Artificial Intelligence in Economics and Managment

Name: Artificial Intelligence in Economics and Managment
Brand: Springer
SKU: 9780792397618
Price: 39416 HUF
Availability: InStock

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

Szerző: Phillip Ein-Dor

Nyelv:

Kötés: Kemény kötésű

Kiadó: Springer

Elérhetőség: Beszállítói készleten

Küldés 10-13 napon belül

39 416 Ft

In the past decades several researchers have developed statistical models for the prediction of corp...

Információk a könyvről

Szerző

Phillip Ein-Dor

Nyelv

Angol

Kötés

Könyv - Kemény kötésű

Kiadva

1996

oldal

276

EAN

9780792397618

ISBN

0792397614

Enbook ID

05251385

Kiadó

Springer

Súly

1290

Méretek

155 x 235 x 20

Teljes leírás

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.

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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

Információk a könyvről

Teljes leírás

Érdekelheti

Azok a vásárlók, akik ezt a könyvet megvásárolták, a következőket is megvásárolták