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A Comparison Of Statistical Techniques And Artificial Neural Network Models In Corporate Bankruptcy Prediction
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Book Synopsis A Comparison of Statistical Techniques and Artificial Neural Network Models in Corporate Bankruptcy Prediction by : Margaret Devine Dwyer
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:
Book Synopsis Statistical Techniques for Bankruptcy Prediction by : Volodymyr Perederiy
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. [...]
Book Synopsis Comparing Statistical Methods and Artificial Neural Networks in Bankruptcy Prediction by : Jung Chu
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:
Book Synopsis Comparing Statistical Methods and Artificial Neural Networks in Bankruptcy Prediction by :
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:
Book Synopsis Corporate Bankruptcy Prediction by : Błażej Prusak
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.
Book Synopsis Bankruptcy Prediction through Soft Computing based Deep Learning Technique by : Arindam Chaudhuri
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.
Book Synopsis Comparing Statistical Methods and Artificial Neural Networks in Bankruptcy Prediction by : Jung Chu
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:
Book Synopsis Artificial Intelligence in Economics and Managment by : Phillip Ein-Dor
Download or read book Artificial Intelligence in Economics and Managment written by Phillip Ein-Dor and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the past decades several researchers have developed statistical models for the prediction of corporate bankruptcy, e. g. Altman (1968) and Bilderbeek (1983). A model for predicting corporate bankruptcy aims to describe the relation between bankruptcy and a number of explanatory financial ratios. These ratios can be calculated from the information contained in a company's annual report. The is to obtain a method for timely prediction of bankruptcy, a so ultimate purpose called "early warning" system. More recently, this subject has attracted the attention of researchers in the area of machine learning, e. g. Shaw and Gentry (1990), Fletcher and Goss (1993), and Tam and Kiang (1992). This research is usually directed at the comparison of machine learning methods, such as induction of classification trees and neural networks, with the "standard" statistical methods of linear discriminant analysis and logistic regression. In earlier research, Feelders et al. (1994) performed a similar comparative analysis. The methods used were linear discriminant analysis, decision trees and neural networks. We used a data set which contained 139 annual reports of Dutch industrial and trading companies. The experiments showed that the estimated prediction error of both the decision tree and neural network were below the estimated error of the linear discriminant. Thus it seems that we can gain by replacing the "traditionally" used linear discriminant by a more flexible classification method to predict corporate bankruptcy. The data set used in these experiments was very small however.
Book Synopsis Comparative Analysis of Artificial Neural Network Models by : Christakis Charalambous
Download or read book Comparative Analysis of Artificial Neural Network Models written by Christakis Charalambous and published by . This book was released on 2001 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study compares the predictive performance of three neural network methods, namely the Learning Vector Quantization, the Radial Basis Function, and the Feedforward network that uses the conjugate gradient optimization algorithm, with the performance of the logistic regression and the backpropagation algorithm. All these methods are applied to a dataset of 139 matched-pairs of bankrupt and non-bankrupt U.S firms for the period 1983-1994. The results of this study indicate that the contemporary neural network methods applied in the present study provide superior results to those obtained from the logistic regression method and the backpropagation algorithm.
Book Synopsis Companies Bankruptcy Prediction by Using Altman Models and Comparing Them by : Mahmood Fahad Abd Ali
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.
Book Synopsis Corporate Bankruptcy Prediction in the Republic of Serbia by : Nemanja Stanisic
Download or read book Corporate Bankruptcy Prediction in the Republic of Serbia written by Nemanja Stanisic and published by . This book was released on 2013 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis The Application of Altman, Zmijewski and Neural Network Bankruptcy Prediction Models to Domestic Textile-related Manufacturing Firms by : Paula M. Weller
Download or read book The Application of Altman, Zmijewski and Neural Network Bankruptcy Prediction Models to Domestic Textile-related Manufacturing Firms written by Paula M. Weller and published by . This book was released on 2010 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis A Comparison of Artificial Neural Network Model and Logistics Regression in Prediction of Companies' Bankruptcy (A Case Study of Tehran Stock Exchange). by : Ali Mansouri
Download or read book A Comparison of Artificial Neural Network Model and Logistics Regression in Prediction of Companies' Bankruptcy (A Case Study of Tehran Stock Exchange). written by Ali Mansouri and published by . This book was released on 2016 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper aims to focus on the comparison of the artificial neural network model and logistic regression model in the prediction of companies' bankruptcy in Tehran stock exchange (TSE) in 3, 2 and 1 year in advance. This study exercises an analytic-mathematical approach which has been utilized three-layer artificial neural network tools, which includes one hidden layer and one output neuron and logistic regression (LR) with seven independent variable and one dependent variable for testing research's hypotheses. Although the given results illustrates the high potential capacities of both models in the prediction of bankruptcies in an interval of three years, two years and one year before bankruptcy, capacity of neural network model showed the relative higher capability than LR model. This study takes into consideration the comparison of two popular tools of artificial neural networks (ANNs) and LR in bankruptcy prediction that are of importance in their own type.
Author :Robert E. Dorsey Publisher :Research Foundation of the Institute of Chartered Financial Analysts ISBN 13 : Total Pages :68 pages Book Rating :4.E/5 ( download)
Book Synopsis Bankruptcy Prediction Using Artificial Neural Systems by : Robert E. Dorsey
Download or read book Bankruptcy Prediction Using Artificial Neural Systems written by Robert E. Dorsey and published by Research Foundation of the Institute of Chartered Financial Analysts. This book was released on 1995 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Comparing Classification Models for Bankruptcy Prediction by : Arben Hasanaj
Download or read book Comparing Classification Models for Bankruptcy Prediction written by Arben Hasanaj and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This study adds to the large body of literature that aims to predict corporate bankruptcy. It does so by evaluating two established machine learning methods which have shown promising results in earlier studies, namely Support Vector Machines (SVM) and Artificial Neural Networks, specifically Multi-Layer Perceptrons (MLP). Furthermore, Bagging and AdaBoost ensemble variations of these models are tested which have been proposed to improve prediction performance. The unique features of the present study are the sampled companies and the sample size: it focuses on unlisted, smaller companies from Western Europe and comprises 46'857 firm-year observations from 2013 to 2017, of which 7'095 or around 15% represent observations preceding bankruptcy of the respective firm. The used variables are mainly financial ratios which have shown predictive value before, general firm characteristics, and three variables proposed for the special case of unlisted SMEs (age of company, country of domicile, and GDP growth rate). In this regard the MLP models clearly outperform the SVM models and the ensemble variations are also generally able to increase the prediction performance. Nevertheless, the achieved performance level is not deemed good enough for a practical implementation of the models as they are. Based on the findings, the author suggests investing in the collection of high-quality samples, considering different model architectures (Decision Trees or heterogeneous ensembles), as well as scrutinizing the role of variables that capture the business conditions for firms.
Book Synopsis Corporate Failure Prediction Using Neural Network Techniques by : Rob Hope
Download or read book Corporate Failure Prediction Using Neural Network Techniques written by Rob Hope and published by . This book was released on 1997 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many published studies of corporate failure prediction claim a high degree of accuracy, often over 90%, in predicting failure on the basis of only a small number of financial ratios. This study uses a uniquely large sample to determine how dramatically increased sample size, allowing better estimates of accuracy and more thorough out of sample validation, effects these results. Models such as Altman's Z score are found to perform poorly on the large sample. Significant improvements are possible through the introduction of new data. This study includes payment behaviour in several models, and this is shown to have a strong positive effect. Neural networks are relatively new in this area. Some comparative studies have been made, with conflicting results. This study looks in detail at their performance relative to accepted methods such as logistic regression. Neural networks are shown to have some powerful properties, but their use in failure prediction seems to offer no improvement over the conventional methods, at least using the methodologies tested here. Further research isjudged necessary. Finally, the study examines the form of the financial data used in traditional models. Constructing trend data is shown to be useful, and different forms of this are examined. The transformation of data is examined in some detail. Various transformations are discussed, including a new function, the hyperbolic tangent or tanh. Transformation of data is found to be very effective in improving a model.
Book Synopsis Developments in Applied Artificial Intelligence by : Paul Chung
Download or read book Developments in Applied Artificial Intelligence written by Paul Chung and published by Springer Science & Business Media. This book was released on 2003-06-11 with total page 831 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 16th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2003, held in Loughborough, UK in June 2003. The 81 revised full papers presented were carefully reviewed and selected from more than 140 submissions. Among the topics addressed are soft computing, fuzzy logic, diagnosis, knowledge representation, knowledge management, automated reasoning, machine learning, planning and scheduling, evolutionary computation, computer vision, agent systems, algorithmic learning, tutoring systems, financial analysis, etc.
