新巴塞爾資本協定的誕生為全球廣大的金融體系定下一個控制風險的準則，要求金融機構對於其自身風險提列一定的資本準備，以避免發生危機時，毫無應變的能力。金融機構若能依內部評等法計算出對應其自身風險的最低計提資本，即可在風險控制下避免準備過多的資本，以追求最大的利潤。 本研究對象為國內於1996至2005年分類為電子工業下之授信企業，違約企業佔總體資料5.51%，本研究遵循新巴塞爾資本協定中內部評等法之規範，及考量實務應用之普遍性與準確性，以羅吉斯迴歸法建置信用評等模型，並依金管會所建議之方向逐一對模型加以驗證，以符合在實務上運作之標準。 違約機率模型中包含了四個財務報表之變數，一個企業基本特性變數，根據拔靴驗證，不僅在AUC項目的表現相當良好，此模型之參數表現亦相當穩定，不受訓練外樣本而有所明顯的變動。在敏感度分析中，變動某一參數至最差信用等級，皆會讓違約機率有明顯上升。本研究授信資料一共橫跨十年的時間，符合內部評等法中需要五年以上歷史資料的要求，信用評等在轉移矩陣中的表現十分優秀，大致集中在對角線的部分，且幾乎沒有發生在前一年度評等為最差等級的企業在下一年度回復至評等最安全的前三等級的狀況。且透過十年間企業評等的轉移情形發現，電子工業產業在十年間大部分企業有下降的情形，僅有少部分是上升的狀況，顯示出電子工業產業在1996至2005此十年間可能有下滑之趨勢。

新巴賽爾協定已於2006年正式實施，國內各金融業為有效控制信用風險，近年來多致力於內部信用評等之建立；本研究透過建置違約預測模型的方式，讓金融機構可採用更科學且快速的方法預測客戶之違約機率，兼顧了金融機構的獲利與安全性。 本研究之研究對象為全國公開資料庫於民國85年至94年的服務業，其中違約客戶佔1.6%，非違約客戶佔98.4%；藉由企業財務報表與基本構面結合經濟變數，經誤差抽樣建立羅吉斯模型；經評估確立以1：2誤差抽樣比例下的羅吉斯迴歸模型效果最佳。接下來便針對模型去評估模型的有效性；最後，更進一步依照該模型所預測之違約機率，建立信用評分等級，同時檢視各等級內客戶之特性。 研究結果發現，以K-S Test以及ROC曲線進行模型正確性評估，本研究之模型有一定水準可以區隔正常授信戶及違約授信戶的能力；等級同質性檢定，也得到了同一等級內違約要素為同質且組內變異小的結果；表示本模型具有一定的穩定性與預測效力。

全球化潮流方興未艾，基於與國際接軌目標，我國金融業自2006年起實施新巴塞爾資本協定，期於現今日新月異金融環境中以全球一致性的銀行管理方法及制度落實其精神。實施新巴塞爾協定後，首當其衝者便是台灣產業發展主體—中小企業。以信用風險中資本計提為例，中小企業不若大型企業體質健全，且財務透明度亦為人詬病，相對提升金融機構授信風險，進而導致中小企業融資授信審查趨於嚴格與保守，中小企業融資難度與成本皆大幅增加。 有鑑於此，本研究以中小企業中電子業為主要研究對象，採資料採礦流程進行信用評等模型建置。為求配適最佳違約機率模型，分別以不同精細抽樣比例逐一配適羅吉斯迴歸、類神經網路及分類迴歸樹等統計模型，經評估後篩選出羅吉斯迴歸模型建置信用評等系統。再者，為確認模型與信用評等系統建置適當，係遵循新巴塞爾協定相關規範進行各項測試及驗證，結果顯示模型於樣本外資料測試表現良好，信用評等系統亦通過正確性分析、等級區隔同質性檢定及穩定度分析等驗證準則，冀能提供金融機構一套有效且精簡的信用管理機制，建立與中小企業間資訊對稱管道，於兩造雙方取得互利平衡，防範危機於未然。 / Globalization trend is still growing. Because of the objective of connecting to the world, the banking and finance industry in Taiwan has implemented the New Basel Capital Accord since 2006, hoping to make use of globally consistent banking management method and system to implement its spirit in this changing financial environment today. After the implementation of the New Basel Capital Accord, the principal development part in Taiwan industry, medium- and small-sized enterprises, is the first to be affected. For example, with regard to the capital requirements in credit risks, the constitution of medium- and small-sized enterprises is not as sound as large-sized enterprises’, and the financial transparency of medium- and small-sized enterprises is insufficient that the credit risk of financial institution would be lifted comparatively; and then, the finance and credit investigation of medium- and small-sized enterprises would become strict and conservative, thus the finance difficulty and cost of medium- and small-sized enterprises would be increased substantially. In view of this, this study regards the electronics industry from medium- and small-sized enterprises as the main study objects, and data mining procedures are used so as to establish the credit scoring system. To get the best probability model of default, different oversampling ratios are used one by one to match such statistical models and logistic regression, Neural Network Analysis, and C&R Tree; and logistic regression model is selected for the establishment of credit scoring system after assessment. Moreover, relevant the New Basel Capital Accord standards are followed to carry out every test and verification so as to confirm that the establishments of model and credit scoring system are appropriate. The result indicates that the model has good performance in out-sample test, while credit scoring system also passes such verification standards as accuracy analysis, level segment homogeneity test, and stability analysis. Hopefully, this study result can provide a set of effective and simple credit management system for the financial institution to establish information symmetrical channel with the medium- and small-sized enterprises, so that both parties can obtain mutual balance and the crisis can be alerted in advance.

經濟體的產出缺口一直是政策執行者的觀察重點，當一國出現產出缺口時，代表資源配置並不均衡，將發生通貨膨脹或是失業的現象，如能提早預期到未來是否會出現產出缺口，將可讓政策執行者即早進行政策實施，且有文獻指出，殖利率曲線資料中具有隱含未來經濟狀況之資訊。 本研究以美國財政部與聯準會之公開資料，將以殖利率曲線之斜率進行預測產出缺口；本文研究美國1977年至2016年之國民生產毛額成分與殖利率之資料，目標為建立對於未來一季將出現正向或負向缺口現象之模型，本研究建立三種預測模型進行比較，分別為線性迴歸模型、羅吉斯迴歸模型與機器學習中的支持向量機，以實質GDP的缺口預測而言，研究結果顯示，三者預測準確度均達到65%以上，支持向量機的準確度更達到80.85%。 得出以下結論，第一，殖利率曲線對於未來總體經濟產出缺口具有一定之解釋力；第二，對於高維度之預測模型在機器學習中的支持向量機表現會較一般常用之迴歸模型佳；第三，進出口的預測力在三個模型下均表現較差，可能為殖利率曲線對於進出口並不具有完整有效的資訊，可能有其餘的經濟指標或金融市場資訊可以解釋；第四，對於實質消費與投資等民間部門經濟行為有超過80%的預測力。 / The output gap of the economy has always been the objectives of policy practitioners. When a country appear the output gap, it means that the allocation of resources is not equilibrium and the inflation or unemployment will occur. The output gap will allow policymakers to implement the policy as early as possible, and the literature notes that the information of the yield curve has information about the future economic situation. In this paper, we using the data from the U.S. Department of Treasury and the Federal Reserve to predict the output gap by the slopes of the yield curve. Our goal is to construct the prediction model for the next quarter. To forecast the real GDP gap, three prediction models were compared, linear regression model, logistic regression model and support vector machine. The results show that the accuracy of the three predictions are more than 65%, support vector machine accuracy to reach 80.85%. We can have conclusions showing below: First, the yield curve has significant explanatory power for the overall economic output gap in the future. Second, the support vector machine perform better than the commonly used regression model. Third, the predictive power of real import and export in the three models are poor performance, there may be the rest of the economic indicators or financial market information can be explained. Fourth, the real consumption and investment has the predictive power more than 80% of the forecast.

資訊不對稱可能造成市場中買賣雙方無法順利進行交易的阻力之一，而評等制度可以為資本供需雙方架起資訊的橋樑，讓資本市場發揮資金仲介的功能，使得投資者可以降低風險的不確定性、信用風險較低的企業減少付出的籌資成本，進而擴大市場的深度與廣度，增進資本市場的整體效率。為提供金融市場透明化訊息，並有別於過去相關研究方法，本研究多變量統計方法之因素分析(Factor Analysis)及順序羅吉斯迴歸(Ordered Logistic Regression)為基礎，分析影響壽險業信用評等之重要因素，以期在分析過程中獲得一套有效信用評等過程的關鍵方向。 　　本文以1994年至1997年接受A.M. Best評等之591家美國壽險公司為研究對象。美國壽險公司年報資料來源為NAIC DAATABASE PRODUCT光碟資料庫;而等級資料來源則是A.M. Best資料庫。首先取1994年至1996年共三年之財務資料與評等結果為估計樣本建立順序羅吉斯迴歸模型，並以相同公司的1997年的資料為保留樣本，作為等級預測之用，同時以多變量之因素分析建立1997年之分析預測模型，來比較兩模型之差異及解釋影響模型之重要因素，進而驗證壽險公司之財務報表資料與評等結果間是否有直接關係存在。

近年來，隨著全球經濟不景氣及金融海嘯的影響，國內失業人口亦逐漸攀高，面臨經濟低迷的衝擊，創業實為促進經濟成長與增加就業機會的途徑之一。創業活動的蓬勃發展除能替個人創造財富，亦能降低失業率與通貨膨脹，為國家帶來經濟效益，可見創業活動與國家經濟發展關係密切。因此了解臺灣民眾的創業行為狀況及其影響因素已成為值得關注的課題。 過去曾有學者從內在因素與外在因素來探討創業行為之影響因素，其中內在因素包含人格特質、個人價值觀、創業態度、創業認知等，外在因素包含政府政策、創業教育、社會資本、組織資源等。本研究之主要目的為藉由實證研究，探討臺灣成年民眾在創業態度、創業觀感上的認知與創業行為間之關聯性，並試圖找出影響創業行為（包含創業傾向、創業活動）的關鍵因素。 研究結果顯示，有創業傾向的比例約占29%左右，參與投入創業活動的比例約占12%左右。影響創業傾向與創業活動之因素並不完全相同，創業傾向除了受到創業態度的影響之外，還與創業觀感有所關聯，而是否參與創業活動則只與創業態度有關，其中以認為自己擁有足夠創業能力的人，以及認識的人當中有創業經驗的人，其有參與創業活動及未來有創業傾向的可能性較高。

2007年，台灣將實施新版巴塞爾資本協定，在舊版協定的最低資本要求之外，更擴展成了三大支柱(Pillar)：最低資本適足、監理審查程序以及市場紀律(公開揭露)之標準，並且引入了風險評等的觀念來計算法定資本，即信用評等方法，使得銀行能夠自行採用銀行內部自有系統評估風險暴險，並據以計提所需資本，以彌補傳統標準法的不足。 新版巴塞爾資本協定引入了內部評等法(internal rating based approach，簡稱IRB)，其又區分為基礎內部評等法(Foundation IRB Approach)與進階內部評等法(Adavanced IRB Approach)，此改革欲提升金融機構的信用風險測量能力，並建立自有之信用評等系統，使銀行能夠自行評估信用風險，資本協定並詳述了內部評等法的內涵與規範，例如風險成份、暴險類型、最低要求等等，使銀行在採用內部評等法時能有所依據。 本研究之目的在以台灣之銀行實例，針對內部評等法(IRB)中的企業型暴險，根據新版巴塞爾資本協定與金管會的準則，建立信用評等模型，來推估風險成份中的違約機率(PD)。本研究利用羅吉斯迴歸建構評分模型，顯著之解釋變數包括產業變數、六項財務比率變數、以及二項授信交易資訊，其中財務變數包括一項獲力能力指標、二項經營效率指標、三項償債能力指標。本模型亦完成了新資本協定所規範之七項評等模型測試中的五項，顯示評等模型在正確性、穩定度、區隔能力等各方面均有良好的表現。這些測試包括：預測力測試、標竿化比較、評等同質性測試、評等穩定性測試、壓力測試。

隨著資訊科技的發展與網際網路的普及，音樂與電影光碟盜版的問題也逐年嚴重，然影音盜版不僅影響權利人團體，影音業者及創作者之生存，亦攸關我國智慧財產之發展，更常成為我國在國際貿易諮商上的重要課題。在種種緣由與現況下，得使國內許多產、官、學、研團體想去研究影音盜版的相關議題，以了解其嚴重程度如何，或有無較客觀合理的指標或評估方式?並進一步研擬有效方法來防範盜版問題進一步惡化，為此上述問題乃是本研究之源起。 目前所有的影音盜版研究，多著重在計算盜版率,探討盜版因素,盜版行為的心理與法制問題，皆還尚未針對影音盜版率，建構出可供學者推論的盜版率機率分配，及其他相關的數量研究，因此，本研究的主要實証方向，乃根據2004年經濟部智慧財產局(intellectual property office ministry of economic affairs,R.O.C)委託政治大學之消費者調查資料，就音樂CD、影音VCD/DVD兩部分，針對筆者有興趣之變項(性別、年齡、有無上網下載等)，(1)分別建構各自的混合分配並了解其分配間的差異與趨勢， (2)探討消費者對盜版行為的態度，(3)了解消費者對喜好的光碟所願付價格之差異，(4)建立盜版率分配的信賴帶，以及(5)針對現有的調查資料進行盜版辨別。 最後，就查緝盜版與維護智慧財產權兩方面，實證分析提供政府相關單位作為參考的依據，以求擬訂周詳且完善的措施來防範日益惡化的盜版問題。 / With the development of computer technology and widespread of internet, the piracy problem goes more serious. The piracy situation makes much influence not only on the rights of international oblige societies but also the growing of the intellectual properties in Taiwan. Moreover, it becomes the rock on the road of international commercial negotiations. Beyond the serious situation in the mean time, more researchers and relevant organizations on the island are trying to pay more attention to this important issue. This research intends to understand several questions: How is the actual situation on the piracy problem? Are there any objective evaluation ways? Are there any effective policies to prevent it from going deeper? These questions lead to this research. In the meantime, most of Audio & Video piracy research emphasized only on calculating the piracy rate, or the reasons, or the relevant psychological and law problems, but few on piracy quantitative studies. Therefore the mainly intention of this research is based on the data from the IPO(Intellectual Property Office Ministry of Economic Affairs, ROC), which is executing by National Chengchi University. As for the two parts concerning music CD and visual VCD/DVD, and the variables those I am highly interested including gender, age, education level, downloading or not. The empirical study results show as below: (1)The piracy rate distribution corresponds with the Mixed Model, that mean that it have been proportionally mixed two degenerate distribution (while X=0 and 100) with the Normal distribution. (2) On the facets of distribution differences and trends analysis, not only music CD and visual VCD/DVD, the results of the research by Mann-Whitney test and Kolmogorov-Smirnov two sample test both reveal the rising tendency of overall piracy rate. The generation of 20~29 years old is the mainly pirate group, moreover, higher education grades group does the more pirating behaviors, and lower income group intends more unauthorized copying conducts. Furthermore, along with the development of internet technology, the infringement behavior is more serious on the network connectors than the non-network downloaders. (3) Under surveying the opinions of consumers about the piracy, regardless of whether music or movies, the deviation is more serious on male than female, under 30-year-old than above, low educated than high, low income than high, pirate than non-pirate, downloaders than non-downloaders. The problem locates not only the lack of the concepts and recognition on the intellectual properties rights, but also the scarce of moral or legal limitations on the unauthorized rebuilding or downloading. But in the other curious facet, although the higher grade educated groups got more equitable standpoints on the piracy discussion, but evidenced depend upon the collected data they are also mainly the group who did the piracy behaviors more. (4) On the price range that a consumer would like to pay for, most of the pirate consumer tends to pay low price to buy the A/V goods, most of the non-pirating consumer group tends to pay general price to buy ones, and no significant difference of these two groups with high price, (5) On the facets of confidence bands on the whole music CD and visual VCD/DVD pirating rate, because of the specialties of pirating data- the higher frequency while the piracy rate values 0 and 100, so that the upper and lower bound reveals at 0 and 100. Futhermore, the confidence bands obtains from the population distribution function, therefore it’s suitable for the goodness-of-fit test. The results met the Kolmogorov-Smirnov one sample test. (6) On the data recognition facets, the logistic regression model of piracy is constructed in this research. Classification from the fitted logistic regression models, the results reveals 107 non-pirate are mis-judged to pirating behaviors, 186 pirating samples are neglected to non-pirate ones, the correct recognition rate goes high of 88 %. Key Words：Piracy Rate, Mixture Models, Mann-Whitney, Kolmogorov-Smirnov, Logistic Regression Model, Nonparametric Statistics.

選舉事件在台灣一直是屬於重大事件，民眾對於選舉事務都非常關心。根據童振源等人(2009)和Forsythe, Rietz and Ross(1999)，透過預測市場的機制來預測選舉結果非常準確，所以近年來預測市場也變得越來越熱門，新聞媒體也開始大幅報導未來事件交易所的預測結果。本論文將透過實證的方式探討除了價格之外，加入與未平倉相關的資訊是否能夠幫助我們更有效地預測選舉結果，並以此建議未來事件交易所揭露網站內的期貨未平倉合約量這項資訊，以提供交易人參考。 本論文使用資料為未來事件交易所中關於2008年總統選舉、立委選舉以及2010年五都市長選舉的資料，資料期間皆為各選舉的最後兩個交易日的資料，採用的變數有最後交易日的合約加權平均價格、最後交易日的合約未平倉量、最後交易日的合約成交量和最後交易日之價格漲跌及未平倉量增減的相乘項，並利用羅吉斯迴歸來幫助預測分析。 研究結果顯示：(一)與過去的研究結果相符合，未來事件交易所中選舉期貨合約的價格能有效幫助預測選舉的結果；(二)未平倉量和成交量均無法幫助預測選舉結果，這可能是由於未來事件交易所使用虛擬貨幣進行交易以及保證金制度，使得部分交易人在交易成交後不再關注自己的部位或是損益所導致；(三)利用最後交易日價格漲跌及未平倉量增減所得到的相乘項對於預測選情有顯著的幫助，當相乘項越大的時候，候選人當選的機率也隨之越小。 一般期貨交易中，透過未平倉合約量這項資訊可幫助交易人得到更多的資訊。而未來事件交易所的選舉期貨合約之未平倉量雖然無法提供額外的資訊，但是仍然建議可以將這項資訊揭露以供交易人參考。此外，相乘項在預測選情時能提供顯著的幫助，因此在交易上或是預測上都可以參考這項資訊以做出更佳的判斷。

信用風險或信用違約意旨金融機構提供給客戶服務卻未得償還的機率，故其在銀行信貸決策的領域是常被鑽研的對象，因為其對於金融機構所扮演的角色尤其重要，對商業銀行來說更是常難以解釋或控制，然而拜現今進步的科技所賜，金融機構可以藉由操控較過去低的成本即可進一步發展強健且精煉的系統與模型去做預測還有信用風險的控管，有鑑於對客戶的評分自大數據時代來臨起，即使是學生亦開始有了可以評鑑的痕跡，憑藉前人所實驗或仰賴的基本考量面向如客戶基本資料、財力狀況或是其於該公司今昔的借貸訊息，再輔以藉由開放資料所帶來的資訊，發想可能影響信用違約率的變數如外在規範對該客戶的紀錄，想驗證是否真有尚可開發的方向，若有則其影響可以到多深。 眾所皆知從過去到現在即有很多種方法被開創以及提出以預測信用違約率，當然所使用的方法和金融機構本身的複雜性、規模大小以及信貸類型有關，最常見的有判別分析，但其對於變數有嚴格的假設，而新興的方法神經網路可以克服判別分析的缺陷且預測的效能也不錯，但神經網路只給予預測結果而運算過程是未知的，對於想要了解變數間的關係無濟於事，故還是選擇從可以對二元分類做預測亦可以藉由模型係數看到應變數和自變數間關係的羅吉斯迴歸方法著手，而研究過程即是依著前人對於羅吉斯迴歸在信用風險上的繩索摸索，將資料如何清理、變數如何轉換、模型如何建立以及最後如何篩選做一個完整的陳述，縱然長道漫漫，對於研究假設在結果終得驗證也始見曙光，考慮的新面向確有其影響力，而在模型係數上也看到其影響的大小，為了更彰顯羅吉斯迴歸對於變數間提供的訊息，故在最後將研究結果以較文字易讀的視覺化方式作呈現。 / Credit risk or credit default means the probability of non-repayment that banks or financial institutions get after they provide services to their customers. Credit risk is also studied intensively in the field of bank lending strategy because it’s usually hard to interpret and control. However, thanks to advanced technology nowadays, banks can manipulate reduced cost to develop robust and well-trained system and models so as to predict and mange credit risk. In the light of the score on customers from the beginning of big data era, every single one can be tracked to assess even though he or she is student. Relying on common facets like personal information, financial statement and past relationship of loan in a specific bank, come up with possible variables like regulations which influence credit risk according to information from open data. Try to verify if there is a new aspect of modeling and how far it effects. As everyone knows, there are several created and offered methodologies in order to predict credit default. They differ from complexity of banks and institutions, size and type of loan. One of the most popular method is discriminant analysis, but variables are restricted to its assumption. Neural network can fix the flaws of the assumption and work efficiently. Considering the unknown process of calculation in neural network, choose logistic regression as research method which can see the relationship between variables and predict the binary category. With the posterior research on credit risk, make a complete statement about how to clean data, how to transform variables and how to build or screen models. Although the procedure is complicated, the result of this study still validates original hypothesis that new aspect indeed has an impact on credit risk and the coefficient shows how deep it affects.