Corporate Bankruptcy Prediction
Corporate Bankruptcy Prediction

Author: Błażej Prusak

Publisher: MDPI

Published: 2020-06-16

Total Pages: 202

ISBN-13: 303928911X

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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.

Corporate Financial Distress and Bankruptcy
Corporate Financial Distress and Bankruptcy

Author: Edward I. Altman

Publisher: John Wiley & Sons

Published: 2010-03-11

Total Pages: 314

ISBN-13: 1118046048

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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.

Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction
Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction

Author: Stewart Jones

Publisher: Cambridge University Press

Published: 2008-09-25

Total Pages: 0

ISBN-13: 0521869285

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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.

Probabilistic Methods for Financial and Marketing Informatics
Probabilistic Methods for Financial and Marketing Informatics

Author: Richard E. Neapolitan

Publisher: Elsevier

Published: 2010-07-26

Total Pages: 427

ISBN-13: 0080555675

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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.

Corporate Financial Distress, Restructuring, and Bankruptcy
Corporate Financial Distress, Restructuring, and Bankruptcy

Author: Edward I. Altman

Publisher: John Wiley & Sons

Published: 2019-03-26

Total Pages: 374

ISBN-13: 1119481805

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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.

Decision Forests for Computer Vision and Medical Image Analysis
Decision Forests for Computer Vision and Medical Image Analysis

Author: Antonio Criminisi

Publisher: Springer Science & Business Media

Published: 2013-01-30

Total Pages: 367

ISBN-13: 1447149297

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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.

Digitalization in Finance and Accounting
Digitalization in Finance and Accounting

Author: David Procházka

Publisher: Springer Nature

Published: 2021-02-05

Total Pages: 368

ISBN-13: 3030552772

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This book explores current digitalization issues in finance and accounting with particular focus on emerging and transitioning markets. It features models, empirical studies and cases studies on topics such as Fintech, blockchain technology, financing renewable energy, and XBRL usage from sectors such health care, pharmacology, transportation, and education. Such a complex view of current economic phenomena makes the volume attractive not only for academia, but also for regulators and policy-makers, when deliberating the potential outcome of competing regulatory mechanisms.

Least Squares Support Vector Machines
Least Squares Support Vector Machines

Author: Johan A. K. Suykens

Publisher: World Scientific

Published: 2002

Total Pages: 318

ISBN-13: 9789812381514

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This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing spareness and employing robust statistics. The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. Furthermore, LS-SVM formulations are given for recurrent networks and control. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nystrom sampling with active selection of support vectors. The methods are illustrated with several examples.

Brain Function Assessment in Learning
Brain Function Assessment in Learning

Author: Claude Frasson

Publisher: Springer

Published: 2017-09-11

Total Pages: 229

ISBN-13: 3319676156

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This book constitutes the thoroughly refereed proceedings of the First International Conference on Brain Function Assessment in Learning, BFAL 2017, held in Patras, Greece, in September 2017. The 16 revised full papers presented together with 2 invited talks and 6 posters were carefully selected from 28 submissions. The BFAL conference aims to regroup research in multidisciplinary domains such as neuroscience, health, computer science, artificial intelligence, human-computer interaction, education and social interaction on the theme of Brain Function Assessment in Learning.

Financial Statement Analysis and the Prediction of Financial Distress
Financial Statement Analysis and the Prediction of Financial Distress

Author: William H. Beaver

Publisher: Now Publishers Inc

Published: 2011

Total Pages: 89

ISBN-13: 1601984243

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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.