Credit risk measurement model for small and medium enterprises : the case of Zimbabwe

Abstracts in English, Zulu and Southern SothoThe advent of Basel II Capital Accord has revolutionised credit risk measurement (CRM) to the extent that the once “perceived riskier bank assets” are now accommodated for lending. The Small and Medium Enterprise (SME) sector has been traditionally perceived as a riskier and unprofitable asset for lending activity by Commercial Banks, in particular. But empirical studies on the implementation of the Basel II internal-ratings-based (IRB) framework have demonstrated that SME credit risk is measurable. Banks are still finding it difficult to forecast SME loan default and to provide credit to the sector that meet Basel’s capital requirements. The thesis proposes to construct an empirical credit risk measurement (CRM) model, specifically for SMEs, to ameliorate the adverse effects of SME credit inaccessibility due to high information asymmetry between financial institutions (FI) and SMEs in Zimbabwe. A well-performing and accurate CRM helps FIs to control their risk exposure through selective granting of credit based on a thorough statistical analysis of historical customer data. This thesis develops a CRM model, built on a statistically random sample, known-good-bad (KGB) sample, which is a better representation of the through-the-door (TTD) population of SME loan applicants. The KGB sample incorporates both accepted and rejected applications, through reject inference (RI). A model-based bound and collapse (BC) reject inference methodology was empirically used to correct selectivity bias inherent in CRM domain. The results have shown great improvement in the classification power and aggregate supply of credit supply to the SME portfolio of the case-studied bank, as evidenced by substantial decrease of bad rates across models developed; from the preliminary model to final model designed for the case-studied bank. The final model was validated using both bad rate, confusion matrix metrics and Area under Receiver Operating Characteristic (AUROC) curve to assess the classification power of the model within-sample and out-of-sample. The AUROC for the final model (weak model) was found to be 0.9782 whilst bad rate was found to be 14.69%. There was 28.76% improvement in the bad rate in the final model in comparison with the current CRM model being used by the case-studied bank.Isivumelwano seBasel II Capital Accord sesishintshe indlela yokulinganisa ubungozi bokunikezana ngesikweletu credit risk measurement (CRM) kwaze kwafika ezingeni lapho izimpahla ezazithathwa njengamagugu anobungozi “riskier bank assets” sezimukelwa njengesibambiso sokuboleka imali. Umkhakha wezamaBhizinisi Amancane naSafufusayo, phecelezi, Small and Medium Enterprise (SME) kudala uqondakala njengomkhakha onobungozi obukhulu futhi njengomkhakha ongangenisi inzuzo, ikakhulu njengesibambiso sokubolekwa imali ngamabhange ahwebayo. Kodwa izifundo zocwaningo ezimayelana nokusetshenziswa nokusetshenziswa kwesakhiwo iBasel II internal-ratings-based (IRB) sezikhombisile ukuthi ubungozi bokunikeza isikweletu kumabhizinisi amancane nasafufusayo (SME) sebuyalinganiseka. Yize kunjalo, amabhange asathola ukuthi kusenzima ukubona ngaphambili inkinga yokungabhadeleki kahle kwezikweletu kanye nokunikeza isikweletu imikhakha enemigomo edingekayo yezimali kaBasel. Lolu cwaningo beluphakamisa ukwakha uhlelo imodeli ephathekayo yokulinganisa izinga lobungozi bokubolekisa ngemali (CRM) kwihlelo lokuxhasa ngezimali ama-SME, okuyihlelo elilawulwa yiziko lezimali ezweni laseZimbabwe. Imodeli ye-CRM esebenza kahle futhi eshaya khona inceda amaziko ezimali ukugwema ubungozi bokunikezana ngezikweletu ngokusebenzisa uhlelo lokunikeza isikweletu ababoleki abakhethekile, lokhu kususelwa ohlelweni oluhlaziya amanani edatha engumlando wekhasimende. Imodeli ye-CRM ephakanyisiwe yaqala yakhiwa ngohlelo lwamanani, phecelezi istatistically random sample, okuluphawu olungcono olumele uhlelo lwe through-the-door (TTD) population lokukhetha abafakizicelo zokubolekwa imali bama SME, kanti lokhu kuxuba zona zombili izicelo eziphumelele kanye nezingaphumelelanga. Indlela yokukhetha abafakizicelo, phecelezi model-based bound-and-collapse (BC) reject-inference methodology isetshenzisiwe ukulungisa indlela yokukhetha ngokukhetha ngendlela yokucwasa kwisizinda seCRM. Imiphumela iye yakhombisa intuthuko enkulu mayelana namandla okwehlukanisa kanye nokunikezwa kwezikweletu kuma SME okungamamabhange enziwe ucwaningo lotho., njengoba lokhu kufakazelwa ukuncipha okukhulu kwe-bad rate kuwo wonke amamodeli athuthukisiwe. Imodeli yokuqala kanye neyokugcina zazidizayinelwe ibhange. Imodeli yokugcina yaqinisekiswa ngokusebenzisa zombili indlela isikweletu esingagculisi kanye negrafu ye-Area under Receiver Operating Characteristic (AUROC) ukulinganisa ukwehlukaniswa kwamandla emodeli engaphakathi kwesampuli nangaphandle kwesampuli. Uhlelo lwe-AUROC lwemodeli yokugcina (weak model) lwatholakala ukuthi luyi 0.9782, kanti ibad rate yatholakala ukuthi yenza i-14.69%. Kwaba khona ukuthuthuka nge-28.76% kwi-bad rate kwimodeli yokugcina uma iqhathaniswa nemodeli yamanje iCRM model ukuba isetshenziswe yibhange elithile.Basel II Capital Accord e fetotse tekanyo ya kotsi ya mokitlane (credit risk measurement (CRM)) hoo “thepa e kotsi ya dibanka” ka moo e neng e bonwa ka teng, e seng e fuwa sebaka dikadimong. Lekala la Dikgwebo tse Nyane le tse Mahareng (SME) le bonwa ka tlwaelo jwalo ka lekala le kotsi e hodimo le senang ditswala bakeng sa ditshebetso tsa dikadimo haholo ke dibanka tsa kgwebo. Empa dipatlisiso tse thehilweng hodima se bonweng kapa se etsahetseng tsa tshebetso ya moralo wa Basel II internal-ratings-based (IRB) di supile hore kotsi ya mokitlane ya SME e kgona ho lekanngwa. Leha ho le jwalo, dibanka di ntse di thatafallwa ke ho bonelapele palo ya ditlholeho tsa ho lefa tsa diSME le ho fana ka mokitla lekaleng leo le kgotsofatsang ditlhoko tsa Basel tsa ditjhelete. Phuputso ena e ne sisinya ho etsa tekanyo ya se bonwang ho mmotlolo wa kotsi ya mokitlane (CRM) tshebetsong ya phano ya tjhelete ya diSME e etswang ke setsi sa ditjhelete (FI) ho la Zimbabwe. Mmotlolo o sebetsang hantle hape o fanang ka dipalo tse nepahetseng o dusa diFI hore di laole pepeso ya tsona ho kotsi ka phano e kgethang ya mokitlane, e thehilweng hodima manollo ya dipalopalo ya dintlha tsa histori ya bareki. Mmotlolo o sisingwang wa CRM o hlahisitswe ho tswa ho sampole e sa hlophiswang, e leng pontsho e betere ya setjhaba se ikenelang le monyako (TTD) ya batho bao e kang bakadimi ba tjhelete ho diSME, hobane e kenyelletsa bakopi ba amohetsweng le ba hannweng. Mokgwatshebetso wa bound-and-collapse (BC) reject-inference o kentswe tshebetsong ho nepahatsa tshekamelo ya kgetho e leng teng ho lekala la CRM. Diphetho tsena di bontshitse ntlafalo e kgolo ho matla a tlhophiso le palohare ya phano ya mokitlane ho diSME tsa banka eo ho ithutilweng ka yona, jwalo ka ha ho pakilwe ke ho phokotseho ya direite tse mpe ho pharalla le dimmotlolo tse hlahisitsweng. Mmotlolo wa ho qala le wa ho qetela e ile ya ralwa bakeng sa banka. Mmotlolo wa ho qetela o ile wa netefatswa ka tshebediso ya bobedi reite e mpe le mothinya wa Area under Receiver Operating Characteristic (AUROC) ho lekanya matla a kenyo mekgahlelong a mmotlolo kahare ho sampole le kantle ho yona. AUROC bakeng sa mmotlo wa ho qetela (mmotlolo o fokotseng) e fumanwe e le 0.9782, ha reite e mpe e fumanwe e le 14.69%. Ho bile le ntlafalo ya 28.76% ho reite e mpe bakeng sa mmotlolo wa ho qetela ha ho bapiswa le mmotlolo wa CRM ha o sebediswa bankeng yona eo.Graduate School of Business LeadershipD.B.L

Szelekciós torzítás és csökkentése az adósminősítési modelleknél

A bankoknak létérdeke, hogy minél több és jobb minőségű adatot szerezzenek az ügyfelekről, és ezekből minél több információhoz jussanak az ügyfelek fizetési képességével és hajlandóságával kapcsolatban. Ezt a célt szolgálja az adósminősítéshez használt credit scoring is. A credit scoring módszerek széleskörű alkalmazásának ellenére még mindig vannak a módszertannak olyan aspektusai, amelyek nem kaptak elegendő figyelmet sem a szakirodalomban, sem a gyakorlatban. A modellépítési minta reprezentativitásának kérdése például ilyen terület. Az adósminősítési modellek általában nem reprezentatív mintán épülnek, hiszen itt tipikusan csak azoknál az ügyfeleknél rendelkezünk teljes adatállománnyal, akik átestek egy hitelbírálati folyamaton és elfogadták őket. Ha csak a befogadott ügyfelek adatait használják a modellek építéséhez, akkor megkérdőjelezhető lesz azok érvényessége, hiszen a befogadottak és az elutasítottak eloszlása valószínűleg különbözik a szisztematikus elbírálási folyamat eredményeként, így a befogadottak nem reprezentálják a teljes sokaságot jelentő összes kérelmezőt. Ezt a jelenséget nevezzük elutasítási torzításnak (reject bias), vagy általánosabban szelekciós torzításnak. A dilemmára az elutasítottak jellemzőinek felhasználásával történő modellépítés (reject inference) jelenthet választ. Ez tulajdonképpen annak becslése, hogy hogyan viselkedett volna az elutasított kérelmező, ha megkapta volna a hitelt. A dolgozatban a credit scoring modelleknél fellépő szelekciós torzítás csökkentésére használható módszerekkel foglalkozunk. A jelenség vizsgálata a magyarnyelvű szakirodalomból szinte teljesen hiányzik, csak említés szintjén találkozhattunk vele. A témaválasztást elméleti érdekességén túl gyakorlati jelentőssége is indokolta. Hiszen ha csak egy kicsit is sikerül javítani a modellek teljesítményén, az óriási profitnövekedést, és/vagy kockázatcsökkenést eredményezhet a bankok számára, mivel nagy volumenű kihelyezésekről van szó. A kockázatok pontosabb értékelése ugyanakkor az ügyfelek számára is előnyös, mert a jó adósok számára a kockázati felár csökkentését teszi lehetővé, vagy megfelelő kockázati felárral olyanok is kaphatnak hitelt, akiket eddig elutasítottak. Az adósminősítési modelleknél fellépő szelekciós torzítás adathiányból eredő probléma, hiszen a korábban elutasított banki ügyfelek esetén a hitelkockázatot (hitelvisszafizetést) leíró eredményváltozó értéke hiányzik (nem megfigyelhető), ezért a dolgozatban a szelekciós torzítás csökkentését szolgáló módszerek ismertetése előtt a hiányzó adatok típusait és kezelésük lehetséges módjait, valamint a credit scoring feladatát és a leggyakrabban alkalmazott módszereket is áttekintjük. Összegezve elmondható, hogy az elutasítottak tényleges és imputált adatainak alkalmazásának haszna függ az elutasítási aránytól, a mintabeli és sokasági eloszlásoktól és az alkalmazott statisztikai feltételek teljesülésétől. Az alkalmazott feltételek teljesülése azonban általánosságban nem tesztelhető, így a torzítás csökkentésének egyetlen robusztus és megbízható módja, ha az elutasítottak egy részét ténylegesen meghitelezik és így figyelik meg viselkedésüket és esetleges bedőlésüket. Az empirikus kutatás keretében egy valós banki adatbázison (lakossági hitelkártya adatokon) vizsgáltam az ezzel a módszerrel elérhető javulást, annak költségeit és várható hasznait, és a modellszámítások eredményeként a gyakorlati szakemberek számára hasznosítható ajánlásokat fogalmaztam meg

NOVEL ALGORITHMS AND TOOLS FOR LIGAND-BASED DRUG DESIGN

Computer-aided drug design (CADD) has become an indispensible component in modern drug discovery projects. The prediction of physicochemical properties and pharmacological properties of candidate compounds effectively increases the probability for drug candidates to pass latter phases of clinic trials. Ligand-based virtual screening exhibits advantages over structure-based drug design, in terms of its wide applicability and high computational efficiency. The established chemical repositories and reported bioassays form a gigantic knowledgebase to derive quantitative structure-activity relationship (QSAR) and structure-property relationship (QSPR). In addition, the rapid advance of machine learning techniques suggests new solutions for data-mining huge compound databases. In this thesis, a novel ligand classification algorithm, Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps (LiCABEDS), was reported for the prediction of diverse categorical pharmacological properties. LiCABEDS was successfully applied to model 5-HT1A ligand functionality, ligand selectivity of cannabinoid receptor subtypes, and blood-brain-barrier (BBB) passage. LiCABEDS was implemented and integrated with graphical user interface, data import/export, automated model training/ prediction, and project management. Besides, a non-linear ligand classifier was proposed, using a novel Topomer kernel function in support vector machine. With the emphasis on green high-performance computing, graphics processing units are alternative platforms for computationally expensive tasks. A novel GPU algorithm was designed and implemented in order to accelerate the calculation of chemical similarities with dense-format molecular fingerprints. Finally, a compound acquisition algorithm was reported to construct structurally diverse screening library in order to enhance hit rates in high-throughput screening

Testing the Option Value Theory of Irreversible Investment

This paper statistically tests the theory of irreversible investment under uncertainty. Using dynamic programming and contingent claims valuation alternatively, we derive the value of options to invest in capacity, where the projects are endogenous to the economic circumstances prevailing at the investment date. We then test whether capacity investment decisions made by Canadian copper mines are compatible with the theory. The result speak strongly in favor of option theory as a theory of real investment; in particular, we provide a test which rejects the Net Present Value criterion, and our model explains both investment size and timing satisfactorily from a statistical and from an economic point of view. En recourrant tour à tour à la programmation dynamique et à la méthode des actifs contigents, nous établissons la valeur de l'option d'effectuer des investissements irréversibles réels qui sont sensibles aux paramètres économiques prévalant au moment de la décision. Nous testons ensuite si des investissements en capacité de production effectués par des mines de cuivre canadiennes sont conformes aux implications de la théorie. Les résultats sont fortements en faveur de celle-ci; nos données rejettent le critère de la valeur actuelle nette et le modèle explique tant la taille que la date des investissements d'une manière statistiquement et économiquement satisfaisante.Irreversible investment/Uncertainty/Dynamic programming/Contingent claims/Option value/Putty Clay/Real investment, Investissement irréversible; Incertitude; Programmation dynamique; Actifs contingents; Valeur d'option; modèle Putty Clay; Investissement réel.

Statistical structures for internet-scale data management

Efficient query processing in traditional database management systems relies on statistics on base data. For centralized systems, there is a rich body of research results on such statistics, from simple aggregates to more elaborate synopses such as sketches and histograms. For Internet-scale distributed systems, on the other hand, statistics management still poses major challenges. With the work in this paper we aim to endow peer-to-peer data management over structured overlays with the power associated with such statistical information, with emphasis on meeting the scalability challenge. To this end, we first contribute efficient, accurate, and decentralized algorithms that can compute key aggregates such as Count, CountDistinct, Sum, and Average. We show how to construct several types of histograms, such as simple Equi-Width, Average-Shifted Equi-Width, and Equi-Depth histograms. We present a full-fledged open-source implementation of these tools for distributed statistical synopses, and report on a comprehensive experimental performance evaluation, evaluating our contributions in terms of efficiency, accuracy, and scalability

Migration, self selection and returns to education in the WAEMU.

Using data from labour force surveys conducted simultaneously in the capital cities of seven West African Economic and Monetary Union countries, we estimate a model of residential location choice in which expected earnings play a role. The model is first estimated in a reduced form. Estimates are then used to correct for the endogeneity of locational choice in the earnings equations estimated for each country. We find that migration behaviour has a significant effect in shaping earnings differentials between education levels and between the seven capital cities. Corrected predicted earnings in each country are then used as an independent variable in a structural multinomial logit of residential choice. Results show that individuals tend to reside in countries in which their expected earnings are higher than elsewhere.Migration; Self-selection; West Africa;

Incentive-Compatible Critical Values

Statistical hypothesis tests are a cornerstone of scientific research. The tests are informative when their size is properly controlled, so the frequency of rejecting true null hypotheses (type I error) stays below a prespecified nominal level. Publication bias exaggerates test sizes, however. Since scientists can typically only publish results that reject the null hypothesis, they have the incentive to continue conducting studies until attaining rejection. Such $p$-hacking takes many forms: from collecting additional data to examining multiple regression specifications, all in the search of statistical significance. The process inflates test sizes above their nominal levels because the critical values used to determine rejection assume that test statistics are constructed from a single study---abstracting from $p$-hacking. This paper addresses the problem by constructing critical values that are compatible with scientists' behavior given their incentives. We assume that researchers conduct studies until finding a test statistic that exceeds the critical value, or until the benefit from conducting an extra study falls below the cost. We then solve for the incentive-compatible critical value (ICCV). When the ICCV is used to determine rejection, readers can be confident that size is controlled at the desired significance level, and that the researcher's response to the incentives delineated by the critical value is accounted for. Since they allow researchers to search for significance among multiple studies, ICCVs are larger than classical critical values. Yet, for a broad range of researcher behaviors and beliefs, ICCVs lie in a fairly narrow range

The effects of short-term training measures on the individual unemployment duration in West Germany

Short-term training measures are the most important intervention of German active labor market policy in terms of persons promoted. However, evidence on the impacts of programs is missing. This study analyzes the effects of these programs on the individual unemployment duration in West Germany. By applying a multivariate mixed proportional hazards model, we are able to consider information of the timing of treatment in the unemployment spell as well as observable and unobservable factors to control for selectivity. Moreover, we allow treatment effects to vary over time and take account of heterogeneity in the effects due to individual differences. --Training Measures,Active Labor Market Policy,West Germany,Multivariate Mixed Proportional Hazards,Time-Varying Treatment Effects