Constructing the Financial Distress Prediction Model Using General Regression Neural Networks

• Main Authors: Kuan-Jen Tseng, 曾冠人
• Other Authors: Lee-Ing Tong
• Format: Others
• Language: zh-TW
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/9d6wtx

碩士 === 國立交通大學 === 工業工程與管理系所 === 92 === Investors always encounter a loss when the financial distress of enterprises occurs. Hence, to prevent the investitive loss, establishing a financial distress warning model for investors is necessary. Many studies used various methods such as: dichotomons classification test, discriminate analysis, Probit analysis, Logit analysis, Back-propagation Neural Networks(BPNN), Fuzzy theory etc, were employed to establish the model. Because of some specific properties of financial ratio variables, all of above methods suggested to use the BPNN method to get high accuracy on financial distress warning model. However, a recently developed neural network, general regression neural networks (GRNN), has been proven to have a higher predictive power than BPNN. Therefore, this study utilizes the GRNN method to establish the financial distress warning model based on a real set of financial ratio data and compares the effectiveness of both methods. The results indicate that the GRNN model has better early warning accuracy than that of the BPNN model.