財務危機預測問題一直以來都是重要且被廣泛討論的問題，包含分類器的使用及特徵篩選方法的選擇，都是全球金融界及研究學者關注的問題。我們過去已在台灣公司資料下證明出使用公司治理指標及財務指標當特徵會比單純用財務指標當特徵得到較好的預測結果。但卻發現美國公司方面的探討並不完整，因此我們想知道在美國公司資料下，將公司治理指標加入以財務指標為主的特徵集中，是否也可以改善預測準確率？
　　在本研究中，利用z_score及block作為特徵，提出一個利用門檻值判斷危機公司的Rule-based預測模型。在美國公司資料下，能顯著提升原本只使用z_score的模型的準確率。同時，本論文也介紹如何手動收集美國公司的CGIs的方法。

Financial distress problem (FDP) has been important and widely studied topic. According different classifiers, feature selection methods even the ensemble learning have been discussed.
The past research had proved CGIs can improve predict model in Taiwan firms dataset, so we want to know if CGIs could improve predict model in US firms dataset.
We use z-score and block to be feature, and propose a new model to judge company by threshold. It can improve the accuracy of FDP in US dataset. We also introduce how to hand collect CGIs of US company.

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