| Summary: | 碩士 === 義守大學 === 工業管理學系 === 103 === Due to the rapid changes of the overall economic environment, possible financial distress increases in a corporation every year recently. Therefore, how to establish an effective early warning model of a business crisis is a relatively important issue for a corporation. In this thesis, the grey correlation analysis and neural network forecasting models were established to predict possible financial crises of a corporation for early warning.
In this research, companies who listed in the Taiwan Stock Exchange and faced financial crisis during 2009 and 2012 were investigated. Other companies in the same industry with good financial conditions were compared with those who had financial crises at the same periods. Financial indicators and corporate governance variables for the last four seasons were studied. The grey relational analysis was used to filter out the most important factors that will affect the company’s financial conditions. Then, two neural networks were trained to find out the best forecasting model for financial indicators and corporate governance variables. Our results showed that the best predictive model was the model only used financial indicators from last season. However, the model incorporated both financial indicators and corporate governance variables may be considered for a long term forecasting.
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