企業發生財務危機，不僅使其經營陷入生死關頭之掙扎，更影響眾多投資人、債權人的利益，對於整個經濟環境亦造成一定的衝擊。因此，如何提早察覺企業之危機，以減少社會成本，實值得我們深入研究。 本研究主要目的為評估現金流量表揭露之資訊，於預測企業財務危機的有用性。本研究欲探討現金流量資訊是否為預測企業財務危機的良好指標，於建構企業財務危機預警模式之際，加入現金流量的財務指標是否會比僅以傳統財務比率建立之預警模式，更具預測能力。 本研究採用配對樣本設計，在我國上市公司中共選取了35家危機公司與68家正常公司。並利用Logit迴歸分析分別建立現金流量模式、應計財務模式與綜合模式，得到以下結論： 一、在財務危機發生之前一至三年，本研究所使用的應計基礎財務比率並非皆適合用來區分危機公司與正常公司。 二、除了營業活動現金流量相關比率具有顯著的區別能力外，部分投資與融資活動現金流量相關比率亦提供額外的財務危機警訊。 三、現金流量比率預警模式之預測力表現不遜於應計基礎比率模式；但在應計基礎比率中加入現金流量比率，並未顯著提高模式的預測能力。 / The objective of this study is to assess the usefulness of cash flow disclosures in the prediction of financial distress. This study also determines whether cash flow ratios are good indicator of financial distress and whether adding cash flow ratios in prediction model can improve the predictive ability of the model employing conventional accrual-based ratios. Using a matched pair design, this study examines a sample of 35 distress firms along with 68 non-distress firms. Also, a logistic regression analysis is used to establish the financial distress model with and without cash flow variables respectively, in order to test the hypotheses developed by this study and to derive the conclusion. The findings of this study are as follows. 1. During the period between 3 years to 1 year before financial distress, the accrual-based ratios used in this study aren't all good predictor in financial distress model. 2. The discriminate ability of operating cash flow data is significant. Also, the investing and financing cash flow data provide additional information in the prediction of business distress. 3. Cash flow ratios provide a superior measure for the prediction of financial distress over accrual-based ratios. However, no significant evidence shows that using cash flow ratios in conjunction with accrual-based ratios can improve the overall predictive power of accrual-based ratios alone.

海岸為海洋與陸地交界之處，風、浪與潮流等自然營力長期於此不斷的侵蝕與堆積交互演替。近年來，隨著人口快速增加，人類對於海岸地區土地利用與開發的需求急遽擴張，使得影響海岸地形的變因日益複雜且變化迅速。宜蘭特殊的沙丘性海岸因抗蝕性弱，易受到外力影響而改變地形，海岸後方的沿海平原為人口與產業集中的地區，因此自然營力與人為因素對宜蘭平原海岸地形與環境的影響，備受關注。因衛星影像具多時期與大尺度的空間特性，可提供土地覆蓋變遷分析之有效資訊，故本文使用2003年、2006年與2009年宜蘭平原沿海地區SPOT 5衛星影像，利用階層式分類程序將研究試區分為水體、建成與交通用地、沙地、農地與林地等五種土地覆蓋類別，透過土地覆蓋分類之結果，比較三個時期土地覆蓋型面積的變化；建立馬可夫轉移矩陣，了解各土地覆蓋型轉移的情況；其次，量化地景指標以了解整體土地覆蓋型區塊在空間結構上的情況，並利用Shannon多樣性指標t檢定測驗兩時期間整體地景是否有明顯的變遷。進一步，利用二項式Logit迴歸分析2003至2006年與2006年至2009年間土地覆蓋型的變化與沙丘海岸變遷的關係以及參考前人宜蘭海岸變遷之研究，選擇可能影響此區海岸變遷的自然與人為環境因子，建立二項式Logit迴歸模式，探討各項因子對於沙丘性海岸的影響，並利用海岸沙丘空間分佈預測機率圖，最後以2006年與2009年沙地主題圖作為驗證資料，探討模式的可行性。本研究透過不同的空間計量方法，了解本區土地覆蓋型的變化，期本研究成果對於此區海岸保護與管理政策制定者有一參考的依據。 / Coastal zone is at the junction of ocean and land. The area constantly experiences interchanging succession of erosion and accumulation due to natural forces such as wind, wave, and tidal currents. In recent years, associated with fast population increase, the demand of lands expanded rapidly such that the effects on topography of coastal zone became more complex and changed quickly. Coastal sand dunes are dynamic and fragile terrain often regarded as environmentally sensitive areas. Sand dunes are vulnerable to erosion by natural process and human activity. The objective of this research was to examine the effects of environmental factors and land-use changes on coastal sand dunes in I-Lan County. Satellite imageries are characterized by multi-temporal and large-scale, therefore they are ideal for providing necessary information to facilitate analysis of regional land-cover changes. In this research, three SPOT 5 images acquired in 2003, 2006 and 2009 were used to analyze land-cover changes in I-Lan coastal zone. Firstly, a hierarchical classification procedure was applied to classify the image data to five land-cover types and the land-cover changes were compared. Secondly, based on the classification results, a Markov transitional probability matrix was constructed to understand the transition among different land-cover types, and the Fragstats software was used to quantify the landscape structure of three different periods. By analyzing the spatial distributions of land-cover types in different time periods, we were able to examine to the temporal and spatial changes of land-cover in the I-Lan coastal zone. In addition, a t-test based on Shannon diversity index was used to evaluate the changes of the whole landscape in the study area. Thirdly, by selecting possible natural and man-made factors that are likely to affect coastal environment based on various prior studies, the mathematical models such as Markov chain and binomial logit regression analysis were applied to predict the future overall landscape structure and to simulate the spatial distribution of the sandy coastal zone. Thematic maps derived from satellite images obtained in 2006 and 2009 were used to verify and assess the feasibility of the models. This study integrated several spatial statistical methods to understand the patterns of land-cover changes in the study area. It is expected that the results of this study may offer a valuable reference for the policy-makers of coastal protection and management.

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.