Artificial Intelligence in Economics and Managment

An Edited Proceedings on the Fourth International Workshop: AIEM4 Tel-Aviv, Israel, January 8–10, 1996

Editors: Ein-Dor, Phillip (Ed.)

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About this book

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.

Table of contents (18 chapters)

  • Using Machine Learning, Neural Networks and Statistics to Predict Corporate Bankruptcy: A Comparative Study

    Pompe, P. P. M. (et al.)

    Pages 3-19

  • Prolog Business Objects in a Three-Tier Architecture

    Schwartz, David G.

    Pages 21-31

  • The Effect of Training Data Set Size and the Complexity of the Separation Function on Neural Network Classification Capability: The Two-Group Case

    Leshno, Moshe (et al.)

    Pages 33-50

  • Imaginal Agents

    Schwartz, David G. (et al.)

    Pages 51-59

  • Financial Product Representation and Development Using a Rule-Based System

    Lange, Anja (et al.)

    Pages 63-75

Buy this book

eBook $129.00
price for USA in USD (gross)
  • ISBN 978-1-4613-1427-1
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $169.00
price for USA in USD
  • ISBN 978-0-7923-9761-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $159.00
price for USA in USD
  • ISBN 978-1-4612-8620-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Artificial Intelligence in Economics and Managment
Book Subtitle
An Edited Proceedings on the Fourth International Workshop: AIEM4 Tel-Aviv, Israel, January 8–10, 1996
Editors
  • Phillip Ein-Dor
Copyright
1996
Publisher
Springer US
Copyright Holder
Kluwer Academic Publishers
eBook ISBN
978-1-4613-1427-1
DOI
10.1007/978-1-4613-1427-1
Hardcover ISBN
978-0-7923-9761-8
Softcover ISBN
978-1-4612-8620-2
Edition Number
1
Number of Pages
X, 276
Topics