Artificial Intelligence in Economics and Managment : An Edited Proceedings on the Fourth International Workshop: AIEM4 Tel-Aviv, Israel, January 8-10, 1996
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.
- Hardback | 276 pages
- 154.94 x 238.76 x 22.86mm | 544.31g
- 01 Oct 1996
- Dordrecht, Netherlands
- 1996 ed.
- X, 276 p.
Table of contents
Foreword. Part I: Artificial Intelligence Techniques. Using Machine Learning, Neural Networks And Statistics to Predict Corporate Bankruptcy: A Comparative Study; P.P.M. Pompe, A.J. Feelder. Prolog Business Objects in a Three-Tier Architecture; D.G. Schwartz. The Effect of Training Data Set Size and the Complexity of the Separation Function on Neural Network Classification Capability: The Two-Group Case; M. Leshno, Y. Spector. Imaginal Agents; D.G. Schwartz, D. Te'eni. Part II: Financial Applications. Financial Product Representation and Development Using a Rule-Based System; A. Lange, et al. Applications of Artificial Intelligence and Cognitive Science Techniques in Banking; P. Lenca. Part III: Business Applications. AI-Supported Quality Function Deployment; Y. Reich. Knowledge Reuse in Mass Customization of Knowledge-Intensive Services; M. Benaroch. Harvest Optimization of Citrus Crop Using Genetic Algorithms; N. Levin, J. Zahavi. `Corpus', An Approach to Capitalizing Company Knowledge; M. Grundstein. Part IV: Economic Applications. Fuzzy Approach in Economic Modelling of Economics of Growth; V. Deinichenko, et al. Computer Based Analysis of an Economy in Transition to Steady State Equilibrium; K. Cichocki, T. Szapiro. A Multistrategy Conceptual Analysis of Economic Data; K.A. Kaufman, R.S. Michalski. The Credible Modeling of Economic Agents with Limited Rationality; B. Edmonds, S. Moss. Reasoning and A Programming Language for Simulating Economic and Business Processes with Artificially Intelligent Agents; B. Edmonds, et al. Part V: Qualitative and Cognitive Research. Information Processing, Motivation and Decision Making; L.M. Botelho, H. Coelho. A PracticalTool for Explanation of Quantitative Model Behavior; R. Berndsen. Practical Application of Artificial Intelligence in Education and Training; L. Dannhauser.