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.

日本經濟自1990年起，由「日本第一」落入「流動性陷阱」，而陷入長達10多年的不景氣，主因是日本股市及不動產市場重挫，企業向銀行貸款所提供之擔保品價值下滑，卻因在低利率時代已過度借貸，又經營不善面臨虧損，發生償債困難，一旦財務有所改善，只想提前償還貸款，而無增加貸款意願，故稱為「資產負債表的衰退」（Balance Sheet Recession）。整體經濟景氣蕭條，國內需求不振，亦使振興經濟之寬鬆貨幣政策無法達到預期效果。 日本資產泡沫的破滅使銀行體系的逾放問題日益嚴重。日本政府為了加強銀行體系的健全性，實施金融改革（Big Bang）。使原本以傳統存、放款業務為主的銀行，在面臨國際化浪潮時，也能同時經營證券、保險業務，並將新金融商品引進日本。並由隸屬於內閣府的金融廳(Financial Services Agency)來監督日本銀行及證券業務，負責金融檢查及金融法規企劃業務，落實金融與財政分離之原則。但日本金融業務日益多元化，及衍生性金融商品日趨複雜，對金融監理機關之專業能力，形成新的挑戰。以上所述為日本國內的經濟與金融問題。 至於日圓對外幣的匯率方面，由於日圓利率偏低，套利交易(carry trade) 盛行。投資人趁著日本央行維持低利率之際，借入低成本的日圓資金，然後換成利率較高的外幣轉戰國際市場，追逐收益較高的資產，同時賺取利差、匯率及資產升值的價差，使日圓匯率的走勢疲弱，也造成全球金融市場的波動。 本論文的分析包含： ㄧ、日本經濟不景氣問題剖析：股市及不動產資產泡沫化 二、日本金融危機形成原因：資產價格下跌，影響抵押品價值，企業償債能力變差，故使銀行不良債權增加。 三、日本總體經濟近況（GDP、CPI、失業率的變化）及經濟復甦後日本央行貨幣政策的改變 四、日本金融市場如股市、房地產市場及日本政府債券（JGB）市場的分析及展望。 五、探討日本銀行業獲利能力、不良債權問題、資本適足率以及銀行業股價指數的變化。 六、根據台灣以及日本最近的發展對金融監理單位及銀行業提出應有的改革與建議。 / The Japanese economy fell into a “liquidity trap” in 1990. Due to the stock market and real estate market plunge, the deep recession has lasted for over 10 years. The bursting of asset bubbles caused the balance sheets of enterprises to become weaker and weaker. All companies hoped to reduce their debt to banks if they were profitable. They had no intention to reinvest any more. So it was called - Balance Sheet Recession. Even though the Bank of Japan adopted an easy monetary policy, the financial system remained vulnerable. With the bad debt of commercial banks increasing, the NPL (non-performing loan) problem has been a major concern for city banks and regional banks. Japan's "Big Bang" reforms radically altered its financial marketplace. The barriers separating banks, securities, and insurance companies were lowered. The Financial Services Agency replaced Ministry of Finance to oversee banking, securities and exchange and insurance in order to ensure the stability of the financial system. As for financial business diversified and derivative products complicated, there were many great challenges facing the financial regulatory authorities. During the past decade, the yen carry trade has become a target for many investors or speculators. Traders using this strategy attempt to capture the difference between the interest rates of two currencies. Taking USD/Yen for example, they borrowed the cheaper yen and invested in U.S. Treasuries yielding a higher interest rate. It causes the depreciation of Japanese Yen and increases the volatility of financial markets. This essay describes Japanese financial crisis, Japanese monetary policy, stock market, and real estate market. Besides, I analyze the profitability, capital adequacy, and non-performing problems of Japanese banks. Finally, I give my personal opinions on Taiwan and Japan’s banking industry.

Vykdydami savo veiklą bankai susiduria su įvairia rizika, susijusia su lūkesčiais, kad gaunama grąža kompensuos prisiimtą riziką. Bankų veiklos rizikingumą sustiprina ne tik vidinės bankų valdymo klaidos, bet taip pat ekonomikos svyravimai arba verslo ciklai. Ekonomikos augimo laikotarpiu bankai optimistiškai vertina skolininkų ateities perspektyvas ir todėl vykdo liberalią kreditų teikimo politiką. Prasidėjus ekonomikos kritimui, sulėtėjus pinigų srautams, bankų rizikingumas išauga, tai reikalauja didesnių atidėjinių, rezervų ir aukštesnio kapitalo lygio. Problemos aktualumą patvirtina ir paskutinė finansų krizė, kuri yra didžiausia nuo Didžiosios depresijos laikų. Finansų sektoriuje kilusi krizė atsiliepė „tikrajai“ ekonomikai ir sukėlė ekonominiams sunkmečiams būdingus padarinius. Todėl yra ieškoma būdų kaip tinkamai vertinant bankų riziką, laiku užkirsti kelią finansinėms krizėms, o kartu išvengti bereikalingų suvaržymų, stabdančių finansų sektoriaus ir viso ūkio plėtrą. Dėl visų minėtų priežasčių bankų rizikos problemos pastaruoju metu susilaukia daug mokslinės visuomenės, bankų priežiūros ir pačių bankų dėmesio. Darbo objektas – pasirinktų, Lietuvoje veikiančių, komercinių bankų riziką atspindintys rodikliai ir jų ryšys su verslo ciklu. Darbo tikslas – ištirti verslo ciklo poveikį bankų rizikai. Darbo tikslui pasiekti, darbe numatoma išspręsti šiuos uždavinius: • Išskirti bankų rizikos šaltinius; • Išanalizuoti kaip bankų rizika pasikeičia, kintant ekonominėms sąlygoms... [toliau žr. visą tekstą] / Banks in the course of their work are confronted with various risks. That’s risks are associated with the expectation, that the return will compensate the risk assumed by bank. Risk in banking activities not only strengthens the internal management of a bank error, but also economic fluctuations or business cycles. In economic growth times, banks are optimistic about the future prospects of the borrowers and therefore banks acts a liberal supply of credit policies, reducing lending standards. When economy stat’s to fall, the cash flow of money will slow, bank risk profile increases, it requires larger provisions, reserves and a higher level of capital. The work issues confirms the relevance of the last financial crisis, which is the largest since the Great Depression. The financial sector crisis effected "the real" economy and financial crisis caused the specific effects of economic recessions. So it is looking for ways of properly assessing the risk of bank, and to prevent financial crises in time, to avoid unnecessary constraints hindering the financial sector and the economy development. For all these reasons, the banks' risk problems recently attracts many scientific societies, banking supervision, and most banks focus of attention. The object of work - the selection, the commercial banks, operating in Lithuania, risk-reflective indicators and indicators link to the business cycle. The aim of work - to explore the business cycle effects of bank risk. To achieve the aim of... [to full text]

本研究探討台灣於2011年依據IAS 39進行34號公報之第三次修訂實施，採用已發生損失模型後的兩項議題：（1）放款壞帳費用之提列與盈餘波動性以及資本適足率波動性之關聯性，（2）以歷史成本評價之期末金額及以公允價值評價之期末金額，究竟何者對於未來之帳款沖銷與不良債權較具有關聯性。 實證結果顯示，自2011年採用已發生損失模型後盈餘波動性無顯著之變化，且壞帳費用對於盈餘波動性無解釋能力；而自2011年後資本適足率波動性亦無顯著變化，但壞帳費用對於資本適足率波動性有顯著的影響，顯示銀行明顯透過壞帳費用之提列進行資本管理而非盈餘管理。在未來信用損失預測之部分，以歷史成本評價之期末放款金額對於未來之帳款沖銷及不良債權有顯著的負相關，而以公允價值評價之期末放款金額對於未來之帳款沖銷及不良債權卻無解釋能力，可能係因未來帳款沖銷與未來不良債權之發生與放款之帳齡有顯著的關聯性，而與未來可收取之現金流量無顯著之相關。 / This study aims to investigate how Incurred Loss Model affects the recognition of loan loss provisions and the valuation of loans due to the third revision of SFAS No. 34 which was revised based on IAS 39 in 2011. For the recognition of loan loss provisions, it focuses on the relationship with earnings volatilities and capital adequacy volatilities, and for the valuation of loans, it specializes on whether credit loss predicting is related to historical cost accounting or fair value accounting. The result shows that, since the implementation of Incurred Loss Model in 2011, both the adoption of Incurred Loss Model and the loan loss provisions have no significant impact on earnings volatilities. For capital adequacy volatilities, implementing Incurred Loss Model has no effect on capital adequacy volatilities neither. However, the loan loss provisions since 2011 significantly enhance the volatilities of capital adequacy. It reveals that banks use loan loss provisions to manage capitals instead of earnings. For credit loss predicting, loans evaluated with historical cost accounting have significant negative relations with future charge-offs and non-performing loans while loans evaluated under fair value accounting do not have any explanation power. It may suggests that future charge-offs and non-performing loans are related to the aging of loans, but not the future payoffs of loans.

The maturity transformation of deposits is a primary driver of economic growth, as loans enable borrowers to spend funds, thereby growing the economy. However, if borrowers cannot repay their loans, the asset quality of banks deteriorate, resulting in non-performing loans or, worse, an economic crisis. An understanding of how macroeconomic and microeconomic determinants impact bank asset quality in South Africa can contribute to knowledge of the bank asset quality phenomenon in the African context. Due to the 2008/2009 global financial crisis, the introduction of new legislation and the value of gold exports, the South African economy presents an opportunity to make an original contribution to the knowledge of determinants that influence bank asset quality. In addition to studying bank asset quality determinants that are contested in research, this study also aims to determine whether a superior returns determinant of non-performing loans exists when comparing a bank’s profitability determinants, namely return on assets, return on equity and interest income on loans. This study applied panel data regression analysis, making use of a balanced panel approach, to study the determinants of bank asset quality. This approach recontextualises the existing bank asset quality theory for the South African financial sector. The results indicate that South Africa is not resilient against the impact of global financial crises trickling through international trade linkages and that regulatory changes do not instantly improve bank asset quality, and may even reduce the short-term asset quality. Moreover, bank asset quality in South Africa is sensitive to the total value of gold exports. It is evident from the profitability measures that the interest income on loans is the most suitable profitability measure of bank asset quality. This study provides an original contribution to bank asset quality determinants and recommends that regulators should pre-emptively determine the impact of new legislation on bank asset quality. Furthermore, interest income on loans as a profitability measure provides the most accurate results. Lastly, a single-country bank asset quality analysis is important, especially for economies that have commodity exports that significantly weigh in on the bank asset mix. / Die termyntransformasie rakende deposito's is die primêre dryfkrag vir groei in die ekonomie: Lenings maak dit vir leners moontlik om fondse te bestee, wat die ekonomie laat groei. Indien hierdie leners hul lenings egter nie kan terugbetaal nie, gaan die gehalte van bankbates agteruit, wat tot wanpresterende lenings of, nog erger, tot 'n ekonomiese krisis kan lei. As begryp kan word hoe makro-ekonomiese en mikro-ekonomiese bepalende faktore op die gehalte van bankbates in Suid-Afrika inwerk, kan dit bydra tot kennis van die verskynsel van bankbategehalte in die Afrika-konteks. In die lig van die 2008/2009 wêreldwye finansiële krisis, die uitvaardiging van nuwe wetgewing en die waarde van gouduitvoere bied die Suid-Afrikaanse ekonomie ’n geleentheid om ’n oorspronklike bydrae te lewer tot kennis van die bepalende faktore wat bankbategehalte beïnvloed. Benewens die bestudering van die bepalende faktore van die gehalte van bankbates wat in navorsing redelik omstrede is, het hierdie studie ten doel om, wanneer 'n bank se winsgewendheidsbepalers, naamlik opbrengs op bates, opbrengs op ekwiteit (eiekapitaal) en rente-inkomste op lenings, met mekaar vergelyk word, vas te stel of daar ’n superieure opbrengsbepaler van wanpresterende lenings bestaan. Vir hierdie studie is ’n regressieontleding van paneeldata uitgevoer, en daar is van ’n gebalanseerde paneelbenadering gebruik gemaak om die bepalende faktore van bankbategehalte te bestudeer. Hierdie benadering herkontekstualiseer die bestaande bankbategehalteteorie vir die Suid-Afrikaanse finansiële sektor. Die resultate van die studie dui daarop dat Suid-Afrika nie veerkragtig is om die uitwerking van wêreldwye finansiële krisisse teen te werk wat met internasionale handelskakelings deursyfer nie en dat reguleringsveranderinge nie dadelik die bankbategehalte verbeter nie; dit kan inteendeel die korttermynbategehalte verlaag. Bowendien is die bankbategehalte in Suid-Afrika gevoelig vir die totale waarde van gouduitvoere. Dit blyk uit die winsgewendheidsmaatstawwe dat die rente-inkomste op lenings die mees geskikte winsgewendheidsmaatstaf van bankbategehalte is. Hierdie studie lewer ’n oorspronklike bydrae tot die bepalers van bankbategehalte en beveel aan dat reguleerders vooruit reeds die uitwerking van nuwe wetgewing op bankbategehalte moet bepaal. Daarby voorsien rente-inkomste op lenings as winsgewendheidsmaatstaf die akkuraatste resultate. Laastens is ’n ontleding van ’n enkele land se bankbategehalte van belang, in die besonder vir ekonomieë met kommoditeitsuitvoere wat beduidend tot die samestelling van bankbates bydra. / Kadimo ya nako ye kopana ya ditipositi ke mokgwa wo bohlokwa wa kgolo ya ekonomi, ka ge dikadimo di dumelela baadimi go šomiša matlotlo, go realo e le go godiša ekonomi. Efela, ge baadimi ba sa kgone go lefela dikadimo tša bona, boleng bja thoto ya dipanka bo a phuhlama, go feleletša go e ba le dikadimo tše di sa šomego gabotse goba, go feta fao, phuhlamo ya ekonomi. Kwešišo ya ka fao ditaetšo tša makroekonomi le maekroekonomi di huetšago boleng bja thoto ya panka ka Afrika Borwa e ka ba le seabe go tsebo ya taba ya boleng bja thoto ya panka go ya ka seemo sa Afrika. Ka lebaka la mathata a ditšhelete a lefase a 2008/2009, tsebišo ya molao wo moswa le boleng bja dithomelontle tša gauta, ekonomi ya Afrika Borwa e fa sebaka seabe sa mathomo tsebong ya ditaetšo tšeo di huetšago boleng bja thoto ya panka. Go tlaleletša nyakišišong ya ditaetšo tša boleng bja thoto ya panka tšeo di ganetšwago nyakišišong, maikemišetšo a nyakišišo ye gape ke go laetša ge eba taetšo ya letseno le legolo la dikadimo tše di sa šomego gabotse di gona ge go bapetšwa ditaetšo tša poelo ya panka, e lego letseno la dithoto, letseno la dišere le letseno la dikadimo. Nyakišišo ye e šomišitše tshekatsheko ya poelomorago ya datha ya phanele, ya go šomiša mokgwa wa phanele wo o lekaneditšwego, go nyakišiša ditaetšo tša boleng bja thoto ya panka. Mokgwa wa go tšwetšapele gape teori ya boleng bja thoto ya panka ya lekala la Afrika Borwa la ditšhelete. Dipoelo di laetša gore Afrika Borwa ga e fokole kgahlanong le khuetšo ya mathata a ditšhelete a lefase ao a rothelago ka dikamanong tša kgwebišano ya boditšhabatšhaba le gore diphetogo tša taolo ga di kaonafatše boleng bja thoto ya panka ka lebelo, gomme di ka fokotša le boleng bja thoto bja paka ye kopana. Go feta fao, boleng bja thoto ya panka ka Afrika Borwa bo ela hloko boleng bja palomoka bja dithomelontle tša gauta. Go a bonagala go tšwa go dikgato tša tiro ya poelo gore letseno la tswala godimo ga dikadimo ke kgato ya poelo ye maleba gagolo ya boleng bja thoto ya panka. Nyakišišo ye e fa seabe sa mathomo ditaetšo tša boleng bja thoto ya panka gomme e šišinya gore balaodi ba swanela go laetša e sa le ka pela khuetšo ya molao wo moswa ka ga boleng bja thoto ya panka. Go feta fao, letseno la tswala godimo ga dikadimo bjalo ka kelo ya tiro ya poelo le go fa dipoelo tše di lebanego gabotse. Sa mafelelo, tshekatsheko ya boleng bja thoto ya panka ya naga e tee, kudu diekonomi tšeo di nago le dithomelontle tša ditšweletšwa tšeo gagolo di dumelelago motswako wa thoto ya panka. / Business Management / Ph. D. (Management Studies)

La récente crise financière qui a débuté aux États-Unis en 2007 a révélé les faiblesses du système bancaire international se traduisant par l’effondrement de nombreuses institutions financières aux États-Unis et aussi par l’augmentation de la part des prêts non performants dans les bilans des banques européennes. Dans ce cadre, nous proposons d’abord d’estimer et de tester l’efficacité des modèles de prévisions des défaillances bancaires. L’objectif étant d’établir un système d’alerte précoce (EWS) de difficultés bancaires basées sur des variables financières selon la typologie CAMEL (Capital adequacy, Asset quality, Management quality, Earnings ability, Liquidity). Dans la première étude, nous avons comparé la classification et la prédiction de l’analyse discriminante canonique (CDA) et de la régression logistique (LR) avec et sans coûts de classification en combinant ces deux modèles paramétriques avec le modèle descriptif d’analyse en composantes principales (ACP). Les résultats montrent que les modèles (LR et CDA) peuvent prédire la faillite des banques avec précision. De plus, les résultats de l’ACP montrent l’importance de la qualité des actifs, de l’adéquation des fonds propres et de la liquidité en tant qu’indicateurs des conditions financières de la banque. Nous avons aussi comparé la performance de deux méthodes non paramétriques, les arbres de classification et de régression (CART) et le nouveau modèle régression multivariée par spline adaptative (MARS), dans la prévision de la défaillance. Un modèle hybride associant ’K-means clustering’ et MARS est également testé. Nous cherchons à modéliser la relation entre dix variables financières et le défaut d’une banque américaine. L’approche comparative a mis en évidence la suprématie du modèle hybride en termes de classification. De plus, les résultats ont montré que les variables d’adéquation du capital sont les plus importantes pour la prévision de la faillite d’une banque. Enfin, nous avons étudié les facteurs déterminants des prêts non performants des banques de l’Union Européenne durant la période 2012-2015 en estimant un modèle à effets fixe sur données de panel. Selon la disponibilité des données nous avons choisi un ensemble de variables qui se réfèrent à la situation macroéconomique du pays de la banque et d’autres variables propres à chaque banque. Les résultats ont prouvé que la dette publique, les provisions pour pertes sur prêts, la marge nette d’intérêt et la rentabilité des capitaux propres affectent positivement les prêts non performants, par contre la taille de la banque et l’adéquation du capital (EQTA et CAR) ont un impact négatif sur les créances douteuses. / The recent financial crisis that began in the United States in 2007 revealed the weaknesses of the international banking system resulting in the collapse of many financial institutions in the United States and also the increase in the share of non-performing loans in the balance sheets of European banks. In this framework, we first propose to estimate and test the effectiveness of banking default forecasting models. The objective is to establish an early warning system (EWS) of banking difficulties based on financial variables according to CAMEL’s ratios (Capital adequacy, Asset quality, Management quality, Earnings ability, Liquidity). In the first study, we compared the classification and the prediction of the canonical discriminant analysis (CDA) and the logistic regression (LR) with and without classification costs by combining these two parametric models with the descriptive model of principal components analysis (PCA). The results show that the LR and the CDA can predict bank failure accurately. In addition, the results of the PCA show the importance of asset quality, capital adequacy and liquidity as indicators of the bank’s financial conditions. We also compared the performance of two non-parametric methods, the classification and regression trees (CART) and the newly multivariate adaptive regression splines (MARS) models, in the prediction of failure. A hybrid model combining ’K-means clustering’ and MARS is also tested. We seek to model the relationship between ten financial variables (CAMEL’s ratios) and the default of a US bank. The comparative approach has highlighted the supremacy of the hybrid model in terms of classification. In addition, the results showed that the capital adequacy variables are the most important for predicting the bankruptcy of a bank. Finally, we studied the determinants of non-performing loans from European Union banks during the period 2012-2015 by estimating a fixed effects model on panel data. Depending on the availability of data we have chosen a set of variables that refer to the macroeconomic situation of the country of the bank and other variables specific to each bank. The results showed that public debt, loan loss provisions, net interest margin and return on equity positively affect non performing loans, while the size of the bank and the adequacy of capital (EQTA and CAR) have a negative impact on bad debts.

Abstracts in English, Zulu and Xhosa / The banking sector is an important industry that needs to be safeguarded because its failure is bound to have a negative knock-on effect on the economy at large. The 2007-2009 financial crises were occasioned by banks assuming disproportionate levels of risk resulting in a high incidence of non-performing loans on their books. As such, this study examined the impact of credit risk on the financial performance of 18 South African banks for the period 2008 to 2018. Panel data techniques, namely the pooled ordinary least squares (pooled OLS), fixed effects and random effects estimators were employed to test the relationship between credit risk and financial performance proxied by non-performing loans (NPLs) and by return on assets (ROA) or return on equity (ROE) respectively. The results of the study documented that credit risk is negatively related to financial performance. Thus, the higher the incidence of non-performing loans, the lower the profitability of the bank. Secondly, the study documented that growth has a positive effect on financial performance. This indicates that productivity capacity is ameliorated through bank development. Thirdly, it was found that capital adequacy is positively related to financial performance. While a greater capital adequacy ratio may instil confidence of stakeholders in a bank, making it competitive, a high capital base may be perceived as a lack of initiative and tying up resources which could have yielded better returns in alternative investments. Fourthly, the study did not find any conclusive relationship between size and financial performance. Lastly, the study found that bank leverage and financial performance are negatively related. The implications of the findings are that at a micro level, banks should observe prudent and stringent credit policies in order to limit the incidence of non-performing loans. At a macro level, regulators must enforce supervision in order to ensure that banks manage their credit risk according to the regulations to minimise the risk of bank failure. / Umkhakha wezamabhanga kulibubulo eliqakathekileko eliding ukobana litjhejwe ngombana ukwehluleka kwalo kuqaleka kungaba nomthelela omumbi kezomnotho ngokubanzi bawo. Umraro wezomnotho weminyaka ephakathi kuka -2007-2009 yayikhambisana nesikhathi lapho amabhanga athoma ukuzifaka engozini ekulukazi, kanti lokho kwarholela ebujameni besehlakalo esikhulu seenkolodo ezingenzi inzuzo encwadini zamabhanga. Yeke-ke, leli rhubhululo belihlola umthintela wesikolodo mayelana nobujamo beemali bamabhanga weSewula Afrika ali-18 ukusukela ngomnyaka ka 2008 ukufika ku 2018. Amano wephanele yedatha, wona ngilawa pooled ordinary least squares (pooled OLS), fixed effects kanye namatshwayo ameda alinganisa imithintela kusetjenzisiwe ngehloso yokuhlola itjhebiswano eliphakathi kobungozi besikolodo kanye nobujamo beemali obukhambisana neenkolodo ezingananzuzo (non-performing loans )(NPLs) begodu lokhu kukhambisana nenzuzo elethwa msebenzi wepahla eligugu (return on assets) (ROA) nanyana inzuzo elethwa magugu womnotho anjengemali/matjhezi (return on equity) (ROE) ngaleyo ndlela.. Imiphumela yerhubhululo itlolwe bona ubungozi bokulodisa buhlobene ngendlela embi nobujamo beemali. Yeke-ke, kutjho bona lokha izinga lezehlakalo zeenkolodo ezingangenisi inzuzo naliya phezulu, kutjho bona izinga lokwenza inzuzo ezincani nalo liya phasi emabhangeni. Kwesibili, irhubhululo litlolwe bona ukuhluma komnotho kunomthelela omuhle ebujameni beemali. Lokhu kutjengisa bona amandla wokukhiqiza asekelwa kuthuthukiswa kwamabhanga. Kwesithathu, kuye kwatholakala bona iimali ezaneleko zikhambisana kuhle nobujamo beemali. Kanti godu, isilinganiso esikhulu seemali ezaneleko singaletha ukuzethemba kwabadlalindima ebhangeni, lokhu kwenze ibhanga bona ibe sezingeni lokuphalisana, isisekelo esiphezulu sezeemali singathathwa njengokutlhogeka komzamo wokuhlanganisa imithombo ebeyingaletha iinzuzo ezincono kwamanye amahlelo wokutjalwa kweemali. . Kwesine, irhubhululo akhange lithole nginanyana ngiliphi itjhebiswano phakathi kobukhulu kanye nobujamo beemali. Kokugcina, irhubhululo lithole bonyana ukuqiniswa kwebhanga ngeemali kanye nobujamo beemali kuzizinto ezingahlobani kuhle. Ilwazi elitholiweko lihlathulula bona ezingeni lamabhizinisi amancani, amabhanga kufanele aqale imigomo eqinileko yokukolodisa ukobana akwazi ukwehlisa izehlakalo zeenkolodo ezingangenisi inzuzo. Ezingeni lamabhizinisi amakhulu, iimbethamthetho kufanele ziqinise ilihlo ukobana aqinisekise ukuthi amabhanga alawula ubungozi bokukolodisa ngokwemithetho ukuphungula ubungozi bokwehluleka kwamabhanga. / Icandelo lezeebhanki lushishino olubalulekileyo olufuna ukukhuselwa kuba ukusilela kwalo ngokuqinisekileyo kunganesiphumo esigangqalanga esingasihlanga kuqoqosho ngokubanzi. Ixesha lobunzima kwezemali ngowe-2007-2009 labangelwa ziibhanki ngamazinga omngcipheko angalamananga athe agqibelela kwisehlo esiphezulu seemalimboleko ezingazaliyo kwiincwadi zazo. Kananjalo, olu phononongo luvavanye impembelelo yomngcipheko wonikezomatyala kwizinga lokuphuma nokungena kwemali kwiibhaki zaseMzantsi Afrika ezili-18 kwisithuba sowe-2008 ukuya kowe-2018. Uluhlu lweenkcukachalwazi zobugcisa, olubizwa ngokuba yi-pooled ordinary least squares (i-pooled OLS), iziqikeleli zeziphumo ezizinzileyo kunye nezeziphumo zebhaqo zasetyenziswa ukuvavanya unxulumano phakathi komngcipheko wonikezomatyala kunye nezinga lokuphuma nokungena kwemali okumelwe ngokwelungelo ziimalimboleko ezingazaliyo (ii-NPL) kunye nembuyekezo yeeasethi (i-ROA) okanye imbuyekezo yezabelo (i-ROE) ngokulandelelana. Iziphumo zophononongo zingqine ngamaxwebhu ukuba umngcipheko wonikezomatyala unonxulumano olungaluhlanga nezinga lokuphuma nokungena kwemali. Ngoko ke, okona isehlo seemalimboleko ezingazaliyo siphezulu, kokona inzuzo yebhanki iphantsi. Okwesibini, uphononongo lungqine ngamaxwebhu ukuba uhlumo lunesiphumo esihle kwizinga lokuphuma nokungena kwemali. Oku kudandalazisa ukuba isakhono sokuvelisa senziwa ngcono ngophuhliso lwebhanki. Okwesithathu, kufunyaniswe ukuba isilinganiso senkunzi sinxulumene ngokukuko nezinga lokuphuma nokungena kwemali. Ngelixa umlinganiselo wesilinganiso senkunzi omkhulu unganika ukuthembeka koqoqosho kwabachaphazelekayo kwibhanki leyo, kuyenze ukuba ibe kwizinga lokukhuphisana nezinye, isiseko senkunzi ephezulu singathathwa njengokusilela kokusungula kunye nokudibanisa imithombo engeyivelise iimbuyekezo ezingcono kutyalomali olulolunye. Okwesine, uphononongo alukhange lufumanise naluphi na unxibelelwano olubonakalayo phakathi kobungakanani nezinga lokuphuma nokungena kwemali. Okokugqibela, uphononongo lufumanise ukuba inkxasomali yebhanki kunye nezinga lokuphuma nokungena kwemali zinxulumene ngokungakuhlanga. Okubhekiselele kokufunyanisiweyo kukuba kwicandelo loshishino olunganeno, iibhanki kufuneka ziqwalasele imigaqonkqubo yamatyala enobulumko nengqongqo ngenjongo yokunciphisa isehlo seemalimboleko ezingazaliyo. Kwicandelo loshishino olubanzi, abalawuli kufuneka banyanzele ukubekwa kweliso ukuqinisekisa ukuba iibhanki zilawula umngcipheko wonikezomatyala lwazo ngokwayamene nemigaqo ukunciphisa umngcipheko wokusilela kwebhanki. / Business Management / M. Com. (Business Management)