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In almost all nations, credit guarantee is an important system that the government relies on to help small and medium enterprises (SMEs) obtain finance and provide guidance to them. In Taiwan, Small and Medium Enterprise Credit Guarantee Fund (SMEG) is an institution mandated by the government to assist SMEs to obtain necessary funds from financial institutions. Although SMEG is a non-profit organization, its financial status still affects its sustainability. Therefore, this paper modifies the model presented by Merrick (2001) and uses data of loans submitted by a domestic bank to SMEG for credit guarantee to estimate probability of default and recovery rate of credit guaranteed loans. As this model quantifies risk of credit guarantee, it can help SMEG calculate the necessary reserve for prepayment in subrogation. In this increasingly complicated financial environment, quality of risk control determines the prosperity or survival of an organization. The proposed model is a feasible risk evaluation model that credit guarantee institutions can utilize to effectively improve their quality of risk control.

Several reports research the best prediction power of the credit risk models for different industries. The structural models use firm¡¦s information for firms¡¦ structural variables, such as asset value and asset volatility, to determine the time of default, but it suffer from some drawbacks, which represent the main reasons behind their relatively poor empirical performance. It require estimates for the parameters of the firm¡¦s asset value, which is nonobservable. Moody's KMV model is well known and useful among them, but it ignores recovery rate and difference in financial structure and industry. The reduced-form models fundamentally differ from typical structural models in the degree of predictability of the default. Reduced-form models use market data and assume the probability of default is exogenously generated. However, the basel committee for banking supervision proposed that risk is endogenous. The purpose of this paper is using quantile and threshold regression to introduce a new approach which is based on the Moody¡¦s KMV model, the Lu and Kuo ( 2005) and the Altman, Brooks Brady, Resti and Sironi (2005) to the evaluation of the endogenous probability of default and the endogenous recovery rate.

As a consequence from the last financial crisis that began 2007 in USA, regulatory frameworks are continuously improved in order to limit the banks’ risk exposure. Two of the amendments are Basel III and IFRS 9. Basel III regulates the capital a bank is required to hold while IFRS 9 is an accounting standard for how banks and insurance companies should classify their assets and estimate their future credit losses. Mutually for both Basel III and IFRS 9 is the estimation of future credit losses which include probability of default in the calculations.The objective of this thesis was therefore to develop scoring model that can estimate the probability of default in lending capital to enterprises based on information from financial statements. The aim is that the developed model also can be used in the daily operations to reduce fixed costs by optimizing the processes and increase the profit on each loan issued. The model should estimate probability of default within 500 days from the last known information and be customized for small and medium size enterprises.The model is based on logistic regression and is therefore returning values between 0 and 1. Parameters that the model consists of can either be calculated or retrieved directly from financial statements. The authors have during the development of the model divided the data, consisting of information from enterprises, based on branches. The grouping of data has been performed to create as homogenous sets of data as possible in order to increase the degree of explanation for each model. The final solution will thus consist of several models, one for each set of data. The validation of the models is performed, on a new set of enterprises where it is observed how well the models can discriminate enterprises defined as defaults from non-defaults.The master thesis did result in a number of models that are calibrated on default, non-defaults and models developed on data divided on branches. By using the calibrated models, it is possible to discriminate defaulting from non-defaulting enterprises which has been the objective of this thesis. During the project the importance of dividing data into homogenous groups has been shown in order to better create models that more accurately can identify defaults from non-defaults. / Som en konsekvens av finanskrisen som började 2007 i USA tillkom ytterligare regelverk för att minimera bankers riskexponering. Två av de regelverk som tillkommit är Basel III och IFRS 9. Basel III reglerar kapitaltäckningen för en bank medan IFRS 9 är en standard för hur banker och försäkringsbolag skall klassificera tillgångar samt estimera framtida kreditförluster. Gemensamt för de båda regelverken är estimeringen av kreditförluster som bland annat baseras på risken för fallissemang.Målet med detta examensarbete är därför att utveckla en scoringmodell som kan estimera risken för fallissemang vid utlåning till företag baserat på information från dess årsredovisningar. Modellen kommer även kunna användas i den operativa verksamheten för att reducera fasta kostnaderna genom att effektivisera processer och då öka avkastningen på varje utlånad krona. Modellen kommer att estimera risken för fallissemang inom 500 dagar från senast kända informationen och den kommer att anpassas till svenska små och medelstora företag.Modellen är baserad på logistisk regression och kommer därför att returnera värden mellan 0 och 1 samt bestå av parametrar som antingen kan beräknas eller hämtas direkt ur en årsredovisning. För att öka modellens förklaringsgrad har författarna vid kalibreringen av modellerna delat in datat efter branscher. Uppdelningen har gjorts för att skapa så homogena grupper som möjligt och lösningen kommer därför att bestå av flera olika modeller. Validering av modellerna sker genom att på nytt data testa hur bra företag som definierats som fallissemang kan diskrimineras från företag som inte definieras som fallissemang.Rapporten resulterar i ett antal modeller som är baserade på konkurser, icke konkurser samt modeller baserade på ett data som är uppdelat på branscher. Genom att använda de kalibrerade modellerna så går det att diskriminera konkurser från icke konkurser vilket varit målet med denna rapport. Arbetet har också påvisat vikten av att dela in datat i homogena grupper för att på ett bättre sätt skapa modeller som mer exakt kan urskilja konkurser från icke konkurser.

Using a dataset of 7757 firms in Brazil, China, India, and Russia from 2009 to 2014, this article examines the effect of political risk variables on financial flexibility and the effects of financial flexibility on future firm value, capital investment, cash holdings and the probability of default while controlling for firm-level effects and political variables. Effective representation of the majority is found to be associated with a higher level of financial flexibility. In terms of the effects of financial flexibility on firm value, results that are much stronger than previously reported are found. However, unlike previous work, the current research does not find that increased financial flexibility leads to increased capital expenditures. It is found that financially flexible firms in these countries lower their probability of default on average by about 0.6 %. It is also found that giving greater voice to the majority and greater adherence to the rule of law adds to the value of firms.

Macroeconomic conditions can impact the payment capacity of individual mortgage holders' household loans. If the clients of a bank's retail credit portfolio experience deteriorating paymentcapacity it will reflect on the probability of default of the overall portfolio. With IFRS 9, banks are expected to sophisticate their calculations of expected credit loss, demanding forward-looking estimates of probability of default by incorporation of macroeconomic forecasts. Finding what macroeconomic factors have a statistical significant relationship to the actual default frequency of a portfolio can aid banks in estimating probability of default with reference to current and forecasted macroeconomic conditions. This study aims to explore the relationship between macroeconomic factors and the default frequency in a Swedish retail credit portfolio. The research is based on quantitative data analysis of historical default data, complemented by implications of the macroeconomic condition on the payment capacity of households from a theoretical perspective. Macroeconomic factors studied are the Swedish gross domestic product, house price index, reporate and unemployment rate. The supporting data consists of default data from Nordea's Swedishretail credit portfolio. The time period covers 2008-2015 and provides basis for analysis of a timeperiod with different conditions in the macroeconomy, including effects of the 2008 financial crisis. A multiple linear regression model is used as a method to suggest the relationship between themacroeconomic factors and the default frequency. The model coefficients are estimated with calculations of Ordinary Least Squares and the significance supported by statistical test. Results show that gross domestic product and repo rate are statistically significant macroeconomic variables in explaining changes in the default frequency and thus probability of default of a Swedish retail credit portfolio. / Makroekonomiska omständigheter kan påverka hushållens betalningsförmåga och i sin tur återbetalningsförmågan hos bolånetagare. Om flertalet låntagare inom en banks retailportfölj upplever en försämrad betalningsförmåga kommer det att avspeglas på sannolikheten för fallissemang (probability of default) i den totala portföljen. Med IFRS 9 förväntas banker förfina sina beräkningar av förväntade kreditförluster, vilket kräver framåtblickande beräkningar av probability of default med makroekonomiska prognoser i åtanke. Genom att identifiera vilka makroekonomiska faktorer som har statistisk signifikans för förändringar i historisk fallissemangsfrekvens i en portfölj förväntas banker kunna integrera dessa i, och därmed förbättra, sina beräkningar av probability of default. Denna studie syftar till att utreda sambandet mellan makroekonomiska faktorer och fallissemangsfrekvensen i en svensk retailportfölj. Den kvantitativa analysen av data över historiska fallissemang och makroekonomiska faktorer kompletteras med teoretiska implikationer av makroekonomiska omständigheter för hushållens betalningsförmåga. De makroekonomiska faktorer som studeras är svensk BNP, Boprisindex, Reporänta och Arbetslöshet. Fallissemangsfrekvensen baseras på data från Nordeas svenska retailportfölj som täcker åren 2008-2015 och därmed inkluderar följdeffekter av finanskrisen 2008. En multipel linjär regressionsmodell används för att förklara relationen mellan de makroekonomiska faktorerna och fallissemangsfrekvensen. Regressionskoefficienterna estimeras med hjälp av minstakvadratmetoden och kompletteras med diagnostiska test. Resultaten visar att BNP och Reporäntan är statistiskt signifikanta makroekonomiska faktorer för påvisandet av förändringar i fallissemangsfrekvensen och följaktligen Probability of Default i en svensk retailkreditportfölj.

本研究目的，是以資料採礦的觀點，配合SPSS Clementine 11.0軟體所提供的資料採礦工具，將資料採礦進行的分析流程，導入企業信用評等模型的建置程序，針對內部評等法中的企業型暴險，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。 投入模型的變數，分為財務變數以及總體經濟變數。在精細抽樣比例與模型方法的比較上，1:2比例訓練出的模型在反查率(Recall)較佳且在整體正確率(Accuracy)上亦有不錯的表現；最後模型評估結果決定使用羅吉斯迴歸模型。 本研究所建構出的信用評等系統分為8個評等等級，違約的機率隨評等遞增，以第8等作為違約戶的評等結果。信用評等的各項驗證，首先各等的授信戶均勻分布於8等之間，各評等的預測違約機率，亦相當接近實際違約機率，總結來說，本研究建構之模型具有一定的穩定性與預測效力，並且皆通過新巴塞資本協定與金管會的各項規範，顯示本研究之信用評等模型能夠在銀行授信流程實務中加以應用。

This dissertation comprises two different financial essays. Essay 1, “An Applied Credit Score Model,” uses data from local credit union to predict the probability of default. Due to recent financial crisis regulation has been enacted that makes it essential to develop a probability of default model that will mitigate charge-off losses. Using discriminant analysis and logistic regression this paper will attempt to see how well credit score can predict probability of default. While credit score does an adequate job at classifying loans, misclassification of loans can be costly. Thus while credit score is a predictor, there is danger in relying solely on its information. Thus other variables are needed in order to more accurately be able to find the probability of default. Essay 2, “Christian Mutual Fund Performance,” draws attention to a much ignored type of funds, Christian mutual funds. The following questions are asked: How does Christian mutual fund perform compared to the market? Is there a difference in performance during recessions as indicated by literature? Is Christian mutual fund performance different than SRI funds? How do Catholic and Protestant fund perform? Looking at qualitative evidence, Christian mutual funds place much more importance on moral issue than SRI funds. Thus there is a clear difference in objectives and the type of screening that these two mutual fund pursue. Overall data reflects that screened data perform worse than the market, however during recession screened funds perform as well and at times better than the market. Christian mutual funds tends to perform worse than SRI funds.

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