Accurate prediction of financial distress of companies with machine learning algorithms

Authors:
Armando S. Vieira;João Duarte;Bernardete Ribeiro;João C. Neves
Affiliations:
ISEP, Porto, Portugal;ISEP, Porto, Portugal;Depart. of Informatics Engineering, University of Coimbra, Coimbra, Portugal;ISEG, School of Economics, Lisboa, Portugal
Venue:
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Year:
2009

Citing 6
Cited 4

Neural network performance on the bankruptcy classification problem

Proceedings of the 15th annual conference on Computers and industrial engineering
The nature of statistical learning theory

The nature of statistical learning theory
Support-Vector Networks

Machine Learning
Gene Selection for Cancer Classification using Support Vector Machines

Machine Learning
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)

Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Bankruptcy prediction for credit risk using neural networks: A survey and new results

IEEE Transactions on Neural Networks

Multi-objective evolutionary algorithms for feature selection: application in bankruptcy prediction

SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
A stable credit rating model based on learning vector quantization

Intelligent Data Analysis
A robust learning model for dealing with missing values in many-core architectures

ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
Credit scoring for SME using a manifold supervised learning algorithm

IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

Prediction of financial distress of companies is analyzed with several machine learning approaches. We used Diane, a large database containing financial records from small and medium size French companies, from the year of 2002 up to 2007. It is shown that inclusion of historical data, up to 3 years priori to the analysis, increases the prediction accuracy and that Support Vector Machines are the most accurate predictor.