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Bank has always played an important role as capital saving and supply and demand of expenditure since long time ago in the whole financial,and it has become an essential part of our financial system.However,because of the 1997 Asia financial crisis and 2008 financial storm all over the world,the structure of domestic banks has shown bad operation quality situation under the over-competitive environment,also,the profitability and asset quality are decreasing year by year. Along with the similarity of financial products,the segment among financial practices is blurring,it can has the multiple operation effect to meet the diversified demands of customers only by the merge of industry and cross-industry integration to operate in different industry.At this time,to encourage local banks to operate in different industry and encourage financial industry to cooperate via strategic alliance,it can help to enhance competency and improve the dilemma of financial institution. 1991 is the important divide for the financial industry.The ministry of finance gave green light to the erection of sixteen banks,the lack of new practices and finance creativity,plus the effects of financial institution are confined to traditional industry,stock market,the depression of house market,the capital outflow,and the evil competitive of finance industry let the finance market decreasing,lead to the non-performing loan of banks is increasing and return on equity is decreasing.Over the past ten years,bank's interest rate spread decreased from 3% to 1% recently.The local banks,franchising guaranteed industry,1046 branches in 1991 increased to 2411 branches in 2000 and 3155 branches in 2009,if includes foreign banks' branches in Taiwan,medium and small size business banks,credit union of farmer's and fisherman's associate,Chunghwa Post Co.,the branches can up to 5973,on average,one bank can only has 3850 customers out of 23 million people in Taiwan,let the industry become red sea.After the financial storm,governments all over the world emphasize on suppressing market rate, plus the defrost of the relationship between Taiwan and China,the conclusion and sign of MOU,negotiation of ECFA,all let the difference among financial institutions decreasing,and more and more financial institutions operate in different industry,for those local financial institution not belonged to financial holding companies,it's critical for them to figure out how to create their own operating advantage. In terms of this, under the never-end competitive environment, this study got the following conclusion, economic of scale has effects on bank industry, but after the on-site interview on local banks' operating effects, do research on the main factors of banks, not only the external environment effects the profitability of branches, but manager's operation of individual project and the copy of success model, to erect cross-region local bank's optimal operating model. It has benefits on bank's overall effects¡Fthe comparative advantages can create higher value for it under the depression.

本研究主要探討銀行在財務報表上的財務揭露與財務報表外的補充性揭露，如何影響股票市場價值的變化？由於銀行本身主要業務為放款，主要資產來自短中長期放款，這類資產所存在的違約風險以及因利率變動之價值減損風險較其他產業來的高，包含逾放款的產生以及放款期間結構的改變等因素，因此欲透過財務資訊的揭露，探討影響銀行股票市場價值變化的因素。本研究首先分析備抵呆帳、逾放款、放款期間結構與股票市場價值間之關係，其次再加入放款成長、買賣票券損益前純益、淨值成長率三變數，連同原先模式中之備抵呆帳、逾放款、放款期間結構，測試其對於股票市場價值之影響。另外透過虛擬變數的設立，重新測試模式。除以上模式採總額水準做測試外，另採增量水準觀念，測試兩期間備抵未帳、逾放款、放款期間結構、買賣票券損益前純益之變化對於股票市場價值變化之影響。 　　本研究係由探討國內外相關文獻，參考國內環境，加以延伸，蒐集民國七十八年至八十二年的資料，以分年及POOLING方式，另外亦透過FIXED EFFECTS MODEL做迴歸分析，探討財務資訊之資訊內涵。 　　經過實證結果，獲致以下結論： 　　1. 分年測試的結果，由於樣本數太少，結果不理想。 　　2. 備抵呆帳此項揭露在所有模式均具資訊內涵。 　　3. 買賣票券損益前純益亦具資訊內涵。 　　4. 逾放款、放款期間結構變數由於國內資料受限，未顧及時效性的因素及人為武斷定義變數的選取，並未在所有模式中得到與預期一致的結果。

Credit granting is not only one core business but also the major profit source of banks. Non-Performing Loan Ratio (NPL Ratio) is an important index to evaluate the quality of credit granting and to influence profitability of banks. Recently, NPL ratio, which soared to record level, of local banks does not only hurt their asset quality but also threaten their surviving space due to the changes of internal structure, overbanking, internationalization of local banking industry, economic recession, the subprime crisis in 2007. Theoretically, banks have their own credit granting policy and credit examination system; however, there is soaring NPL ratio to cause huge NPL losses in banks because of the differences of credit grating practices. Nevertheless, there are some local banks which have lower NPL ratio. Therefore, this research is to help us to understand the possible factors of overdue loan that happened in corporate banking, and try to discover the key factors. Also, try to sampling those key factors from the past experiences for future crediting reference. The main profit from a bank is the margin of deposit and credit loan interest. Therefore, the overdue loan is highly related to a bank¡¦s profit. Moreover, it is necessary for banks to pay more cost to make up the losses which are caused by the NPL. Certainly, it is safest way for banks to acquire 100% collaterals for creditors¡¦ right, although 100% collaterals could be acquired, the creditors cannot ignore the impacts (on the creditors¡¦ profitability) of the necessary litigation expenses for disposing the collaterals. As a result, fully recognize the customers¡¦ credit condition before drawing is the only way to avoid the NPL loss and ensure the profit.

由於過去數年間金融業者對於授信品質控管的鬆散，產生國內數量及金額龐大的不良債權，資產管理公司也在此時先後成立，不良資產市場亦因而逐漸熱絡，在整體不動產市場占有一席之地。 從過去文獻可得知影響不動產價格之因素數量眾多，但過去較重視實質屬性對於不動產價格之影響，而較忽略非實質因素對於處分價格的影響，其中有關討論時間因素之研究更為稀少；然而由於不良資產同時包含了一般不動產的特徵屬性（如土地、建物面積及區位）與不良資產專有的特徵屬性（如債務人屬性、處分時間），因此進一步研究影響不良資產處分價格之因素便相對重要。 本研究目的主要在於探討影響不良資產處分時間因素對於不良資產處分價格之影響，以供投資人或公司更重視時間因素對於處分不良資產所產生的成本與效益，作為相關單位評估與處分價格之參考。 因此，本文首先探討影響不良資產處分價格之因素種類，並透過特徵價格法建置不良資產特徵價格模型，並進行實證分析，分析探討時間以及相關因素對於不良資產價格的影響關係與程度。 實證結果顯示處分時間、區位、土地面積、建物面積、利率與債務人屬性對於不良資產處分價格有正向的影響力，唯利率因素雖顯著但與假設不符，本研究認為與投資人之行為與心態有關，或因買方之資金成本隨利率提高而增加。 另特別就處分時間因素而言，實證結果顯示當不良資產處分時間越長，價格越高，因此，應合理延長標的物之市場曝光期，以提高資產的處分價格。雖然延長處分時間，相對機會成本亦增加，但投資人或金融機構應在法務流程上更有彈性，以利不良資產的回收。因此藉由本文之研究成果，希冀提供金融機構或資產管理公司處分不良資產時，應謹慎考量處分時間與處分價格間的得失權宜，擬定最適處分策略。 / Due to the loosening credit in the past few years, Taiwan’s non-performing loans, both in terms of volume and amount, burgeoned, resulting in rapid growth of establishments of asset management companies. NPLs consequently became one asset class that was highly sought after in the overall real estate market in Taiwan. From past literatures we understand that there are various factors affecting real estate prices. However, most studies focused on the physical attributes of the underlying real estate, rather than non-physical attributes’ effect on real estate prices. Even more rare are studies that focus on the impact of time. Since non-performing loans encompass both performing real estates’ attributes (land and building area, and location) and non-performing real estates’ attributes (characteristics of debtors and days on market), further study on such factors that affect the real estate prices should be warranted. This paper is to probe the effects of days on market on non-performing loans’ prices. This paper should help investors (including asset management companies) understand the importance of time in regards to its effect on the cost and benefits when disposing NPLs. Therefore, this paper first explores the different factors that affect the prices of NPLs by using Hedonic price method to build models to determine the prices, and then uses real examples to analyze the relationship and magnitude of time and other factors’ impact on NPLs’ prices. Empirical data suggests that days on market, location, land area, building area, interest rate, and borrower types all have positive impact on the prices of NPLs. Interest rate, although showed positive relationship, did not fit the hypothesis, which is probably due to investors’ behavior or mindset or increase in cost of funding as interest rate rises. Specifically for days on market, empirical results manifest that the longer the time on market, the higher the resolution price is. Therefore, it is recommended to reasonably lengthen the days on market to achieve higher resolution price. Although the increase in days on market will increase the opportunity cost, investors and financial institutions should be able to enjoy more flexibility in the legal process, thereby resulting in better recovery. Finally, through the research of this paper, financial institutions and AMCs should carefully consider the cost/benefit between time on market and resolution prices to achieve optimal resolution strategy.

本文以2000年至2009年計22家台灣商業銀行之追蹤資料（panel data），並利用雙因子固定效果模型（two-factor fixed-effects model）探討不同放款之成長對逾期放款比率（逾期放款/放款總額）與轉銷呆帳率（轉銷呆帳/放款總額）之影響效果。 一般而言，金融主管機關以逾期放款比率為代表銀行資產品質之指標，然而銀行或為達成業績目標，或為符合主管機關之監理要求，往往追求短期成長而忽視風險控制，在初期因放款基礎擴大反而造成逾期放款比率下降的資產品質改善假象，之後卻面臨資產品質惡化須大幅轉銷呆帳或出售不良債權而侵蝕銀行獲利及資本，因此除逾期放款比率外，本文另納入轉銷呆帳率為衡量放款品質之指標，以確實瞭解銀行信用擴張對資產品質之影響，提供金融主管機關及銀行業者在未來相關政策上之參考。 研究結果顯示，民營企業放款成長率、消費者放款成長率及資本適足率對逾期放款比率有顯著負向影響，無擔保消費者放款成長率、前期逾期放款比率及淨值比率對轉銷呆帳率有顯著正向影響，總放款成長率、中小企業放款成長率及消費者放款成長率對轉銷呆帳率有顯著負向影響。 / This paper studies 22 banks from Taiwan during the 2000-2009 period. The panel data regression is used to analyze the different loan growth influencing non-performing loan (NPL) and charge-off ratios of Taiwan’s commercial banks. In general,the authority use non-performing loan (NPL) ratio to represent the quality of bank assets.But for performance or in compliance with the authority's request,banks may pursue loan growth in the short term and ignore the risk.In the beginning, NPL ratios decrease and then banks face deterioration in assets quality.Except NPL ratio, this paper adds charge-off ratio to analyze the effect of different loan growth on assets quality. Our major findings are as follows:(1) The effects of private enterprises loan growth rate, consumer loan growth rate and capital adequacy ratio on NPL ratio are significantly negative. (2) The effects of non-residential consumer loan growth rate,NPL ratio of the previous period and equity ratio on charge-off ratio are significantly positive. (3) The effects of total loan growth rate,small and medium enterprises loan growth rate and consumer loan growth rate on charge-off ratio are significantly negative.

This thesis aims to predict the probability of default (PD) of non-performing loan (NPL) customers using transaction and debt data, as a part of developing credit scoring model for Hoist Finance. Many NPL customers face financial exclusion due to default and therefore are considered as bad customers. Hoist Finance is a company that manages NPLs and believes that not all conventionally considered subprime customers are high-risk customers and wants to offer them financial inclusion through favourable loans. In this thesis logistic regression was used to model the PD of NPL customers at Hoist Finance based on 12 months of data. Different feature selection (FS) methods were explored, and the best model utilized l1-regularization for FS and predicted with 85.71% accuracy that 6,277 out of 27,059 customers had a PD between 0% to 10%, which support this belief. Through analysis of the PD it was shown that the PD increased almost linearly with respect to an increase in either debt quantity, original total claim amount or number of missed payments. The analysis also showed that the payment behaviour in the last quarter had the most predictive power. At the same time, from analysing the type II error it was shown that the model was unable to capture some bad payment behaviour, due to putting to large emphasis on the last quarter. / Det här examensarbetet syftar till att predicera sannolikheten för fallissemang för nödlidande lånekunder genom transaktions- och lånedata. Detta som en del av kreditvärdighetsmodellering för Hoist Finance. På engelska kallas sannolikheten för fallissemang för "probability of default" (PD) och nödlidande lån kallas för "non-performing loan" (NPL). Många NPL-kunder står inför ekonomisk uteslutning på grund av att de konventionellt betraktas som kunder med dålig kreditvärdighet. Hoist Finance är ett företag som förvaltar nödlidande lån och påstår att inte alla konventionellt betraktade "dåliga" kunder är högrisk kunder. Därför vill Hoist Finance inkludera dessa kunder ekonomisk genom att erbjuda gynnsamma lån. I detta examensarbetet har Logistisk regression används för att predicera PD på nödlidande lånekunder på Hoist Finance baserat på 12 månaders data. Olika metoder för urval av attribut undersöktes och den bästa modellen utnyttjade lasso för urval. Denna modell predicerade med 85,71 % noggrannhet att 6 277 av 27 059 kunder har en PD mellan 0 % till 10 %, vilket stödjer påståendet. Från analys av PD visade det sig att PD ökade nästan linjärt med avseende på ökning i antingen kvantitet av lån, det ursprungliga totala lånebeloppet eller antalet missade betalningar. Analysen visade också att betalningsbeteendet under det sista kvartalet hade störst prediktivt värde. Genom analys av typ II-felet, visades det sig samtidigt att modellen hade svårigheter att fånga vissa dåliga betalningsbeteende just på grund av att för stor vikt lades på det sista kvartalet.