Visualization and dynamic evaluation model of corporate financial structure with self-organizing map and support vector regression

Authors:
Mu-Yen Chen
Affiliations:
Department of Information Management, National Taichung University of Science and Technology, 129 Sec. 3, San-Min Road, Taichung 40444, Taiwan, ROC
Venue:
Applied Soft Computing
Year:
2012

Citing 11
Cited 0

Self-organization and associative memory: 3rd edition

Self-organization and associative memory: 3rd edition
Self organizing neural networks for financial diagnosis

Decision Support Systems
A threshold-varying artificial neural network approach for classification and its application to bankruptcy prediction problem

Computers and Operations Research
Predicting corporate financial distress based on integration of support vector machine and logistic regression

Expert Systems with Applications: An International Journal
Shape-based image retrieval using support vector machines, Fourier descriptors and self-organizing maps

Information Sciences: an International Journal
Improved financial time series forecasting by combining Support Vector Machines with self-organizing feature map

Intelligent Data Analysis
A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting

Expert Systems with Applications: An International Journal
Using neural networks and data mining techniques for the financial distress prediction model

Expert Systems with Applications: An International Journal
A two-stage architecture for stock price forecasting by integrating self-organizing map and support vector regression

Expert Systems with Applications: An International Journal
Applying enhanced data mining approaches in predicting bank performance: A case of Taiwanese commercial banks

Expert Systems with Applications: An International Journal
Support vector self-organizing learning for imbalanced medical data

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Prediction of financial bankruptcy has been a focus of considerable attention among both practitioners and researchers. However, most research in this area has ignored the non-stationary nature of corporate financial structures. Specifically, financial structures do not always present consistent statistical tests at each point of time, resulting in dynamic relationships between financial structures and their predictors. This characteristic of financial bankruptcy presents a significant challenge for any single artificial prediction technique. Therefore, this paper will propose a multi-phased and dynamic evaluation model of the corporate financial structure integrating both the self-organizing map (SOM) and support vector regression (SVR) techniques. In the 1st phase, the inputs to the SOM are financial indicators derived from listed companies' public financial statements adopting the principle component analysis (PCA) to extract useful indicators with a strong influence that each year determines the company's position on the SOM. In addition, we used the SOM to visualize and cluster each corporate in the 2D map. We also investigated each cluster and classified them into healthy and bankrupt-prone ones based on their regions in visualizing the 2D map. In the 2nd phase, we drew the trajectory for the healthy and the bankrupt-prone companies for consecutive years in a 2D map. Therefore, several visualized and dynamic patterns of corporate behavior could be recognized. In the 3rd phase, we used the SVR method to forecast the future trend for corporate financial structure. In addition, this research also compared the hybrid SOM-SVR architecture with single SOM, SVR, and Learning Vector Quantization (LVQ) algorithms. The results showed that the proposed methodology outperformed the other methods in both prediction accuracy and ease of use.