Download or read book Statistical Techniques for Bankruptcy Prediction written by Volodymyr Perederiy and published by GRIN Verlag. This book was released on 2015-05-22 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master's Thesis from the year 2005 in the subject Business economics - Accounting and Taxes, grade: 1,0, European University Viadrina Frankfurt (Oder), course: International Business Administration, language: English, abstract: Bankruptcy prediction has become during the past 3 decades a matter of ever rising academic interest and intensive research. This is due to the academic appeal of the problem, combined with its importance in practical applications. The practical importance of bankruptcy prediction models grew recently even more, with “Basle-II” regulations, which were elaborated by Basle Committee on Banking Supervision to enhance the stability of international financial system. These regulations oblige financial institutions and banks to estimate the probability of default of their obligors. There exist some fundamental economic theory to base bankruptcy prediction models on, but this typically relies on stock market prices of companies under consideration. These prices are, however, only available for large public listed companies. Models for private firms are therefore empirical in their nature and have to rely on rigorous statistical analysis of all available information for such firms. In 95% of cases, this information is limited to accounting information from the financial statements. Large databases of financial statements (e.g. Compustat in the USA) are maintained and often available for research purposes. Accounting information is particularly important for bankruptcy prediction models in emerging markets. This is because the capital markets in these countries are often underdeveloped and illiquid and don’t deliver sufficient stock market data, even for public/listed companies, for structural models to be applied. The accounting information is normally summarized in so-called financial ratios. Such ratios (e.g. leverage ratio, calculated as Debt to Total Assets of a company) have a long tradition in accounting analysis. Many of these ratios are believed to reflect the financial health of a company and to be related to the bankruptcy. However, these beliefs are often very vague (e.g. leverages above 70% might provoke a bankruptcy) and subjective. Quantitative bankruptcy prediction models objectify these beliefs in that they apply statistical techniques to the accounting data. [...]

Download or read book A Comparison of Statistical Techniques and Artificial Neural Network Models in Corporate Bankruptcy Prediction written by Margaret Devine Dwyer and published by . This book was released on 1992 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Bankruptcy Prediction through Soft Computing based Deep Learning Technique written by Arindam Chaudhuri and published by Springer. This book was released on 2017-12-01 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book proposes complex hierarchical deep architectures (HDA) for predicting bankruptcy, a topical issue for business and corporate institutions that in the past has been tackled using statistical, market-based and machine-intelligence prediction models. The HDA are formed through fuzzy rough tensor deep staking networks (FRTDSN) with structured, hierarchical rough Bayesian (HRB) models. FRTDSN is formalized through TDSN and fuzzy rough sets, and HRB is formed by incorporating probabilistic rough sets in structured hierarchical Bayesian model. Then FRTDSN is integrated with HRB to form the compound FRTDSN-HRB model. HRB enhances the prediction accuracy of FRTDSN-HRB model. The experimental datasets are adopted from Korean construction companies and American and European non-financial companies, and the research presented focuses on the impact of choice of cut-off points, sampling procedures and business cycle on the accuracy of bankruptcy prediction models. The book also highlights the fact that misclassification can result in erroneous predictions leading to prohibitive costs to investors and the economy, and shows that choice of cut-off point and sampling procedures affect rankings of various models. It also suggests that empirical cut-off points estimated from training samples result in the lowest misclassification costs for all the models. The book confirms that FRTDSN-HRB achieves superior performance compared to other statistical and soft-computing models. The experimental results are given in terms of several important statistical parameters revolving different business cycles and sub-cycles for the datasets considered and are of immense benefit to researchers working in this area.

Download or read book Corporate Bankruptcy Prediction written by Błażej Prusak and published by MDPI. This book was released on 2020-06-16 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Bankruptcy Prediction written by Tonatiuh Peña and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Comparing Statistical Methods and Artificial Neural Networks in Bankruptcy Prediction written by and published by . This book was released on 1997 with total page 746 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Comparing Statistical Methods and Artificial Neural Networks in Bankruptcy Prediction written by Jung Chu and published by . This book was released on 1997 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Comparing Statistical Methods and Artificial Neural Networks in Bankruptcy Prediction written by Jung Chu and published by . This book was released on 1997 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Companies Bankruptcy Prediction by Using Altman Models and Comparing Them written by Mahmood Fahad Abd Ali and published by . This book was released on 2018 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bankruptcy prediction of economic institutions is considered a necessary matter at the present time in order to avoid the risks that may drive such institutions out of business. Given such fact, the current study was made to highlight the intellectual aspects of the subject of bankruptcy prediction and means of measuring it. There are five main types of models for predicting companies bankruptcy: one-way analysis of variance, multiple discriminant analysis, logarithmic analysis, recurrent algorithm analysis, and finally neural networks analysis, which is the most recent bankruptcy prediction method. These methods do not produce similar results. Most bankruptcy prediction studies used multiple discriminant analysis (MDA) and statistical methods for models development. These studies covered both large and small companies as well as private and public companies. MDA is the essence of this research paper which deals with Altman Model in detail and describes the changes that the original Z-Score equation has gone through. The study problem lies in arranging Altman Models for bankruptcy prediction of commercial companies in Iraq in accordance with the importance of each model.

Download or read book Probabilistic Methods for Financial and Marketing Informatics written by Richard E. Neapolitan and published by Elsevier. This book was released on 2010-07-26 with total page 427 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book What Can Modern Statistical and Mathematical Techniques Add to the Analysis and Prediction of Bankruptcy written by Sjur Westgaard and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper discusses the wide spectrum of statistical and mathematical methods for bankruptcy prediction that exists in the literature. We compare and analyse the applicability of the various methods in a scientific framework for financial econometrics. While many of the methods might add value in form of being a search or an explorative technique, only a few these techniques can add full value in the sense of enabling us to test and validate financial economic theories. In addition to predicting, we need models that make it possible to analyse and understand the problem of bankruptcy. Empirical models should be based on financial/economic theory and sound statistical properties. These models can be used as a communication tool between the credit analyst and the management and hence serve in a practical credit risk policy context.

Download or read book Financial Statement Analysis and the Prediction of Financial Distress written by William H. Beaver and published by Now Publishers Inc. This book was released on 2011 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Predictive Capability of Financial Ratios for Forecasting of Corporate Bankruptcy written by Saiful Islam, MD and published by . This book was released on 2020-09-07 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bankruptcy of a business firm is an event which results substantial losses to creditors and stockholders. A model which is capable of predicting an upcoming business failure will serve as a very useful tool to reduce such losses by providing warning to the interested parties. This was the main motivation for Beaver (1966) and Altman (1968) to construct bankruptcy prediction models based on the financial data (Deakin 1972). This research study also initiated with a great interest on this subject to investigate the predictive capability of financial ratios for forecasting of corporate distress and bankruptcy events. This study is expounded on similar previous studies by Altman (1968), Ohlson (1980), Beaver (1966) by examining the effectiveness of financial ratios for predicting of corporate distress. The logistics regression analysis (LRA) statistical method is used to scan the risk factors from the previous financial year data and prediction models are constructed which can reasonably classify the expected bankruptcy group and can reasonably predict the solvency status of a firm. The research has been focused on the USA companies only. A set of bankrupted and non-bankrupted company financial data are used for constructing the bankruptcy prediction model and then a second set of bankrupted and non-bankrupted company financial data has been used to test the classification accuracy of the constructed models. The result of this study is consistent with the previous bankruptcy prediction researches outcomes. This study also investigates the time factor implication of bankruptcy prediction models using 5 years financial ratios.

Download or read book Probability of Default Models of Russian Banks written by Anatoly Peresetsky and published by . This book was released on 2007 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Corporate Financial Distress and Bankruptcy written by Edward I. Altman and published by John Wiley & Sons. This book was released on 2010-03-11 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Forecasting Corporate Failure written by Andrew Wong and published by . This book was released on 2017 with total page 10 pages. Available in PDF, EPUB and Kindle. Book excerpt: The application of bankruptcy prediction models in litigation to evaluate whether a company was in default, or about to default, at a particular time can be helpful in a wide range of contexts when used appropriately, according to a recent AIRA Journal article authored by Managing Principal Andrew Wong and Manager Konstantin Danilov. In "Forecasting Corporate Failure: Understanding Statistical and Theoretical Approaches to Bankruptcy Prediction" (Vol. 29, No. 1, 2015), the authors describe two categories of bankruptcy prediction models -- statistical and theoretical -- and examine their use in litigation. "Statistical models attempt to identify the most common symptoms exhibited by bankrupt companies, and then use this information to estimate the likelihood that a particular firm will go bankrupt in the future," the authors explain. "Conversely, theoretical models predict bankruptcy by attempting to identify and gauge the factors responsible for the causes of bankruptcy."Noting that understanding "the strengths and weaknesses of these approaches is helpful when deciding which particular prediction model to apply in a bankruptcy setting," the authors describe the history of each model, explore their predictive performance and limitations, and examine several case examples. The authors conclude that, because of "differences in rationale, effectiveness, and applicability, each model is uniquely suited" to determine the likelihood that a company will go bankrupt. "As such, the applicability and validity of any approach will always depend on the context and the details of the particular case."

Download or read book Brain Function Assessment in Learning written by Claude Frasson and published by Springer. This book was released on 2017-09-11 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.