1999年新巴塞爾協定規定鼓勵銀行採用內部信用評等法(internal ratings based approach)，以衡量貸款者無法償還之風險以計提最低資本。為因應此一授信風險控管之需要，銀行亟需建立一套有效之財務危機預警系統，以判定銀行授信客戶發生財務危機之機率。 本研究運用羅吉斯迴歸分析(logistic regression analysis)與離散時間涉險分析(discrete-time survival analysis)分法於三種相互具有巢狀式關連性之財務危機預測模型，逐步加入財務、非財務及公司治理變數，以便在同一種分析方法下比較三種模型，以及在同一種模型下比較兩種分析方法。實證結果顯示，就樣本期間內而言，同一種分析方法下模型之財務危機預測能力，隨著不同種類解釋變數之加入而逐步提高。然而，就樣本期間外而言，同一種分析方法下模型之財務危機預測能力，並未隨著不同種類解釋變數之加入而逐步提高，但分類能力皆十分優良；而在同一種模型下離散時間涉險分析方法之整體分類能力皆高於羅吉斯迴歸分析方法。 / The 1999 Basel II Accord suggests banks measure the impossibility of reimbursement of debtors to calculate capital minimums by internal ratings-based approach. To reduce the credit risk, it is important that banks construct accurate financial distress prediction systems to determine the probability of financial distress of debtors. This study employs logistic regression and discrete-time hazard analysis to construct nested models to which the financial, non-financial, and corporate governance corporate variables are added step by step. I therefore make comparison of the performance of three models under logistic regression and discrete-time hazard analysis, respectively. Meanwhile, the comparison of the performance of logistic regression and discrete-time hazard analyses under each of three models is also made. The empirical results show that the in-sample predictive ability of financial distress is enhanced by gradually incorporating different kinds of variables in both analyses. Although the out-of-the-sample predictive ability of financial distress is not improved by gradually incorporating different kinds of variables in one analysis, the model performance is quite well overall. The entire discriminability of discrete-time hazard analysis is better than logistic regression under each model.