth of the credit markets has spawned a powerful array of new instruments for managing credit risk, but until now there has been no single source of information and commentary on them. In Managing Credit Risk, three highly regarded professionals in the field have-for the first time-gathered state-of-the-art information on the tools, techniques, and vehicles available today for managing credit risk. Throughout the book they emphasize the actual practice of managing credit risk, and draw on the experience of leading experts who have successfully implemented credit risk solutions. Starting with a lucid analysis of recent sweeping changes in the U.S. and global financial markets, this comprehensive resource documents the credit explosion and its remarkable opportunities-as well as its potentially devastating dangers. Analyzing the problems that have occurred during its growth period-S&L failures, business failures, bond and loan defaults, derivatives debacles-and the solutions that have enabled the credit market to continue expanding, Managing Credit Risk examines the major players and institutional settings for credit risk, including banks, insurance companies, pension funds, exchanges, clearinghouses, and rating agencies. By carefully delineating the different perspectives of each of these groups with respect to credit risk, this unique resource offers a comprehensive guide to the rapidly changing marketplace for credit products. Managing Credit Risk describes all the major credit risk management tools with regard to their strengths and weaknesses, their fitness to specific financial situations, and their effectiveness. The instruments covered in each of these detailed sections include: credit risk models based on accounting data and market values; models based on stock price; consumer finance models; models for small business; models for real estate, emerging market corporations, and financial institutions; country risk models; and more. There is an important analysis of default results on corporate bonds and loans, and credit rating migration. In all cases, the authors emphasize that success will go to those firms that employ the right tools and create the right kind of risk culture within their organizations. A strong concluding chapter integrates emerging trends in the financial markets with the new methods in the context of the overall credit environment. Concise, authoritative, and lucidly written, Managing Credit Risk is essential reading for bankers, regulators, and financial market professionals who face the great new challenges-and promising rewards-of credit risk management.

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.

Jonathan Edwards was a preacher, pastor, revivalist, and theologian. This volume unpacks his magnificent theological vision, which starts with God’s glory and ends with all creation returning to that glory. Sean Michael Lucas has converted his years of teaching on Edwards into this valuable work, which places Edwards’s vision in an accessible, two-part framework. Part one focuses on Edwards’s understanding of redemption history-God’s cosmic, grand work from eternity past to eternity future, where all things are united in Christ. Part two examines Edwards’s perspective on “redemption applied”-how that gracious, divine work unfolds in space and time to personally transform individuals, stirring their affections, illuminating their minds, and moving their wills to form new habits and practices. This overview of Edwards’s theology will prove to be a thought-provoking, encouraging guide to contemporary believers at every stage of their spiritual journey.

The purpose of this paper is firstly to review the literature on the efficacy and importance of the Altman Z-Score bankruptcy prediction model globally and its applications in finance and related areas. This review is based on an analysis of 33 scientific papers published from the year 2000 in leading financial and accounting journals. Secondly, we use a large international sample of firms to assess the classification performance of the model in bankruptcy and distressed firm prediction. In all, we analyze its performance on firms from 31 European and three non-European countries. This kind of comprehensive international analysis has not been presented thus far. Except for the U.S. and China, the firms in the sample are primarily private and cover non-financial companies across all industrial sectors. Thus, the version of the Z-Score model developed by Altman (1983) for private manufacturing and non-manufacturing firms (Z"-Score Model) is used in our testing. The literature review shows that results for Z-Score Models have been somewhat uneven in that in some studies the model has performed very well, whereas in others it has been outperformed by competing models. None of the reviewed studies is based on a comprehensive international comparison, which makes the results difficult to generalize. The analysis in this study shows that while a general international model works reasonably well, for most countries, with prediction accuracy levels (AUC) of about 75%, and exceptionally well for some (above 90%), the classification accuracy may be considerably improved with country-specific estimation especially with the use of additional variables. In some country models, the information provided by additional variables helps boost the classification accuracy to a higher level.

A better development and implementation framework for credit risk scorecards Intelligent Credit Scoring presents a business-oriented process for the development and implementation of risk prediction scorecards. The credit scorecard is a powerful tool for measuring the risk of individual borrowers, gauging overall risk exposure and developing analytically driven, risk-adjusted strategies for existing customers. In the past 10 years, hundreds of banks worldwide have brought the process of developing credit scoring models in-house, while ‘credit scores' have become a frequent topic of conversation in many countries where bureau scores are used broadly. In the United States, the ‘FICO' and ‘Vantage' scores continue to be discussed by borrowers hoping to get a better deal from the banks. While knowledge of the statistical processes around building credit scorecards is common, the business context and intelligence that allows you to build better, more robust, and ultimately more intelligent, scorecards is not. As the follow-up to Credit Risk Scorecards, this updated second edition includes new detailed examples, new real-world stories, new diagrams, deeper discussion on topics including WOE curves, the latest trends that expand scorecard functionality and new in-depth analyses in every chapter. Expanded coverage includes new chapters on defining infrastructure for in-house credit scoring, validation, governance, and Big Data. Black box scorecard development by isolated teams has resulted in statistically valid, but operationally unacceptable models at times. This book shows you how various personas in a financial institution can work together to create more intelligent scorecards, to avoid disasters, and facilitate better decision making. Key items discussed include: Following a clear step by step framework for development, implementation, and beyond Lots of real life tips and hints on how to detect and fix data issues How to realise bigger ROI from credit scoring using internal resources Explore new trends and advances to get more out of the scorecard Credit scoring is now a very common tool used by banks, Telcos, and others around the world for loan origination, decisioning, credit limit management, collections management, cross selling, and many other decisions. Intelligent Credit Scoring helps you organise resources, streamline processes, and build more intelligent scorecards that will help achieve better results.