我國資本市場發展至今,起伏之間變化莫測,唯一不變的卻是始終存在著地雷股公司,在資本市場蓬勃發展的過程中,即好比一顆老鼠屎壞了一鍋粥,本研究即是在探討上市櫃公司發生財務危機之前,是否可由公司的財務資訊與非財務資訊中,推測出公司未來發生財務危機之可能性,避免投資者的血汗錢最終成為地雷公司爆破後的犧牲品。 近年來,由於總體經濟環境的快速變遷,造成企業發生財務危機的可能性逐年增加,因此,有效建立一套企業危機預警模式,是當前學術與實務界相當重視的課題。本研究運用灰關聯分析與類神經網路的預測方法來建立我國上市櫃公司財務預警模型,以提升預測的準確度。 本研究論文利用灰色理論與類神經網路來預測財務危機事件,以財務危機發生於2009年到2012年期間之台灣上市櫃公司,以相同產業、相同期間及規模相近的公司進行1:2的配對,並選取其發生財務危機前四季的財務變數與公司治理變數,透過灰關連分析篩選出影響公司治理與財務狀況的重要指標,作為類神經網路訓練的輸入變數,接著利用類神經網路訓練出最佳預測模式。實證結果發現財務指標於財務危機發生前一季預測率最佳,惟考量公司治理指標後於短期內較無法立即預測財務危機,整體來說整合財務指標與公司治理指標似乎需經中、長期後才具有較佳的預測能力。
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.