The research of Self-organizing maps combined with backpropagation neural network applied to financial distress prediction model.

• Main Authors: Yan-Siao Lai, 賴彥曉
• Other Authors: Pei-how Huang
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/29910541684122769444

碩士 === 國立中山大學 === 企業管理學系研究所 === 98 === With the advance of computer science, the processing power and speed of computer increase dramatically. Such improvement also allows Artificial intelligence a feasible tool to assist on dealing with complex problem or scenario. In recent year, the neural network has become an important methodology in Artificial intelligence technology field. It is capable of producing good referential materials for categorizing and predicting financial crisis rights worth.After Financial Crisis, many unhealthy companies busted out emerging financial crisis and led to the crash of the stock market. For our study, we try to build early warning System (EWS) for predicting financial distress. In out research, a neural network to categorized Self-Organizing Map and combine with predicted Back-Propagation Neural Networks to produce Hybrid-Neural Networks Forecast Model is applied. This study also compares and checks Hybrid-Neural Networks Forecast Model with simple Back-Propagation Neural Networks model. The predicting models in this investigation employed 16 financial variables, selected in previous research on financial distress, as input variables. For our results pointed out that the accuracy rate with the predicting model of self-organizing map neural network combined with Back-propagation neural network was much better than the control group which only adopted back-propagation neural network models. The thesis attempts to apply clustering technique, which to grasp the environmental changes, to make a dynamic learning and to provide investors more explicit company information to support decision-makers to do the correct choice. key word: Self-organizing map、 backpropagation neural network、 financial distress prediction model