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Bankruptcy Prediction Using Artificial Neural Systems
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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 Artificial Neural Networks in Financial Crises and Bankruptcy Prediction by : Yi-Fong Lin
Download or read book Artificial Neural Networks in Financial Crises and Bankruptcy Prediction written by Yi-Fong Lin and published by . This book was released on 2006 with total page 102 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 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 Engineering Applications of Neural Networks by : John Macintyre
Download or read book Engineering Applications of Neural Networks written by John Macintyre and published by Springer. This book was released on 2019-05-14 with total page 546 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 19th International Conference on Engineering Applications of Neural Networks, EANN 2019, held in Xersonisos, Crete, Greece, in May 2019. The 35 revised full papers and 5 revised short papers presented were carefully reviewed and selected from 72 submissions. The papers are organized in topical sections on AI in energy management - industrial applications; biomedical - bioinformatics modeling; classification - learning; deep learning; deep learning - convolutional ANN; fuzzy - vulnerability - navigation modeling; machine learning modeling - optimization; ML - DL financial modeling; security - anomaly detection; 1st PEINT workshop.
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 Neural Networks in Finance and Investing by : Robert R. Trippi
Download or read book Neural Networks in Finance and Investing written by Robert R. Trippi and published by Irwin Professional Publishing. This book was released on 1996 with total page 872 pages. Available in PDF, EPUB and Kindle. Book excerpt: This completely updated version of the classic first edition offers a wealth of new material reflecting the latest developments in teh field. For investment professionals seeking to maximize this exciting new technology, this handbook is the definitive information source.
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 Combining Pattern Classifiers by : Ludmila I. Kuncheva
Download or read book Combining Pattern Classifiers written by Ludmila I. Kuncheva and published by John Wiley & Sons. This book was released on 2004-08-20 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covering pattern classification methods, Combining Classifiers: Ideas and Methods focuses on the important and widely studied issue of how to combine several classifiers together in order to achieve improved recognition performance. It is one of the first books to provide unified, coherent, and expansive coverage of the topic and as such will be welcomed by those involved in the area. With case studies that bring the text alive and demonstrate 'real-world' applications it is destined to become essential reading.
Book Synopsis Bankruptcy Prediction Using Ex Ante Neural Networks and Realistically Proportioned Testing Sets by : Marilyn M. Greenstein-Prosch
Download or read book Bankruptcy Prediction Using Ex Ante Neural Networks and Realistically Proportioned Testing Sets written by Marilyn M. Greenstein-Prosch and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of this study is to assess the viability of a neural network in the prediction of bankruptcy, using a more realistic setting than has generally been examined in the past. In order for neural network bankruptcy prediction models to be useful and relevant, they must be designed to predict in realistic settings, rather than carefully designed matched-pair settings. The data sets which are tested in this study reflect the true proportion of firms that actually fail, which is less than one percent of all firms. Further, all models are developed ex ante to test subsequent years' data sets. Comparison of neural network predictive power with that from traditional logit models indicate that logit models remain a viable alternative to neural networks. The results also indicate that need for continuous updating of bankruptcy prediction models, a task to which neural networks are well adopted.
Book Synopsis Bankruptcy Prediction of Banks by : Surbhi Dhama
Download or read book Bankruptcy Prediction of Banks written by Surbhi Dhama and published by . This book was released on 2020 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper provides a review of literature on the use of Artificial Neural Networks as a statistical tool for predicting the bankruptcy in Indian Private sector banks.
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 Brain Function Assessment in Learning by : Claude Frasson
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.
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 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 Neural Networks in Business Forecasting by : G. Peter Zhang
Download or read book Neural Networks in Business Forecasting written by G. Peter Zhang and published by IGI Global. This book was released on 2004-01-01 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting is one of the most important activities that form the basis for strategic, tactical, and operational decisions in all business organizations. Recently, neural networks have emerged as an important tool for business forecasting. There are considerable interests and applications in forecasting using neural networks. Neural Networks in Business Forecasting provides for researchers and practitioners some recent advances in applying neural networks to business forecasting. A number of case studies demonstrating the innovative or successful applications of neural networks to many areas of business as well as methods to improve neural network forecasting performance are presented.
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.
