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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

  • Conference proceedings
  • © 1996

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Editors:
  1. Phillip Ein-Dor

    1. Tel-Aviv University, Israel

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Table of contents (18 papers)

  1. Front Matter

    Pages i-x
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  3. Financial Applications

    1. Front Matter

      Pages 61-61
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    2. Financial Product Representation and Development Using a Rule-Based System

      • Anja Lange, Juergen Seitz, Eberhard Stickel
      Pages 63-75

    3. Applications of Artificial Intelligence and Cognitive Science Techniques in Banking

      • Philippe Lenca
      Pages 77-90

  4. Business Applications

    1. Front Matter

      Pages 91-91
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    2. AI-Supported Quality Function Deployment

      • Yoram Reich
      Pages 93-106

    3. Knowledge Reuse in Mass Customization of Knowledge-Intensive Services

      • Michel Benaroch
      Pages 107-127

    4. Harvest Optimization of Citrus Crop Using Genetic Algorithms

      • Nissan Levin, Jacob Zahavi
      Pages 129-138

    5. “CORPUS,” An Approach to Capitalizing Company Knowledge

      • Michel Grundstein
      Pages 139-152

  5. Economic Applications

    1. Front Matter

      Pages 153-153
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    2. Fuzzy Approach in Economic Modelling of Economics of Growth

      • V. Deinichenko, G. Bikesheva, A. Borisov
      Pages 155-173

    3. Computer Based Analysis of an Economy in Transition to Steady State Equilibrium

      • Krzysztof Cichocki, Tomasz Szapiro
      Pages 175-191

    4. A Multistrategy Conceptual Analysis of Economic Data

      • Kenneth A. Kaufman, Ryszard S. Michalski
      Pages 193-203

    5. The Credible Modelling of Economic Agents with Limited Rationality

      • Bruce Edmonds, Scott Moss
      Pages 205-219

    6. Logic, Reasoning and a Programming Language for Simulating Economic and Business Processes with Artificially Intelligent Agents

      • Bruce Edmonds, Scott Moss, Steve Wallis
      Pages 221-230
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Keywords

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.

Editors and Affiliations

  • Tel-Aviv University, Israel

    Phillip Ein-Dor

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