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Author: Mahmood Fahad Abd Ali
Publisher:
ISBN:
Category :
Languages : en
Pages : 17
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Book Description
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.
Author: Mahmood Fahad Abd Ali
Publisher:
ISBN:
Category :
Languages : en
Pages : 17
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Book Description
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.
Author: Błażej Prusak
Publisher: MDPI
ISBN: 303928911X
Category : Business & Economics
Languages : en
Pages : 202
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Book Description
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.
Author: Paula M. Weller
Publisher:
ISBN:
Category :
Languages : en
Pages : 480
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Book Description
Some of the largest United States bankruptcies of publicly-traded non-financial firms have occurred within the last decade. The continuing need to improve bankruptcy prediction has generated numerous research studies utilizing various prediction models. The purpose of this study is to test the usefulness of the multiple discriminant, probit, and artificial neural network (ANN) models in predicting bankruptcy in the United States textile-related industry. Financial data is examined for 47 bankrupt and 104 non-bankrupt publicly-traded firms in the textile-related industry during the time period 1998-2004, which includes the events of the Asian currency crisis and increased competition from China. Models developed by Altman (1968), Altman (1983), Zmijewski (1984) are compared to ANNs based upon each of these models. A comparison to an ANN including all of the ratios of the previous models and variables for firm size and domestic sales is also made. The Altman (1968) model and ANN 68 model are found to have the higher predictive power for one and two years prior to bankruptcy, respectively, for bankrupt firms. The ANN 84 model and the ANN 83 model have the highest correct classification results for nonbankrupt firms for the entire time period. Solvency and leverage variables appear to have the most impact on the bankruptcy prediction of textile-related firms. The additional variables of firm size and domestic sales are not found to improve the predictive accuracy. This study supports the continued use of the original Altman (1968) model for predicting bankruptcy in a manufacturing industry. Simultaneous utilization of the ANN 83 model to predict nonbankrupt firms is also suggested since the majority of the Altman (1968) variables can be used and the higher potential for improved predictability. This study may be extended to years after 2004 with consideration given to quarterly information, NAICs codes, and leverage variable alternatives.
Author: Volodymyr Perederiy
Publisher: GRIN Verlag
ISBN: 3656965919
Category : Business & Economics
Languages : en
Pages : 106
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Book Description
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. [...]
Author: Edward I. Altman
Publisher:
ISBN:
Category : Business & Economics
Languages : en
Pages : 408
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Book Description
"A Wiley-Interscience publication."Includes index. Bibliography: p. 355-361.
Author: Edward I. Altman
Publisher:
ISBN:
Category :
Languages : en
Pages : 47
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Book Description
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.
Author: William H. Beaver
Publisher: Now Publishers Inc
ISBN: 1601984243
Category : Business & Economics
Languages : en
Pages : 89
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Book Description
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.
Author: Nemanja Stanisic
Publisher:
ISBN:
Category :
Languages : en
Pages : 15
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Book Description
The aim of this paper is to present corporate default prediction models constructed in the specific market conditions that prevail in the Republic of Serbia, and to compare their prediction accuracy with the most frequently used model - Altman's Z-score. Many authors have constructed models for the purpose of bankruptcy prediction, but predominantly in stable market conditions or in times of economic growth. We have presented three models that use standard ratios and some specific variables in order to predict corporate bankruptcy in emerging and distressed markets. For that purpose, we have used the following statistical and machine learning methods on a training sample (130 companies): Logistic Regression, Decision Trees and Artificial Neural Networks. Finally, we have compared accuracies of predictions of our models to those of the Altman's Z-score models using an independent hold-out sample (102 companies). Results show that, out of the aforementioned three models, only the one relying on the artificial neural network algorithm performs better when applied on the hold-out sample, compared to Altman's Z-score models.
Author: Olga Maria Stefania Cucaro
Publisher: Olga Maria stefania Cucaro
ISBN: 882959167X
Category : Business & Economics
Languages : en
Pages : 36
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Book Description
The bankruptcy prediction model Z-ScoreM for Italian Manufacturing Listed Companies and Z'-ScoreM for Italian Industrial Company. The work stems from the study of the probability of default started in 2007 and continues today. In particular, this analysis is taken up with the study of the Rating and the credit and liquidity risk carried out during the author's research doctorate. The study is the continuation of other recently published author's e-books. The main objective is to identify a model for Italian companies based on Altman's Z-Score variables. Several researchers have analyzed the probability of failure of large companies, listed or emerging markets, other authors have tried to create a dashboard useful for the analysis of key indicators to be monitored, but this research differs for the creation of a specific indicator for the Italian Industrial Companies based on Altman variables.
Author: Edward I. Altman
Publisher: John Wiley & Sons
ISBN: 1118046048
Category : Business & Economics
Languages : en
Pages : 314
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Book Description
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.
