美國在2007年末的次級房貸事件，引發全球金融海嘯，造成房市、股市的巨幅下跌。迫使企業及工廠以裁員的手段來節省公司的開銷，最終造成許多企業不敵環境的劇烈變化而引發財務危機。過去對於財務危機的研究，多半以考慮公司的財務狀況為主。近年來則搭配公司治理變數、總體經濟變數與外部評等變數等建立公司財務危機預警模型。
不同於以往的研究，本研究考慮在企業發生危機以前，報章雜誌中所透露有關公司的相關報導的文本資料，運用文字探勘技術，透過卷積神經網路模型萃取新聞特徵變數。接著蒐集公司財務資訊、公司治理、總體經濟、外部評等變數等，採用隨機森林之特徵挑選方法，從中挑選較重要的15個變數，最後結合新聞特徵變數與15個選取變數來建立財務危機預測模型。本研究運用卷積神經網路結合支援向量機(CNN-SVM)與卷積神經網路結合邏輯斯迴歸(CNN-LR)來建立分類模型，並採用五次交叉驗證，計算各分類模型的準確度。研究結果顯示，使用卷積神經網路結合支援向量機的預測模型，在預測企業是否有財務危機的表現較為優異，準確度(Accuracy)達92.51%，精確度(Precision)、召回率(Recall)、F1-measure等皆高於93%，同時也比較以往只使用15個研究變數所建立的預測模型，結果顯示本研究所採用方法具較高的準確度。

In late 2007, the subprime mortgage event in the United States caused the global housing markets and the stock markets to plummet. As a consequence, many companies lay off employees to reduce their operating cost. Ultimately, some companies could not withstand the crisis and distressed. Most of the existing researches considered only the financial soundness of a company in building a financial crisis prediction model. Recent researches considered corporate governance variables, macro-economic variables and external rating variables in building a prediction model.
Unlike the existing researches, in this thesis we considered the textual information in the daily news or posts on the webs in building the prediction model. We used text mining technique and convolutional neural network to extract from the daily news and web posts a new textual variable to build the prediction model. Furthermore, we collected totally 96 financial and non-financial prediction variables and then used the random forests method to select 15 most important variables as the final prediction variables. With the textual variable and the 15 selected variables, we built two different prediction models using Convolution Neural Network plus Support Vector Machine (CNN-SVM) and Convolution Neural Network plus Logistic Regression (CNN-LR). The experiment results showed that the CNN-SVM achieved the highest prediction accuracy of 92.51%. Furthermore, all the precision, recall and f-measure of the CNN-SVM were higher than 93%. We also compared our models with the other models which did not consider the textual variable. The experimental results showed that our models prevail over the other models in prediction accuracy.

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