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Artificial Intelligence in Economics and Managment - An Edited Proceedings on the Fourth International Workshop: AIEM4 Tel-Aviv, Israel, January 8-10, 1996 (Hardcover, 1996 ed.) Loot Price: R2,953
Discovery Miles 29 530
Artificial Intelligence in Economics and Managment - An Edited Proceedings on the Fourth International Workshop: AIEM4...

Artificial Intelligence in Economics and Managment - An Edited Proceedings on the Fourth International Workshop: AIEM4 Tel-Aviv, Israel, January 8-10, 1996 (Hardcover, 1996 ed.)

Phillip Ein-Dor

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Loot Price R2,953 Discovery Miles 29 530 | Repayment Terms: R277 pm x 12*

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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."

General

Imprint: Springer
Country of origin: Netherlands
Release date: 2001
First published: 1996
Editors: Phillip Ein-Dor
Dimensions: 235 x 155 x 17mm (L x W x T)
Format: Hardcover
Pages: 276
Edition: 1996 ed.
ISBN-13: 978-0-7923-9761-8
Categories: Books > Business & Economics > Economics > General
Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN: 0-7923-9761-4
Barcode: 9780792397618

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