市場上存在許多傳統資產模型無法解釋的異常現象，本論文將探討台灣證券 市場異常報酬投資策略之獲利與財務危機間的關聯性，重點放在價值型投資策略， 由買進高淨值市價比的公司股票，放空低淨值市價比的股票，建構出價值型投資 組合。此投資策略的主要獲利來源出自投資組合的多頭部位，即高淨值市價比的 公司。且信用風險作為財務危機的代理變數，在解釋異常報酬上扮演重要角色， 價值型投資策略的異常報酬在高信用風險公司是較大的，表示財務危機的影響是 有反映在股票報酬上的。而與美國市場的結果不同，信用評等降評對報酬的影響 在台灣證券市場並不顯著。 / Anomalies exist in the markets that cannot be explained by traditional asset-pricing models. This paper assesses implications of financial distress for the profitability of anomaly-based trading strategies in Taiwan stock market. We focus on the value-based strategy which conditions on the BM ratio. It involves buying highest BM and selling lowest BM stocks. Financial distress, as proxied by rating downgrades, is likely to be a primary ex ante indicator of a company’s future performance. Anomaly returns of value-based strategy are bigger in high credit risk companies. But unlike the evidence in U.S market, rating downgrades only have limited impact on stock returns in Taiwan.

關於Merton (1969) 最適投資組合策略問題，所考慮之投資情境為：一個將其財富資金安排配置於風險性資產(各類證券)與無風險短期現金部位之投資人，在給定此投資人心目中財富效用函數之前提下，希望事先決定出投資組合之最適投資權重(策略)，藉此達成在投資期滿時極大化財富效用之期望值。基於Merton (1974) 公司價值觀點，具有違約風險之證券(公司債與股票)乃是公司價值之衍生性商品，無法以傳統資產配置對股票與債券部位採取現貨方式處理最適投資策略，在此必需同時結合財務工程處理衍生性金融商品計價與避險之技術來解決。本研究利用Kron & Kraft (2003) 彈性求解法來針對市場是否有投資限制、債券提前違約、到期違約及利率隨機與否等假設，基於不同投資組合情境分析來最適投資部位策略。本研貢獻和究創新突破之處在於特別探討公司違約時，債券投資人不再享有全部公司殘值之求償權，此時股東亦享有部份比例之求償權，違約後之公司殘值將由債券投資人與股東兩者比例共分之特殊情境下，對數型態財富效用之投資人對於提前違約風險之接受度高於到期違約風險，若一般情境(股東無任何求償權)則為相反。此外亦特別提供最適成長投資組合之動態避險策略封閉解，藉以提供投資人面臨企業違約風險時應制定之投資決策與動態調整，使本研究臻至週延與實用。 / Under the Merton (1969) optimal portfolio problem, we only consider the specific investor, whose wealth utility follows the type of logarithm function; wants to maximize the expected value of the terminal wealth utility through determine the optimal investment strategy in advance. He divides his wealth into the riskless asset and risky assets such as the money market account and the various-risky securities issued by the corporate. Based on the Merton firm value framework (1974), the defaultable securities, such as the corporate bonds and stocks, are the derivatives instruments of the firm value. It will be inappropriate if we deal with this optimal portfolio problem under the original methods. Therefore, we need to handle this optimal asset allocation problem through the pricing, valuation and hedging techniques from the financial engineering simultaneously. This study apply the elasticity approach to portfolio optimization (EAPO, Kraft ,2003) to solve the optimal portfolio strategy under various scenarios, such as the market contains the investment constrain or not, intermediate default risks, mature default risk, interest rate risky under the stochastic process. The innovation and contribution of this paper are especially breaking the common setting and analysis the optimal-growth-portfolio strategy under the special scenario. In the common setting, as soon as the default event occurs, the residual firm value will be claimed by the corporate bondholders with fully proportion and the stockholder cannot share any residual value. Oppositely, the stockholder will be able to share the residual firm value proportionally with the corporate bondholder together under the so-called special scenario. We found that the investor would have higher acceptance of the premature default risk than the mature default risk in the special scenario. This phenomenon will be reversed under the common scenario. Furthermore, in order to make this study more completely and useful, we do not only illustrate the optimal investment strategy but also provide the closed-formed solution of the dynamic hedge strategy of the risky position, composed by the defaultable securities. This could help the optimal-growth-portfolio-oriented investor to make investment decision while they face the firm value downward decreasing.

我國的信用卡市場在民國八十二年全面開放以來，發展至今不過10餘年，已成為全球成長最快速的信用卡市場之一。但近年來也隨著信用卡業務已有相當顯著的成長，然而信用卡不僅只是一種支付工具，也屬於免擔保的信用融資，對發卡銀行而言，風險很高。故本文對於銀行要如何快速且正確的掌握客戶信用與還款能力，以防範呆帳發生，也變得日趨重要。 故本文利用Binary Quantile Regression可用於探討解釋變數對於被解釋變數在給定「特定分位數之下的邊際效果」，提供不同分位數的估計結果，可用於觀察被解釋變數的整個分配狀況。在實證上，二元分量迴歸模型不只可用來解釋平均的狀況，更常用來觀察分配尾端的情況。在以ROC與CAP的信用風險模型來驗證其Binary Quantile Regression的效力。

雙層擔保債權憑證（CDO-squared）是目前全球資產證券化商品市場相當熱門之商品，回顧國內對信用風險之研究，極少有相關文獻或研究被提出。本研究乃以合成型雙層擔保債權憑證（synthetic CDO-squared）為主體，試圖以一套毋須進行蒙地卡羅模擬之半解析式評價模型為基礎，目的旨在探討雙層擔保債權憑證具有高投資收益的背後，所隱含之風險程度為何？廣泛探索各種不同分券（tranches）之風險特徵，透過比較分析使各個分券間之相互關係能環環相扣，進而對此商品之風險/報酬特性有全面性之瞭解並規劃合適避險策略。本研究在違約事件為條件式獨立的假設下，運用遞迴法則（recursive algorithm）及一個多維超立方體結構（hyper-cube）建構出雙層擔保債權憑證之損失分配，並以求得之評價模型為風險分析之基礎，得到下列發現與避險涵義：（1）雙層擔保債權憑證雖然標榜具有雙重的信用違約保護且能達到更大程度的投資組合分散，同時兼顧利潤與風險的平衡，但實際上卻是高槓桿程度的商品。（2）名目本金數額及分券信用評等之揭露無法反映分券風險本質，市場參與者需要仔細區分風險金額移轉數目與內含風險移轉程度之差異。（3）應用delta避險策略可以規避分券所面臨之市場風險，而使避險組合價值不受標的資產市場價差波動之影響，繼而經由避險成本之求算，可適當選用數個單一信用違約交換（single name CDS）或信用違約交換指數來進行有效之避險。

為因應新巴賽爾資本協定(BaselⅡ)於2007年開始實施，金管會與各大銀行莫不致力於相關作業流程與法規的修訂。根據巴賽爾監理委員會統計，銀行面對的風險以信用風險占60%的比重最高，可見信用風險對銀行風險控管的重要性。 依BaselⅡ信用風險內部評等法的規範，銀行對每個授信戶均需估計一個一年期的違約機率(PD)。針對銀行對企業的授信放款，本研究採用計量模型Logistic Regression model建置企業信用風險預警模型，並與Altman(1983)的Z-score model比較其模型預測能力，以期降低專家意見法最為人所詬病的缺乏一致性與客觀性的問題。 / 研究結果顯示，以財務變數加上公司治理變數並以規模為控制變數的logistic regression model比Altman(1983)提出的Z-score model更能有效降低型一誤差和型二誤差並提高整體的歸類正確率；在危機發生前一年、前兩年，財務變數仍具有鑑別力；但在財務危機發生前三年所有的財務變數已不具鑑別力，反而是公司治理變數較具鑑別力，故可知財務數據為落後指標，公司治理變數為鑑別企業發生財務危機事件的領先指標。實證結果證明，若企業長期公司治理不佳將不利於公司經營獲利。

信用連結債券的價值主要取決於所連結資產池內的資產違約狀況，使得原始信用風險債券在到期時的本金償付受到其他債券的信用風險影響，因此如何準確且客觀的估計資產池內違約機率便一個很重要的課題，而過去文獻常以給定參數的方式，並且假設資產間的違約狀況彼此獨立下進行評價，對於聯合違約機率的捕捉並不明顯，因此本文延伸Factor Copula模型，建立信用連結債券之評價模型，該模型考慮了資產間的違約相關程度，以期達到符合市場的效果，同時配合統計之因素分析法，試圖找出影響商品價格背後的市場因子。 本研究利用延伸的評價模型以及Copula法，對實際商品做一訂價探討，結果發現，不管是使用樣本內或樣本外的資料去評價時，本研究的評價模型表現都優於Copula法，表示說評價時額外加入市場因子的考慮，對於評價是有正向的幫助；而在因子選取方面，我們選取18項因子後，經由因素分析共可萃取出三大類因素，藉由觀察期望價格與市場報價的均方根誤差，發現國家因素以及產業因素均對於商品價格有所影響，而全球因素對於商品不但沒有顯著影響，同時加入後還會使得計算出的商品期望價格更偏離市場報價，代表說並不是盲目的加入許多因子就能使得模型計算出的價格貼近市場報價，則是要視加入的因子對於資產的影響程度而定。 對於後續研究的建議：由於本研究的實證中存在一些假設，使得評價過程中並不完全符合現實市場現況，若能得到市場上的真實數據，或是改以隨機的方式來計算，相信結果會更貼近市場報價；同時，藉由選取不同的因子來評價，希望能找出國家因素、產業因素以外的其他影響因子，可助於我們更了解此項商品背後的影響因素，使得投資人能藉由觀察市場因子數據來判斷商品未來價格走勢。 / Value of the credit-linked notes depend on the pool of assets whether default or not, so the promised payoff of credit-linked notes is affected by other risky underlying assets. Therefore, how to estimate the probability of default asset pool accurately and objectively will be a very important issue. In the past literature, researchers usually use given parameters, and assume assets probability of default are independent from each other under valuation. Furthermore, it is not obvious to capture the joint probability of default. Thus, this article extends the Factor Copula Model to provide a new methodology of pricing credit-linked notes, which consider the default correlation between the extent of assets in order to achieve result in line with market and with Factor Analysis method added, trying to figure out the impact of commodity price factor behind the market. In the empirical analysis, pricing the actual commodity issued by LB Baden-Wuerttemberg using extend model and Copula model, we found that no matter choose in-the-sample or out-the-sample data to valuation, the models in this article are superior to Copula model by compare the root-mean-square deviation(RMSE). It means add the market factors into our valuation is beneficial. In terms of selection factors, we select eighteen factors prepared by Morgan Stanley Capital International, and three categories of factors may be extracted from Factor Analysis method. By observing RMSE, both national factors and industry factors will influence on the commodity, but world factors not only did not significantly impact on the commodity, but also add it to calculate the expected price further from the market price. Representative said not blind join the many factors can make the model to calculate the price close to the market price, it is a factor depending on the degree of influence of the added asset. For the suggestion of future research. The fact that the presence of empirical assumptions in this study, result in the evaluation process is not entirely realistic to market situation. We suggest to get the real data on the market or use random way to calculate, we believe that the outcome will be closer to the market price. Meanwhile, by selecting different factors to evaluate, trying to discover further factors which significantly impact on the commodity; it will help us better to understand the factors behind the commodity, so investors can predict commodity future prices by observing the market data.

本文針對金融海嘯時期,信用風險大幅擴張的階段,藉由大股東持股比例、 銀行持股比例、政府持股比例分析其對於各家大型金融機構的信用風險之影響, 本文主要採用 Bloomberg 資料庫以及 Bankscope 資料庫,期間涵蓋 2003 年至 2013 年全球資本排名前 60 的大型金融機構的季資料,並透過縱橫資料的技術來分析 股權結構與信用風險之間的相關性。 結論顯示在危機發生的期間時,政府對於大型金融機構的信用風險存在顯著 的抑制效果,本文接著依照過去文獻對投資人的股權保護程度加以分類後,發現 與過去分析總風險和股權結構之間的關係之結果並不相同,以往的結果顯示,保 護程度比較差的國家中,政府對於公司的風險以及公司決策會存在比較大的影響 力,並會進而降低公司的風險,然而本文得到的結果是投資人股權保護程度比較 強的區域裡面,政府對於降低大型金融機構的信用風險會有著比較顯著的影響 性。 / In this thesis, we investigate the level of credit risk taking concerning the ownership structure in large complex financial institutions during the sub-prime crisis period. We use quarterly data of the top 60 large financial institutions based on Bloomberg and Bankscope, covering the interval from 2003 to 2013. From our results, we show that there is negative relationship between the level of risk and government ownership on banks. Furthermore, if we categorize the banks regarding its investor protection, our findings support that government ownership on banks has a significant effect in countries where it provides stronger protection to investors during financial crisis. Different from previous works, which states that in country with less investor protection, government ownership on banks has more influence power, here we arrive at an opposite result. We conclude that increasing government ownership can significantly reduce the level of credit risk on banks.

本研究旨在探討企業盈餘管理行為對其信用評等之影響，以Ordered Probit Model，探討台灣上市櫃(包含興櫃)企業之盈餘管理行為，對台灣經濟新報社(TEJ)的台灣企業信用風險指標(Taiwan Corporate Credit Risk Index, 簡稱TCRI)是否造成影響，並進一步觀察其影響為何。 本研究發現，企業之盈餘管理行會影響其信用評等，而且透過進一步之邊際影響分析，顯示企業盈餘管理對當期盈餘之影響，與信用評等之間存在著反向的關係，意味著，信用評等人員在使用財務報表，進行信用評等程序時，統計上會因為企業之盈餘管理行為而有處理上之差異；本研究另外發現就平均而言，盈餘增加(減少)之裁量性應計數影響TCRI下降機率的增加(減少)幅度，大於TCRI不變以及TCRI上升之機率變動幅度。 / This study investigates the influence of earnings management on credit rating. Using Ordered Probit Model, this study tests whether earnings management of listed companies (emerging stock included) effects Taiwan Corporate Credit Risk Index (TCRI). This study finds that earnings management does affect credit rating. In addition, it suggests that earnings with income-increasing discretionary accruals have more opportunity for getting downgradings than earnings with income-decreasing discretionary accruals through analysis of marginal effects. That is, the credit analysts treat earnings management in a different way when rating companies. Moreover, this study also finds that the marginal effects of income-increasing discretionary accruals of increasing opportunity for getting downgradings are larger than the marginal effects of opportunity for getting unchanged and upgradings. Similarly, the marginal effects of income-decreasing discretionary accruals of decreasing opportunity for getting downgradings are larger than the marginal effects of opportunity for getting unchanged and upgradings.

信用風險為金融業經營上最大的風險來源，也是金融業損失的最主要的原因，近日企業紛紛不約而同的強調風險控管的重要性，風險控管更被視為下一波的競爭力，信用風險更是佔銀行各項風險之首。 本文將著重信用風險損失機率分配之探討，然後針對兩種近似方法，同質性近似與拉普拉斯近似模式以及各種不同的投資組合，研究其與蒙地卡羅之配適情形，並嘗試利用比常態厚尾的t分配，目的是為了找出更加保守的估計方式。 分析結果顯示，每一種近似法都沒有絕對的好或壞，各有其相對帶來的效益，同質性近似法不需花費很長的時間，且其結果大致與蒙地卡羅模擬相符，相對來說，拉普拉斯近似法所需的時間較長，但是其對於估計很小之違約機率的準確性是非常有幫助的。整體而言，此二種估計法皆可提供風險管理者作為估計違約機率的參考。

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.