The use of genetic programming for the construction of a financial management model in an enterprise
The fast development in China's economy has caused the rapid expansion of the domestic market. Since many economists do not have optimistic views regarding the bubble economy of China, it is necessary for Taiwanese businessmen to understand in-depth the business operational performance and financial situation of enterprises in China, so as to reduce the risk of a potential investment. In this article, data from the China Economic Research Database (CCER), the financial database of financial corporations are collected for analysis to investigate the business operation and management performance and financial characteristic of enterprises in China. In this article, grey relational analysis is applied first in order to investigate the business operational performance of 600 enterprises in China. Afterwards, a more recent clustering technique is used to divide, based on financial characteristic, enterprises in China into two groups. Finally, three models, namely genetic programming, Back-Propagation Neural Network and Logistic Regression are adopted to construct an Enterprise Operational Performance model and an Enterprise Finance Characteristic model, respectively. Based on the results found, it can be concluded that genetic programming yielded the best classification and forecast performance, compared to the other three techniques.
genetic programming grey relational analysis back-propagation neural network cluster analysis data mining