Στη παρούσα μελέτη, αρχικά παρουσιάζονται από θεωρητική πλευρά θέματα που αφορούν το ρυθμιστικό πλαίσιο, τον πιστωτικό κίνδυνο, τα μη εξυπηρετούμενα δάνεια και οι έννοιες Ευρωζώνη και Ευρωσύστημα. Ακολούθως, με τη χρήση ενός οικονομετρικού μοντέλου επιχειρήθηκε ο προσδιορισμός των παραγόντων εκείνων που επηρεάζουν τον δείκτη μη εξυπηρετούμενων δανείων στην Ευρωζώνη. Ο δείκτης των μη εξυπηρετούμενων δανείων ουσιαστικά συνιστάται ως προσεγγιστική μεταβλητή του πιστωτικού κινδύνου και την περίοδο αυτή της παρατεταμένης ύφεσης αποτελεί ενδεχομένως τη μεγαλύτερη απειλή που αντιμετωπίζουν τα διάφορα τραπεζικά συστήματα όλου του κόσμου. Χρησιμοποιώντας συγκεντρωτικά δεδομένα (aggregate data) σε ένα πάνελ 13 χωρών της Ευρωζώνης για την περίοδο 2000-2008 και με την βοήθεια της fixed effect προσέγγισης, εντοπίστηκαν ισχυρές συσχετίσεις μεταξύ του NPL και διαφόρων μακροοικονομικών και τραπεζικών (banκ specific) παραγόντων. Πιο συγκεκριμένα, τα ευρήματα της εμπειρικής διερεύνησης, επιβεβαιώνουν τη διεθνή βιβλιογραφία καθώς από πλευράς τραπεζικών μεταβλητών ισχυρή επίδραση στο δείκτη μη εξυπηρετούμενων δάνειων εμφανίζει ο δείκτης κεφαλαιακής επάρκειας, ο δείκτης δάνεια προς καταθέσεις και ο δείκτης των μη εξυπηρετούμενων της προηγούμενης χρονιάς. Τέλος, από μακροοικονομικής πλευράς το δημόσιο χρέος και η ανεργία φαίνεται να είναι δυο επιπλέον παράγοντες που επιδρούν στη διαμόρφωση του δείκτη, αποτυπώνοντας ότι η κατάσταση της οικονομίας των χωρών της ευρωζώνης συνδέεται άρρηκτα με τον δείκτη NPL. / In this study, from the theoretical point of you, issues regarding regulation, credit risk, non-performing loans, Eurozone and Eurosystem are presented. Then, implementing an econometric model it was examined which factors influence the ratio of nonperforming loans in the Eurozone. It is worthwhile to mention that the ratio of NPLs can be used as a proxy of credit risk. Nowadays, credit risk seems to be the greatest risk, which banking systems are facing all over the world. Particularly, Using aggregate data on a panel of 13 countries for the period 2000-2008 and applying the fixed effect approach, strong correlations between the NPL and various macroeconomic and bank specific factors are confirmed. Our findings largely agree with the literature as, in terms of bank-specific variables, the capital ratio, the loans to deposits ratio and the rate of non-performing loans of the previous year appear to exert a powerful influence on the non-performing loans rate. At the same time, from a macroeconomic perspective, the public debt and unemployment seem to be two additional factors that affect the index, revealing that the state of the economy of Eurozone countries is clearly linked to the NPL index.

Evidence from various countries over the past two decades proves that swings in house prices have been concomitant with financial instability. The history of financial crises shows that the six biggest banking crises in advanced economies were accompanied by housing busts. Despite the abundance of literature on the forces behind the financial crisis, and in particular studies investigating the connections between financial stability and disturbances in the real estate market, fundamental questions still wait for convincing answers, such as: (i) To what extent is regional heterogeneity in property price increases reflected in dissimilarity in the evolution of credit default? (ii) What role do borrower-related factors such as housing affordability and household indebtedness, and financial market-related factors such as financial developments, play on the growth of bad loans as a main concern for banking sector? (iii) To which extent do banks’ lending behaviour and property prices undermine the stability of the banking sector, and what are the directions of causality between credit defaults, property prices and banks’ lending behaviour? The goal of this thesis is to investigate these issues and explain the practical implications of the findings. This thesis contains three empirical essays. The first essay explores the nexus between house prices and non-performing loans (NPLs), concentrating on the extent to which geographical variations in house prices are translated into regional variations in credit defaults. The stochastic dominance approach has been used for this purpose, with 372 individual US banks. The stochastic dominance analyses disclose symmetric behaviour between NPLs and the scale of house price increments. The essay is further extended by employing Arellano and Bond’s (1991) GMM model to explore the effect of GDP, unemployment rates, lending interest rates and house prices on the growth of NPLs. The outcomes of the GMM estimations reveal a high explanatory power of economic growth, unemployment and lending interest rates on NPLs. In an additional analysis, a generalised panel threshold model is estimated to check for the presence of a threshold point, above which different impacts of house prices might be found. The threshold model specifications provide a threshold point, in relation to which two different impacts of house prices on the evolution of NPLs are estimated. A general consensus in the literature attributes credit defaults to a wide-ranging spectrum of drivers that take into consideration borrower-related factor, lender-related factors and factors related to financial and real estate markets. The second essay attempts to answer the second question mentioned above, by investigating the impact of borrower-related factors, lender-related factors and financial market-related factors in driving NPLs. The impact of these factors on the evolution of impaired loans is explored by estimating fixed effect models then the analysis is extended to dynamic models using the GMM procedure on an annual balanced panel dataset. Household vulnerability, financial developments and housing affordability are found to be significant contributors to the growth of NPLs. The interaction mechanism between the real estate market and the financial system has often been blamed for being the root of financial crises, through the accumulation of housing market bubbles that leads to the ultimate collapse of the financial markets. The third essay, using the Autoregressive Distributed Lag technique, looks for the presence of cointegrating relationships between mortgage defaults, property prices and bank lending in Hong Kong. Our findings reveal evidence of cointegrating relationships between bank lending, property prices and mortgage defaults in the long term, which governs the correction mechanism between these variables. These outcomes call for more effort to be devoted to maintaining a balanced relationship between these factors. The essay also finds evidence of short-term dynamics between these variables. Importantly, loan-to-value is found to play the most effective role in curbing mortgage default risk in the portfolios of the Hong Kong banking sector.

A main goal of the diploma thesis is to perform a strategic analysis of a selected enterprise with the purpose of examining a current situation and trends in external and internal environment of the enterprise, and, based on the analysis, to suggest recommendations concerning a future strategy of the company.

Die Sanierung notleidender Kredite ist in den Banken und in der Wissenschaft ein prominentes Thema. Banken installieren eigene Abteilungen, denen Aufgabe die Betreuung solcher in Zahlungsverzug befindlicher Engagements obliegt. Die Wissenschaft untersucht den Themenkomplex in allen Facetten, das weithin bekannteste Gebiet sind die prognostizierten Ausfallwahrscheinlichkeiten für Unternehmen und Staaten, gemeinhin Ratings. Neben der Ermittlung der Wahrscheinlichkeit eines Zahlungsverzuges widmen sich andere Autoren den Maßnahmen, welche während einer Sanierung ergriffen werden. So auch die hier vorliegende. Einem Kreditinstitut steht im Rahmen einer Sanierung eine Vielzahl an Maßnahmen zur Verfügung. Wie wirken diese Maßnahmen auf das angestrebte Ziel der Sanierung? Unterscheiden sich Maßnahmen, die einen gleich gelagerten Fokus haben, in ihrem Einfluss auf das Ergebnis der Sanierung? Läßt sich also eine Empfehlung ableiten, welche Maßnahmen ergriffen werden sollen, um beispielsweise das Überleben eines Unternehmens zu erreichen? Der Arbeit liegt ein Datensatz zugrunde, der Daten zu deutschen Unternehmen enthält, die dem Segment der Klein- und mittelständischen zuzuordnen sind. Die verwendeten Daten wurden nicht, wie sonst üblich, aus teils frei verfügbaren Datenbanken abgerufen, sondern konnten aus den Unterlagen eines Kreditinstitutes erhoben werden. Sie bieten damit ein breiteres Spektrum als bereits aufbereitete Daten. Zum anderen konnten Informationen verarbeitet werden, die nur dem Kreditinstitut zur Verfügung stehen und im Regelfall nicht an Dritte zur Veröffentlich weitergegeben werden. Die Auswertung der erhobenen und aufbereiteten Daten erfolgt in zwei Stufen. In einem ersten Schritt werden durch univariate logistische Regressionen die aussagekräftigen Maßnahmen identifiziert. Diese Variablen werden in einem zweiten Schritt in multivariaten logistischen Regression überprüft. Das Ergebnis dieses Schrittes sind Modelle, die den Einfluss bestimmter Maßnahmen auf den jeweiligen Ausgang einer Sanierung beschreiben. Die Aufbereitung und Auswertung der Maßnahmen erfolgt hier in einem sehr hohen Detailgrad, der in anderen Arbeiten nur teilweise anzuztreffen ist. Es lassen sich anhand des Datensatzes Maßnahmen identifizieren, deren Ergreifen einen Einfluss auf den Ausgang der Untersuchung hat. Durch die aufgestellten Modelle ist es nicht ur möglich, diese Maßnahmen zu identifizieren, sondern auch ihren Einfluss zu quantifizieren. Dies erfolgt über die sog. „odd ratios“, die eine Aussage darüber liefern, wie sich das Chancen-Verhältnis einer Zielgröße ändert, wenn die im Fokus stehende Variable um eine Einheit geändert wird. Neben einer Ergänzung der bestehenden Literatur zu diesem Thema schafft diese Arbeit auch konkrete Handlungsempfehlungen für die Praxis.:Tabellenverzeichnis V Abbildungsverzeichnis VIII 1 Ziel und Gang der Untersuchung 1 2 Theoretischer Rahmen der Arbeit 6 2.1 Die Krise eines Unternehmens 7 2.2 Die Sanierung als Bewältigung einer Unternehmenskrise 14 2.3 Maßnahmen in einer Krisenbewältigung 16 2.4 Der Einfluss der Lage des Unternehmens auf das Ergebnis einer Sanierung 41 2.5 Ansätze zur Weiterentwicklung der bestehenden Literatur 64 3 Methodik der Untersuchung 68 3.1 Erhebung der Daten und Transformation in Variablen 68 3.2 Überblick über die angewandten statistischen Maße 80 3.3 Überblick über die Methoden zur Auswertung 91 3.4 Grundsätzliches Vorgehen bei der Auswertung 120 4 Beschreibung des Datensatzes 124 4.1 Herkunft der Daten 124 4.2 Ergebnis der Sanierung 125 4.3 Durchschnittliche Größe der untersuchten Unternehmen 126 4.4 Erfasste Rechtsformen 128 4.5 Erfasste Branchen 129 5 Auswertung der ermittelten Variablen 132 5.1 Auswirkung der quantitativen und qualitativen Variablen auf den Ausgang der Sanierung 132 5.2 Auswirkung der Maßnahmen auf den Ausgang der Sanierung 180 5.3 Auswirkungen der qualitativen und quantitativen Variablen auf die ergriffenen Maßnahmen 222 5.4 Überprüfung der aufgestellten Thesen 227 6 Einflussfaktoren auf den Erfolg einer Sanierung 234 6.1 Einfluss der Variablen auf Basis quantitativer und qualitativer Daten 235 6.2 Einfluss der Variablen auf Basis der Maßnahmen 236 7 Fazit 241 8 Anhang IX 8.1 Überblick über die aufgestellten Thesen IX 8.2 Branchenklassifikation nach destatis XI 8.3 Methoden zur Auswertung der Variablen XIII 8.4 Bezeichnung der Variablen zur Kennzeichnung der Branche XV 8.5 Variablen auf Basis der Daten zu den Maßnahmen XVI 8.6 Variablen auf Basis der qualitativen Daten XXVII 8.7 Signifikante Variablen in anderen Untersuchungen XXXIII 8.8 Variablen auf Basis der quantitativen Daten XL 8.9 Herleitung der Formel für Durchschnittswerte LXVI 8.10 Validierung der Ergebnisse bereits erfolgter Studien LXVIII 8.11 Univariat signifikante Variablen auf Basis der Maßnahmen LXX 8.12 Vergleich der Modelle auf Basis der qualitativen und quantitativen Variablen LXXI 8.13 Vergleich der Modelle auf Basis der Variablen zu den Maßnahmen .LXXII 8.14 Überprüfung der Thesen LXXV 8.15 Wirkung der Variablen auf Basis der Maßnahmen LXXXI 8.16 Überblick über den Einfluss der Variablen auf Basis der Maßnahmen XCII Literaturverzeichnis XCII

本文利用可以同時捕捉到銀行增加意欲產出、減少投入與減少非意欲產出的方向距離函數，評估台灣銀行業與個別銀行在民國93年至97年的無效率值與生產力變化。設定逾期放款為銀行生產放款時的非意欲產出，以考量銀行的放款品質，也考慮到銀行都會付出成本來提供服務給存戶，因此以交易性存款作為銀行提供服務的替代變數，使銀行的服務包含在效率評估內，並以固定的方向向量作為無效率值加總的基礎，來衡量台灣整體銀行業的無效率。實證結果是台灣銀行業的無效率值隨著雙卡風暴的遠去，有逐年遞減的趨勢，生產力的變化則是退步的情況較多。 / In this paper we estimate Taiwanese banks’ efficiency and productivity change during 2004-2008. The estimates are derived from the directional distance function. We treat non-performing loans as an undesirable output arising from the production of loans to measure the quality of loans. Considering every bank would make costs to serve customers, we choose transaction deposits as an alternative variable to capture the service provided by banks. The way we set the directional vector allows the aggregation of individual bank inefficiency and productivity change to the industry level. Our findings indicate that inefficiencies of Taiwanese banking industry were decreasing after the over of credit and cash card debts. And Taiwanese banking industry experienced productivity regress during this period.

In the financial industry , typical indirect-financial institution attracts deposit, inter-bank loan, or issuing negotiable certificate of time deposit and bonds.¡@After collecting money from excess capital units through auditing procedure then loan to the needed parties as a financial intermediary in the market. The roles of financial institutions such as banks are acting as a financial intermediary by providing buy-sell funding to enterprises or individuals. Those banks actually take whole funding liquidity risk to exchange main resource of bank¡¦s profitability. Once failure in managing risk or facing dynamically financial environment changing, bank may engage in difficulty and cause serious financial crisis. Comparison with large international financial institutions, our financial institutions hold a lot of NPL (Non-Performing Loan; Taiwan major NPL almost came from mortgage), it not only lower the liquidity of fund, longer payment duration but also raise operation risk can¡¦t recover financial assets. The quality of asset has also been worse off rapidly. These phenomena raise financial institution operation risk and influence stability of financial system and development of financial environment. With the financial environment is changing, those developed countries mostly adopted structured finance or financial asset securitization methods. The purpose of financial asset securitization in general is to raise fund for originator. Originator is the most important participant on the securitization process. The originators pool and reorganize those assets, which could create cash flow into small-amount unit security and sell to the investors. By this way originator don¡¦t have to wait till maturity and buyback those securities. That is why by using financial asset securitization will help financial institution to improve asset/liability management, spread asset risk and increase the ratio of equity to assets. At the same time, this will improve the effect and efficiency of finance institution¡¦s operating and open up the funding market. Mortgage securitization can raise banks¡¦ capital adequacy and current ratio. By way of asset securitization, the originators enjoy higher asset liquidity, lower funding cost, and improved capital ratio; while investors can use mortgage-backed securities to diversity their portfolios, improve liquidity and enhance yields. For originators, securitization is not only lower the cost of capital, increase the net profit but also enhances the liquidity of cash and balances the assets¡¦ structure. Assets-backed securitization has been prevailed in USA for years. It effectively controls the NPL (Non-performing Loans) problem and stabilizes financial management. Through financial asset securitization optimal asset allocation model, this thesis has the following finding: 1. Financial market funding supply shows multiple effects after Banking Financial asset securitization. In the initial stage of securitization, banks will lower risky assets and then will increase to original size. 2. After Financial asset securitization, a capital adequate ratio will rise first then become normal level. 3. Under assumption that financial asset securitization does not create any capital gain or loss; bank will lower profitability at initial stage. Then after a while, profitability will increase dramatically later. 4. After consideration of risk, this research discovers that securitization wills steeper Capital Allocation Line. It means every risk taking will compensate higher return. Improve Banking efficiency and profitability. Securitization provides a groundbreaking tool to increase profitability and avoid risk. Under MBS structure, the commissions and fees, absolutely out of risk, is major and stable income of the bank. On the other hand, the successful development of USA RTC implement is another contribution to resolve NPL. In sum, financial asset securitization not only accelerates the efficiency of financial institutions for more balance capital markets but also avoids financial risk in the banking system. At present, the prime theme of he banking sector should be how to maintain sound operations by strengthening credit risk management and restructure assets quality. Introducing successful external professional partner system is another way to deal with NPL problems.

The dissertation thesis investigates the relationship between corporate social responsibility (CSR) and financial performance (FP) on the sample of 51 Eurozone banks over the period from 2008 to 2014. The investigation is based on a panel data regression analysing the financial data from Bankscope and the social performance data from CSRHub. Return on assets and the ratio of non-performing loans to total loans represent the measures of financial performance and are used as dependent variables. The results of this model have shown a positive and statistically significant CSR-FP relationship. It is argued that even though the results show statistical significance, they do not necessarily include such a strong informational value. This is caused by methodological limitations, such as potentially biased data on CSR, as well as by the theoretical ones. The main theoretical concern, detected in the dissertation thesis, is a need for redefinition of the banks' driving motives of engaging in CSR activities. Banks engaging in CSR activities for merely strategic reasons should be analysed separately on a firm-level as they may otherwise bias the empirical results. Another important aspect of the work was an argument that banks benefit from CSR mainly through the product differentiation. This could not have been tested empirically, but it is assumed that the product differentiation, for example through reputation enhancement, may play a significant role in boosting bank's profits.

The financial stability of banks is crucial if they are to fulfil their role in facilitating transactions between borrowers and lenders. The purpose of this study was to investigate the effect of credit risk on the South African banking industry following a movement in credit ratings by rating agencies. Data from a sample of 11 banks were collected from 2006 to 2015. Econometric regression analysis was used to analyse the data. The results show that inflation, credit ratings, exchange rate, gross domestic product, unemployment rate, capital adequacy ratio and size of the bank are significant factors that determine "non-performing loans". Therefore, it is imperative that banks continuously monitor these factors and adapt their credit policies on "non-performing loans". This action would prepare banks for any adverse effects and ensure that the banking industry remains a sound and efficient contributor to the growth of the South African economy. / Business Management / M. Com. (Business Management)

This thesis investigates three different techniques for estimating loss given default of non-performing consumer loans. This is a contribution to a credit risk evaluation model compliant with the regulations stipulated by the Basel Accords, regulating the capital requirements of European financial institutions. First, multiple linear regression is applied, and thereafter, zero-and-one inflated beta regression is implemented in two versions, with and without Bayesian inference. The model performances confirm that modeling loss given default data is challenging, however, the result shows that the zero-and-one inflated beta regression is superior to the other models in predicting LGD. Although, it shall be recognized that all models had difficulties in distinguishing low-risk loans, while the prediction accuracy of riskier loans, resulting in larger losses, were higher. It is further recommended, in future research, to include macroeconomic variables in the models to capture economic downturn conditions as well as adopting decision trees, for example by applying machine learning. / Detta examensarbete undersöker tre olika metoder för att estimera förlusten vid fallissemang för icke-presterande konsumentlån. Detta som ett bidrag till en kreditrisksmodell i enlighet med bestämmelserna i Baselregelverken, som bland annat reglerar kapitalkraven för europeiska finansiella institut. Inledningsvis tillämpas multipel linjär regression, därefter implementeras två versioner av utvidgad betaregression, med och utan bayesiansk inferens. Resultatet bekräftar att modellering data för förlust givet fallissemang är utmanande, men visar även att den utvidgade betaregressionen utan bayesiansk inferens är bättre de andra modellerna. Det ska dock tilläggas att alla modeller visade svårigheter att estimera lån med låg risk, medan tillförlitligheten hos lån med hög risk, vilka generellt sett medför större förluster, var högre. Vidare rekommenderas det för framtida forskning att inkludera makroekonomiska variabler i modellerna för att fånga ekonomiska nedgångar samt att implementera beslutsträd, exempelvis genom applicering av maskininlärning.

This thesis evaluates the performance of three machine learning methods in prediction of the Loss Given Default (LGD). LGD can be seen as the opposite of the recovery rate, i.e. the ratio of an outstanding loan that the loan issuer would not be able to recover in case the customer would default. The methods investigated are decision trees, random forest and boosted methods. All of the methods investigated performed well in predicting the cases were the loan is not recovered, LGD = 1 (100%), or the loan is totally recovered, LGD = 0 (0% ). When the performance of the models was evaluated on a dataset where the observations with LGD = 1 were removed, a significant decrease in performance was observed. The random forest model built on an unbalanced training dataset showed better performance on the test dataset that included values LGD = 1 and the random forest model built on a balanced training dataset performed better on the test set where the observations of LGD = 1 were removed. Boosted models evaluated in this study showed less accurate predictions than other methods used. Overall, the performance of random forest models showed slightly better results than the performance of decision tree models, although the computational time (the cost) was considerably longer when running the random forest models. Therefore decision tree models would be suggested for prediction of the Loss Given Default. / Denna uppsats undersöker och jämför tre maskininlärningsmetoder som estimerar förlust vid fallissemang (Loss Given Default, LGD). LGD kan ses som motsatsen till återhämtningsgrad, dvs. andelen av det utstående lånet som långivaren inte skulle återfå ifall kunden skulle fallera. Maskininlärningsmetoder som undersöks i detta arbete är decision trees, random forest och boosted metoder. Alla metoder fungerade väl vid estimering av lån som antingen inte återbetalas, dvs. LGD = 1 (100%), eller av lån som betalas i sin helhet, LGD = 0 (0%). En tydlig minskning i modellernas träffsäkerhet påvisades när modellerna kördes med ett dataset där observationer med LGD = 1 var borttagna. Random forest modeller byggda på ett obalanserat träningsdataset presterade bättre än de övriga modellerna på testset som inkluderade observationer där LGD = 1. Då observationer med LGD = 1 var borttagna visade det sig att random forest modeller byggda på ett balanserat träningsdataset presterade bättre än de övriga modellerna. Boosted modeller visade den svagaste träffsäkerheten av de tre metoderna som blev undersökta i denna studie. Totalt sett visade studien att random forest modeller byggda på ett obalanserat träningsdataset presterade en aning bättre än decision tree modeller, men beräkningstiden (kostnaden) var betydligt längre när random forest modeller kördes. Därför skulle decision tree modeller föredras vid estimering av förlust vid fallissemang.