International Journal of Computational Intelligence Research (IJCIR)

Volume 3, Number 1 (2007)


Application of neural networks to business bankruptcy analysis in Thailand

Kingkarn Sookhanaphibarn
Advanced Virtual and Intelligent Computing Center Faculty of Sciences, Chulalongkorn University Phayathai Rd., Pathumwan, Bangkok 10330 Thailand. 

Piruna Polsiri
Faculty of Business Administration and DPU International College Dhurakij Pundit University 110/14 Prachachuen Rd., Laksi, Bangkok 10210 Thailand. 

Worawat Choensawat
Faculty of Information Technology, Dhurakij Pundit University, 110/14 Prachachuen Rd., Laksi Bangkok 10210 Thailand.

Frank C. Lin
Dept. of Mathematics and Computer Science University of Maryland Eastern Shore Princess Anne, MD. 21853, U.S.A


The recent East Asian economic crisis is a lesson one can learn from the absence of effective early warning systems. To serve as a sound early warning signal, the accuracy of a failure prediction model is as important as its robustness over time. This study analyses financial and ownership variables using principal component analysis. It can reduce huge number of financial data of the business bankruptcy prediction problem. Using neural networks for bankruptcy forecasting, the obtained features are fed into neural networks as the input data. Our experiments examine the predictive performance of three neural networks: Learning Vector Quantization, Probabilistic Neural Network, and Feedforward network with backpropagation learning. All these approaches are applied to data sets of 41 Thai financial institutions for the period 19932003.

Bankrupcy, Neural networks, Thailand, Time series prediction, financial variables, Principal component analysis (PCA).