Method for assessment of companies' credit rating (AJPES S.BON model) Short description of the methodology

Transcription

1 Method for assessment of companes' credt ratng (AJPES S.BON model) Short descrpton of the methodology Ljubljana, May 2011

2 ABSTRACT Assessng Slovenan companes' credt ratng scores usng the AJPES S.BON model s based on analyzng fnancal statements and occurrence of payment default events for the entre populaton of Slovenan companes over a longer perod of tme. Payment default event s defned as the occurrence of at least one of the followng events: ntaton of bankruptcy, composton, lqudaton or compulsory lqudaton proceedngs. Transacton account blocks and court notces ssued for companes and subsdares are consdered soft nformaton, taken nto account when updatng credt ratng scores durng the year, or after credt ratng scores have been assgned based on the annual report. Credt ratng assessment comples wth Basel II regulatons, whch corporate banks use n calculatng captal requrements for credt rsks. Based on fnancal statements and the fnancal ndcators calculated on the bass thereof, ndvdual rsk factors for the potental occurrence of a payment default event are analyzed (proftablty, lqudty, ndebtedness, actvty, sze, productvty and growth of busness) and ther contrbuton to the total probablty of the potental occurrence of a payment default event. To ensure the specfc ways n whch ndvdual companes from varous ndustres conduct ther busness are consdered to the hghest extent, the AJPES S.BON model ncludes several sector-specfc submodels for companes, whch are appled accordng to ther prncpal actvty. The AJPES S.BON model s used to calculate each company's overall probablty of a payment default event occurrng wthn the next 12 months after the date of the company's fnancal accounts. The sample-dependent values are calbrated wth consderaton to the characterstcs of the Slovenan economy and ndvdual ndustry sectors over a longer tme perod, whch ncludes the overall macroeconomc cycle. The sample-ndependent or calbrated payment default probabltes are the bass for determnng credt ratng scores usng the AJPES S.BON model. The result are unbased credt ratngs for the entre populaton of Slovenan companes, whch wll help banks assess the credt rsk nvolvng the probablty of a payment default event for any Slovenan company. Other enttes wll be able to use these credt ratng scores as a bass for examnng the ablty of selected companes/busness partners to meet ther fnancal oblgatons. The AJPES S.BON model classfes Slovenan companes nto 10 credt ratng categores accordng to the credt rsk, represented by credt ratng scores rangng from SB1 to SB10. The credt ratng scores are defned on a scale of probablty that at least one of the dfferent types of payment default events wll occur n a specfc case n the 12-month perod followng the date of the relevant fnancal statements upon whch the credt ratng score s based. The frst 10 credt ratngs (SB1 through SB10) represent categores of payers, and the credt ratng SB10d represents the non-payer category. The credt ratng score of SB10d s assgned to companes n whch a payment default event has actually occurred.

3 The probablty of the occurrence of a potental payment default event s lowest wth the credt ratng of SB1, ncreasng exponentally as we move towards the credt ratng of SB10.

4 TABLE OF CONTENTS UNDERLYING DATA USED IN THE AJPES S.BON MODEL ANNUAL REPORTS ON COMPANY ACTIVITIES DEFINITION OF THE PAYMENT DEFAULT EVENT AND COLLECTION OF PAYMENT DEFAULT DATA Insolvency (bankruptcy, compulsory composton, lqudaton) MAIN STEPS INVOLVED IN THE PREPARATION AND ASSESSMENT OF THE AJPES S.BON MODEL PARAMETERS FINANCIAL INDICATORS AND ANALYSIS OF INDIVIDUAL RISK FACTORS Processng mssng values of fnancal ndcators Fnancal ndcator transformaton Selecton of a smaller subgroup of fnancal ndcators MULTIVARIATE ANALYSIS SPECIFICATION AND ASSESSMENT OF MODEL PARAMETERS Includng fnancal ndcators n the logstc model, assessment of the model parameters and selecton of the optmal model Calculaton of estmated lkelhood of payment default for companes CHAPTER III CALIBRATION OF THE AJPES S.BON MODEL AND ASSIGNMENT OF CREDIT RATING SCORES ASSIGNMENT OF CREDIT RATING IN RELATION TO THE CALCULATION OF CALIBRATED LIKELIHOOD OF PAYMENT DEFAULT DESCRIPTION OF CREDIT RATINGS TRANSITION MATRICES CHAPTER IV MODEL VALIDITY TESTING CHAPTER V UPDATING OF CREDIT RATING SCORES... 23

5 Chapter 1 1. Underlyng data used n the AJPES S.BON model 1.1. Annual reports on company actvtes The core database used n development of the AJPES S.BON model conssts of fnancal statements of all actve Slovenan companes, made at the end of the fnancal year n the perod. Companes submt ther annual reports to AJPES n order to ensure publcty of data and for natonal statstcs purposes. Pursuant to the Companes Act (Offcal Gazette of the Republc of Slovena, ssue no. 42/2006, 60/2006-amended and 10/2008, herenafter: ZGD-1), companes are requred to submt ther annual reports (ths ncludes all legal forms defned n the ZGD-1 except slent partnershps, whch are not consdered legal enttes). Ths also apples to those legal enttes whose ndvdual acts stpulate that they are requred to keep books of account and prepare annual reports n accordance wth ZGD-1 (e.g. publc commercal nsttutes and other legal forms whch provde commercal publc servces). In addton to company performance data from annual reports, AJPES S.BON model also collects nformaton on the occurrence of payment default events for the Slovenan companes n the perod n order to assess the lkelhood of payment default and assgn a credt ratng score, whle accountng for the one-year gap between the fnancal statements and the occurrence of payment default event. The correspondng payment default events were thus collected for the perod , on the populaton of companes exstng n the perod. In order to calbrate and normalze the model, we also collected nformaton on the ncdence of payment default events by year on a longer tme horzon, whch ncludes the entre macro-economc cycle, for the perod from 1994 to Thus, n our assessment of the parameters of the AJPES S.BON model, the characterstcs of the Slovenan economy were consdered to the largest extent possble, reflected n the ncdence of payment default events Defnton of the payment default event and collecton of payment default data Defnng the occurrence of a payment default event s of crucal mportance from the aspect of assessng the model and ts usefulness to the end user of credt ratng nformaton, snce the extent of the defnton affects the realzed payment default rates. The defnton of payment default was expanded wth the new Basel Accord (Basel II). It s deemed that a default event has occurred on the debtor's sde, when ether or both of the followng events occur (Resoluton on the Calculaton of the Captal Requrement for Credt Ratng Usng the Internal Credt Ratng Systems for Banks and Savngs Insttutons Approach, Offcal Gazette of the RS, 135/2006): 5

6 - The bank beleves that there s lttle probablty that the debtor wll repay ts credt oblgatons towards the bank, ts supervsng companes or any of ts subordnated companes n full, wthout havng to employ measures such as dsposal of fnancal nsurance - foreclosure (f any); - A debtor s more than 90 days late on the payment of any sgnfcant credt lablty towards the bank, ts supervsng company or any of ts subordnated companes. Notwthstandng the above defnton, there are dfferences between countres as to the defnton of payment default events whch comples wth the Basel II standard, and there are dfferences n the laws governng the bankruptcy of companes. Bearng n mnd the lmtatons regardng avalablty of drect bank data, we tred to come as close as possble to the payment default event as defned by Basel II when assessng the AJPES S.BON model. A Payment default event s therefore defned as the occurrence of one of the followng events: - bankruptcy of a company; - ntaton of compulsory composton proceedngs aganst a company; and - ntaton of lqudaton and/or mandatory lqudaton of a company Insolvency (bankruptcy, compulsory composton, lqudaton) In accordance wth the Commercal Regster of Slovena Act AJPES manages the Slovenan Busness Regster (SBR) as the central database on all busness enttes based on the terrtory of the Republc of Slovena and nvolved n a proft or non-proft busness actvty. As of , the court regster forms part of the SBR, whch means that the data on companes contaned n the SBR s entrely up-to-date. The court regster, as part of the SBR, has two parts: the man regster and documentary archve. The man regster contans nformaton about the ndvdual subject of the entry, as provded n the Court Regster Act (ths ncludes data on ntated bankruptcy proceedngs, compulsory composton proceedngs, lqudaton or compulsory lqudaton proceedngs). The resoluton on the ntaton of compulsory composton, bankruptcy or lqudaton proceedngs s entered n the SBR, as well as the resoluton on concluson of the compulsory composton, bankruptcy or lqudaton proceedngs, wth a bref mark of the manner n whch the proceedngs were concluded, and the resoluton on confrmaton of the compulsory composton havng taken place. The manner of entry of these data s defned n further detal n the Fnancal Operatons of Companes Act, under nsolvency and compulsory lqudaton proceedngs. Regstraton courts decde on the entry of data whch are requred by law to be entered nto the court regster. 6

7 The entry n the court regstry and consequently n the SBR s carred out mmedately after the court decson on regstraton s ssued and publshed on the AJPES webste at the tme of regstraton, whch s extremely mportant as publcty effects come nto beng at the tme of publcaton of the entry n the court regster. The AJPES webste also contans the underlyng documents whch served as a bass for regstraton n the court regster, and documents whch are fled n the documentary archves pursuant to the law. Untl data on ntated bankruptcy proceedngs, compulsory composton proceedngs and lqudaton was entered n the SBR on the bass of decsons receved, whch AJPES or the enttes themselves sent to competent courts. At least once per year, AJPES also carred out reconclaton of data wth the court regster, whch further ensured that the data kept n the SBR was complete and up-to-date. Data entered n the SBR or the court regster s publc. AJPES ensures data publcty by allowng access va ts webste (eprs applcaton), provdng SBR prntouts and preparng data packages selected accordng to user crtera. Easy access to data and a large user base further ncreases the qualty of SBR data. 7

8 Chapter II 2. Man steps nvolved n the preparaton and assessment of the AJPES S.BON model parameters The frst step defnes dfferent fnancal ndcators whch, accordng to economc theory, have explanatory sgnfcance for antcpatng payment default events and cover varous rsk factors leadng to the payment default event: lqudty, proftablty, ndebtedness, actvty, productvty, sze and operatonal growth. Ther predctve power n explanng the occurrence of a payment default event s tested and analyzed. Over the course of the testng process the specfcs of the operatons of companes are taken nto consderaton dependng on ther relevant ndustry or feld of operatons. In the next step these ndcators, are transformed accordng to the best optons offered by economc theory and current professonal practce. In the transformaton of ndcators we pursue the goal of obtanng maxmum predctve power of the model n explanng the occurrence of a payment default event. The transformed ndcators are then entered nto multvarate sector submodels for assessment of the payment default probablty, and ther parameters wll be assessed through applcaton of logstc regresson. Dfferent statstc methods are used to select the optmum combnaton of transformed fnancal ndcators by sectoral submodel. The next step s testng the dstngushng ablty of multvarate logstc models and calbraton of payment default rates Fnancal ndcators and analyss of ndvdual rsk factors In economc theory no generally accepted theory exsts whch determnes factors whch drectly affect whether companes become nsolvent and how exactly ths happens. When studyng ths phenomenon we draw on fnancal ndcators calculated from fnancal statements. These are often understood as symptoms of approachng nsolvency. In practce the followng groups of ndcators are used: - proftablty and cash flow ndcators, - ndebtedness or fnancal leverage ndcators, - lqudty ndcators, - actvty and asset management ndcators, - productvty ndcators, - growth ndcators and - sze ndcators. 8

9 Fnancal ndcators present basc company performance characterstcs n terms of ther economc features and compettve advantages, allowng comparson between dfferent enterprses, snce the calculaton method helps to elmnate the effect of the enterprse sze. Ths apples to all the aforementoned groups of fnancal ndcators, wth the excepton of enterprse sze ndcators whch are not ratos between fnancal categores but are fnancal categores n themselves. Companes from dfferent ndustres have dfferent operatng characterstcs, reflected n specfc segments of ther fnancal statements, and consequently also n the calculated fnancal ndcators. Due to the aforementoned characterstcs the fnancal ndcators and ther effect on the ncdence of payment default events are analyzed separately by sectoral submodel. In theory there are a multtude of dfferent ndcators whch can be calculated from companes' fnancal statements. The tradtonal approach to selectng ndcators for accountancy analyss nvolves defnng dfferent aspects of company operatons and an arbtrary selecton of a few ndcators whch shed sgnfcant lght on these aspects. If we look at many domestc and foregn textbooks, we see that dfferent authors categorze ndcators nto smlar but not entrely dentcal groups, whch are ntended to shed lght on ndvdual segments of company operatons. Amendments of accountng and other standards also affect the defnton of ndcators. Due to changes n the standard of fnancal reportng to AJPES, a few changes were ntroduced n 2006, nvolvng defntons n the calculaton of ndcators. These changes were taken nto consderaton n the defntons of fnancal ndcators between and sub-perods. In accordance wth the AJPES S.BON model methodology, a set of fnancal ndcators was defned for ndvdual rsk factors affectng the occurrence of a payment default event, wth the am of fndng the smallest subgroup of ndcators whch best reflect the ndvdual rsk factor for the occurrence of a payment default event by ndvdual sectoral submodel Processng mssng values of fnancal ndcators After the fnancal ndcators were defned and the values calculated for each of the companes ncluded n the analyss, we elmnated the problem of any mssng values of fnancal ndcators n ndvdual observatons. Proper statstcal procedure has helped us to elmnate the ssue, so that there were no more mssng values n fnancal ndcator data. 9

10 Fnancal ndcator transformaton Incluson of explanatory varables nto the model and ther transformaton are the two most mportant steps n the process of modelng payment default probablty. In lterature the followng ndcator transformatons are used most often: - categorzaton of the ndcators; - standardzaton and normalzaton of the ndcators; - use of sgmod functons; - use of non-parametrc transformaton; - smoothng. Transformaton methods are used n order to acheve a monotonous correlaton between an explanatory varable and the lkelhood of payment default. Standardzaton s used as the most popular method of transformaton, whch means that the average value s subtracted from the observed values of the varable, and the dfference s then dvded by the standard devaton of the varable. Standardzaton enables the same measurement scale for all ndcators, allowng drect comparson of assessed parameter values between ndcators. Smply usng standardzaton does not solve the ssue of non-normal dstrbuton of observed varable values, as the varable s stll asymmetrcal despte standardzaton, wth thcker tals, and the problem of nonlnearty. Other transformaton are also possble, whch attempt to solve the ssue of nonlnearty (the determned correlaton between the fnancal ndcators and the lkelhood of payment default s nonlnear, and may also be nonmonotonous), such as usng polynomal approxmatons of the functon, however ths decreases the model transparency. Because the correlaton between fnancal ndcators and the lkelhood of payment default s usually nonlnear, and because logstc regresson s based on a lnear correlaton, the nonlnear model needs to be lnearzed through transformatons. However, the most sutable transformaton functon s not known n advance. Upon revewng professonal theory and practce, we decded that AJPES S.BON model would use one of the transformaton methods, whch has been proven to be the most sutable after practcal testng on data Selecton of a smaller subgroup of fnancal ndcators We defned and tested a set of dfferent fnancal ndcators whch reflect dfferent rsk factors for the occurrence of a payment default event separately by sectoral submodel. We checked, 10

11 separately by sectoral submodel, how fnancal ndcators, as the rsk factor ndcators of ndebtedness, proftablty, actvty, productvty, growth, company sze and lqudty, relate to the probablty of a payment default event, and whether ths correlaton s consstent wth theoretcal expectatons. We tested the followng: - sgn of the correlaton; - form of the correlaton; - predctve power of fnancal ndcators when forecastng the occurrence of a payment default event. In the selecton of the subset of the best fnancal ndcators, separately by sectoral submodel, we used dfferent statstcal approaches. The forecastng power of the ndvdual fnancal ndcator n the sectoral submodel of the AJPES S.BON model was tested wth the ROC curve and the AUC statstcal measurement. The greatest dstnctve power s observed n fnancal ndcators where AUC statstcs assume the hghest values. AUC represents the measure of predctve power and, lke any other statstc, s subject to random fluctuaton caused by the sample data. Trust ntervals were calculated usng the AUC curve Multvarate analyss specfcaton and assessment of model parameters In the next step, fnancal ndcators - transformed usng the chosen transformaton method - enter multvarate logstc regressons, whch are appled to sectoral submodels wth the am of determnng ther multvarate predctve power n explanng the probablty of a payment default event n companes belongng to specfc sectors. There are varous methods of statstcal multvarate analyss whch can be used for ths purpose (dscrmnant analyss, logstc regresson, probt model, neuron networks). Logstc regresson was used to assess the parameters of the multvarate sectoral submodel of the AJPES S.BON model, as t has the least requrements regardng certan statstcal assumptons of all the alternatve methods. The advantage of usng logstc regresson s that t does not assume a normal multvarate dstrbuton of ndependent varables and a lnear correlaton between the dependent and ndependent varable. It also does not assume homoscedastcty. However, t does requre a suffcently large sample. The man dsadvantage of usng logstc regresson s ts senstvty to mult-colnearty. The result of ts presence s a greater standard error n the assessment of the model parameters and a greater standard error of the projecton. The logstc regresson model can be wrtten as: Pr e 1+ e xβ ' ( y = 1x ) = F( xβ ' ) = x' β 11

12 The logt model equaton s often wrtten as: logt [ Pr ( = 1x )] = x' β p y wth logt (p) ln 1 p The assessment of the logstc regresson parameters s based on the maxmum lkelhood method Let y 1, y 2,, y N represent a sample of N ndependent results of bnary varables Y 1, Y 2,, Y N, where these are generated n the manner ndcated by the latent regresson model. The total probablty of the observaton (the so-called lkelhood functon=), dependng on the value of the explanatory varables x 1, x 2,, x N and the vector of parameters β, can be wrtten as: L = Pr = : y = 0 ( Y = y, Y = y,..., Y = y x, x,..., x, β) 1 1 y ( 1 F( x ' β) ) F( x ' β) = ( F( x ' β) ) ( 1 F( x ' β) ) 2 2 : y = 0 n n 1 N = 1 2 N 1 y In the nterest of mathematcal smplfcaton, we usually apply the natural logarthm of the lkelhood functon: ln L = = N = 1 N = 1 ( y ln F( x ' β) + ( 1 y ) ln( 1 F( x ' β) )) ln F ( q x ' β) where q = 2y 1. The vector of the optmal value of parameters β * can be obtaned by maxmzng the logarthmzed lkelhood functon n relaton to the vector parameter β usng the teratve numerc procedure (MLE method). Standardzed assessors of the parameters of the maxmum lkelhood functon b * * of the optmal parameter values β wth consderaton to the dfferences between the varances of explanatory varables s calculated as follows: b * βs = s y β non-standardzed assessor of -th parameter s varance of the -th explanatory varable s y varance of the ob dependent varable wth the condtonal value of Pr (y = 1) 12

13 After the model parameters are assessed, we use the logt equaton Pr e = to forecast the lkelhood of payment default. ' 1+ e xβ ' ( y 1x ) = F( xβ ' ) = xβ In order to determne goodness-of-ft of logstc regresson, the Hosmer-Lemeshow (2000) test s used. In order to determne the success of the logstc regresson model we can use the so-called pseudo R 2 (Cox&Snell n Nagelkerke), whch attempts to mtate the characterstcs of the determnaton coeffcent n lnear regresson (R 2 ). In order to determne the statstc sgnfcance of the model as a whole we use the χ 2 lkelhood rato test, whch helps us test whether all coeffcents equal zero. The α value dsmsses the null assumpton and we conclude that at least one coeffcent does not equal zero. The Wald test helps us determne the statstc sgnfcance of ndvdual coeffcents of the varables ncluded n the model. Thus the statstcally nsgnfcant Wald test can help us elmnate certan varables from the model, helpng us clear the model of any unnecessary and dstractng varables Includng fnancal ndcators n the logstc model, assessment of the model parameters and selecton of the optmal model Before begnnng the multvarate analyss we have a small subset of fnancal ndcators whch ft the economc crtera and have good unvarate dscrmnatory power across ndvdual subgroups of companes, collected dependng on ther ndustry. The ndcators are transformed usng the chosen transformaton method. Logstc regresson, or the logt model, s used to assess the parameters of the multvarate sectoral submodels. In logstc regresson we can apply several methods of ncludng explanatory varables n the model. AJPES S.BON model employs the stepwse selecton method. stepwse selecton gradually ncludes and elmnates varables dependng on ther statstc sgnfcance. In the case of logstc regresson the Wald test s used as nclusve or exclusve statstcs. In the process of ncludng (transformed) fnancal ndcators n the multvarate sectoral submodels, we need to check the stablty of dscrmnatory power measured n AUC, the statstc sgnfcance and prefx of the coeffcent of ndvdual fnancal ndcators ncluded, and ensure good representaton of all relevant rsk factors or nformaton categores. When ncludng ndvdual fnancal ndcators n the multvarate sectoral submodels we also need to take nto consderaton the correlaton between them, snce logstc regresson s 13

14 senstve to the correlaton between the explanatory varables. Includng multple mutually correlated explanatory varables n the model results n nstablty of the parameters and dmnshed model qualty. Furthermore, the sgn of the parameter can be contrary to economc expectatons. In multvarate logstc regresson the ssue of correlaton between transformed ndcators reflects as the ssue of ncreasng the error of coeffcent assessment and the error of assessment of the lkelhood of payment default. Snce, n addton to the AUC measure, a 95% confdence nterval for the AUC measure was also calculated, the ssue of potental correlaton can be dentfed by analyzng the wdth of the ntervals of the AUC measure. We analyzed the results of a large number of dfferently specfed multvarate logstc sectoral submodels. When selectng the optmal sectoral submodels we took nto consderaton the presence of varous rsk factors, the sze of the AUC measure and the wdth of confdence ntervals, the Hosmer-Lemeshow goodness of ft test, the Cox&Snell and Nagelkerke pseudo R 2 and statstc sgnfcance test of the model as a whole (χ 2 test) Calculaton of estmated lkelhood of payment default for companes The AJPES S.BON model sectoral submodel parameters are assessed usng the teratve procedure of maxmzng the logarthmc maxmum lkelhood estmaton (MLE) functon. Based on the assessed parameters and actual values of the (transformed) fnancal ndcators ncluded n the model for ndvdual observaton and wth consderaton to the sector to whch t belongs, we calculate the lkelhood of payment default for ndvdual observaton usng the followng logt equaton: Pr e 1+ e xβ ' ( y = 1x ) = F( xβ ' ) = xβ ' 14

15 Chapter III 3. Calbraton of the AJPES S.BON model and assgnment of credt ratng scores Dstncton between predctve power and model calbraton s needed. The model can have great predctve power, yet s uncalbrated. On the other hand, a model can be calbrated, yet carry low predctve power. A model s calbrated f the average sample predcted lkelhood of payment default for companes ncluded n the analyss equals the long-term rate of payment default for the populaton from whch the sample was selected. The goal s to create a model wth a large predctve power, meanng that t s able to dstngush between good and bad companes whle calbrated at the same tme. It s sgnfcantly easer to recalbrate an uncalbrated model wth hgh predctve power than mprove the predctve power of a weak but calbrated model. Basel standard requres banks to mplement a robust system for confrmng the accuracy of lkelhood of payment default assessments. A sgnfcant porton of ths confrmaton process nvolves checkng whether the average lkelhood of payment default accordng to credt ratng scores corresponds to the actual long-term rate of payment default. Ths s the so-called "level valdaton", whch s subject to the effects of specal data characterstcs e.g. that the data relates to a perod characterzed by a hgh correlaton of payment default events or that ths data does not refer to the entre macroeconomc cycle. By evaluatng the parameters of the multvarate sectoral submodels, avalable data can be used to assess the sample-dependent or uncalbrated lkelhood of payment default for any company. Ths allows ordnal rankng of companes dependng on ther assessed lkelhood of payment default. In the next step we calbrate the results wth the long-term practcally determned level of payment default, and fnally we also calbrate the results wth the credt ratng scale wth defned credt ratng scores. If the calculated probablty of a payment default event dverges sgnfcantly from the long-term average payment default rate, the model may be recalbrated n order for the calculated calbrated probablty of a payment default may better reflect actual payment default rates. The calbraton procedure nvolves the followng steps: - calculaton of average uncalbrated, sample-dependent probablty of payment default; - analyss of payment default rates for the Slovenan economy over a longer perod of tme and calculaton of long-term annual payment defeault rate averages; - calculaton of calbraton factors and ther applcaton to adjustment the uncalbrated sample-dependent condtonal probablty of payment default, resultng n calbrated payment default probabltes; 15

16 - determnng the need for model recalbraton n order for the calculated calbrated probablty of a payment default may better reflect actual payment default rates. For the purposes of calbratng the AJPES S.BON model we analyzed the changes n the rates of payment default n Slovena n the perod from 1994 to We analyzed the statstcal characterstcs of annual payment default rates, ther fluctuaton through the macroeconomc cycle and the calculated long-term average payment default rate Assgnment of credt ratng n relaton to the calculaton of calbrated lkelhood of payment default After calbraton we gan access to sample-uncondtonal or calbrated payment default lkelhoods for each ndvdual observaton. A number of credt ratng categores need to be defned n order to create a credt ratng scale system, wth correspondng threshold values of payment default probablty, whch wll serve as a bass for assgnment of credt ratng scores n each ndvdual observaton. When reflectng the lkelhood of default onto credt ratng scores, we pursue the followng goals: - exstence of a suffcent number of credt ratng scores for the purposes of economc and regulatory applcaton (Basel II requrements); - dstrbuton of credt ratng scores across credt ratng categores resembles normal dstrbuton; - none of the credt ratng categores may nclude an excessve number of observaton; - credt ratng categores are created n such a manner that the payment default rate for the ndvdual credt ratng category s constantly ncreasng from the poorest to the best credt ratng category; - the credt ratng system must present a suffcent ncrease n the lkelhood of payment default when passng from better to poorer credt ratng scores, meanng there are no excessve gaps n the lkelhood of payment default between two adjacent credt ratng categores. Accordng to Basel II the lkelhood of payment default may be classfed nto a maxmum of 20 credt ratng categores. Assgnng a lkelhood of payment default to the credt ratng scores s key to satsfyng the mnmum requrements for the IRB approach under Basel II and the EU Drectve. In order to satsfy these requrements, the credt ratng scale must contan at least seven credt ratng categores for payers and one credt ratng category for non-payers, a total of eght credt ratng categores. 16

17 The AJPES S.BON credt ratng model sorts the payees nto 10 credt ratng categores accordng to ther calculated lkelhood of payment default. Based on the calculated thresholds we assgned ndvdual companes credt ratng scores dependng on ther calculated or calbrated sample-ndependent lkelhood of payment default. The AJPES S.BON model credt ratng scale ncludes 10 credt ratng scores for payers and one credt ratng category for non-payers,.e. companes n whch a payment default event has actually occurred. The credt ratng scores for payers comprse SB1, SB2, SB3, SB4, SB5, SB6, SB7, SB8, SB9 and SB Companes n whch a payment default event has actually occurred are assgned the credt ratng score of SB10d. SB1 s the best credt ratng score on the credt ratng scale, and SB10 s the poorest on the credt ratng scale Credt ratng score descrptons The credt ratng scores are defned on a scale of probablty that a payment default event wll occur n a specfc case n the 12-month perod followng the date of the relevant fnancal statements upon whch the credt ratng score s based. The probablty of the occurrence of a potental payment default event s lowest wth the credt ratng of SB1, ncreasng exponentally as we move towards the credt ratng of SB10. The credt ratng score of SB10d s assgned to companes n whch a payment default event has actually occurred. The average probabltes of payment default ncrease exponentally (not lnearly) as they move from the best credt ratng score of SB1 towards the poorest score of SB10. Despte the fact that more than one half of all Slovenan companes have a credt ratng of SB5 or better, the exponentally ncreasng probablty of payment default from one category to the next, the average lkelhood of payment default n the 7th credt ratng category (SB7) s roughly equal to the average lkelhood of payment default for all Slovenan companes. The average predcted lkelhood of payment default for the frst sx credt ratng scores (SB1, SB2, SB3, SB4, SB5, SB6) s thus lower than the overall average lkelhood of payment default calculated for all Slovenan companes. The average lkelhood of payment default n the seventh credt ratng category (SB7) s roughly equal to the predcted average lkelhood of payment default for all Slovenan companes. The average predcted lkelhood of payment default for the credt ratng scores SB8, SB9 and SB10 s sgnfcantly hgher than the overall average lkelhood of payment default antcpated for all Slovenan companes. Table: Credt ratng score descrptons 1 The "SB" desgnaton of credt ratng scores and the correspondng number of the credt ratng category derves from the umbrella name of the AJPES S.BON model methodology, and s an acronym for "Slovenan Credt Ratng" ("slovenska bonteta"). 17

18 Credt ratng score SB1 SB2 SB3 SB4 SB5 SB6 Descrpton SB1 s the hghest score on the credt ratng scale. A company whch receves ths ratng has the best ablty to settle ts labltes. The credt ratng score s determned dependng on ts fnancal standng and credtworthness. In companes wth a credt ratng score of SB1 the ndcators showng the rsk factors for occurrence of a payment default event suggest that the probablty of a payment default event occurrng, assessed by applyng the model, s at the lowest level. The company's ablty to settle ts oblgatons s very hgh. A company wth an SB2 score s only slghtly dfferent from an SB1-rated company n terms of credtworthness. Indcators showng the rsk factors for the occurrence of a payment default event suggest that the probablty of a payment default event occurrng, assessed by applyng the model, s very low, yet stll hgher than that n the frst credt ratng category. The company's ablty to settle ts oblgatons s hgh. In companes wth a credt ratng of SB3 ndcators showng the rsk factors for the occurrence of a payment default event suggest that the probablty of a payment default event occurrng, assessed by applyng a model, s low, yet stll hgher than that n the second credt ratng category. Compared to companes wth hgher credt ratngs, t s more senstve to adverse changes n the busness envronment. The company's ablty to settle ts oblgatons s stll hgh, but lower than companes n the thrd credt ratng category. Companes wth a credt ratng score of SB4 present ndcators showng the rsk factors for occurrence of a payment default event, whch suggest that the probablty of a payment default event occurrng, assessed by applyng the model, s stll low. Regardless, on average, the probablty of a payment default event n companes wth a SB4 ratng s hgher than wth those wth a SB3 credt ratng. The company's ablty to settle ts oblgatons s stll above average, but lower than companes n the fourth credt ratng category. Adverse changes n the busness envronment or other unexpected events (shocks) can brng the company n a poston where t wll be unable to settle ts oblgatons. Companes wth a credt ratng score of SB5 present ndcators showng the rsk factors for occurrence of a payment default event, whch suggest that the probablty of a payment default event occurrng, assessed by applyng the model, s lower than the overall average for Slovenan companes. The company s ablty to settle ts oblgatons s stll above average, however, due to exponental ncreasng of the probablty of payment 18

19 SB7 SB8 SB9 default, about 60% of all Slovenan companes are ascrbed a hgher credt ratng score. The company s stll able to settle ts oblgatons under normal market condtons, but s hghly senstve to changes n the busness envronment. Adverse changes n the macroeconomc envronment or n the ndustry can put the company n a poston where t wll be unable to settle ts oblgatons. Companes wth a credt ratng score of SB6 present ndcators showng the rsk factors for occurrence of a payment default event, whch suggest that the probablty of a payment default event occurrng, assessed by applyng the model, s stll lower than the overall average for Slovenan companes, however, exponental ncreasng of the probablty of payment default place t at a sgnfcantly hgher rsk than companes belongng to the ffth credt ratng category. The company s ablty to settle ts oblgatons s average, however, due to exponental ncreasng of the probablty of payment default, about 75% of all Slovenan companes are ascrbed a hgher credt ratng score. Companes wth a credt ratng score of SB7 present ndcators showng the rsk factors for occurrence of a payment default event, whch suggest that the probablty of a payment default event occurrng, assessed by applyng the model, does not devate sgnfcantly from the overall average for Slovenan companes. The company's busness performance and ts ablty to settle oblgatons depend sgnfcantly on favorable condtons n the macroeconomc envronment and the relevant ndustry, and the company can quckly fnd tself n trouble. The company's ablty to settle ts oblgatons s very low and s sgnfcantly dependent on the condtons n the busness envronment. Any adverse changes n the crcumstances are very lkely to lead to a payment default event. Companes wth a credt ratng score of SB8 present ndcators showng the rsk factors for occurrence of a payment default event, whch suggest that the probablty of a payment default event occurrng, assessed by applyng the model, s hgh and t s at a sgnfcantly hgher rsk than companes belongng to the seventh credt ratng category due to exponental ncreasng of the probablty of payment default. The company's ablty to settle ts oblgatons s very low. Companes wth a credt ratng score of SB9 present ndcators showng the rsk factors for occurrence of a payment default event, whch suggest that the probablty of a payment default event occurrng, assessed by applyng the model, s very hgh and t s at a sgnfcantly hgher rsk than companes belongng to the eghth credt ratng category due to exponental ncreasng of the probablty of payment default. In normal 19

20 crcumstances n a busness enlvenment a company wth a credt ratng of SB9 s barely able to settle ts oblgatons. The company's ablty to settle ts oblgatons s at the lowest level across all Slovenan companes. Companes wth a credt ratng score of SB10 present ndcators showng the rsk factors for occurrence of a payment default event, whch suggest that the probablty of a payment default event occurrng, assessed by applyng the model, s at the hghest SB10 level and t s at a crtcally hgher rsk than companes belongng to the nnth credt ratng category due to exponental ncreasng of the probablty of payment default. For companes wth a credt ratng score of SB10 there s the greatest probablty that the company wll be unable to settle one or more of ts oblgaton durng the 12-month perod followng the date of the fnancal statements. A credt ratng score of SB10d s assgned to companes n whch a SB10d payment default event has actually occurred,.e. bankruptcy, lqudaton or compulsory composton proceedngs. Source: own defntons Transton matrces A credt ratng score assgned to a company changes through tme. The change s the result of constant updatng of credt ratng scores, and the correspondng regulatory requrements. Basel II requres that the credt ratng requrements are updated at least once per year, and more often f events occur from whch we can reasonably assume the credt rsk has ncreased. Ths mproves the dentfcaton of rsk, and helps us test the valdty of credt ratng models. One annual transfer matrx s created by dentfyng the credt ratng scores assgned to all companes subject to assessment over a 12-month perod. All changes of credt ratng scores durng ths perod are counted, provdng absolute frequences of transton. The transton matrces are specfc to the ndvdual credt ratng model and reflect the lkelhood of transton from an exstng credt ratng score (presented n columns) nto other credt ratng scores (presented n rows) n a gven tme perod. Due to the characterstcs of the constructon of the transton matrx observatons are concentrated along the dagonal (unchanged credt ratng), then the densty of observaton decreases as the dstance from the dagonal ncreases. The concentraton power along the 20

21 dagonal also depends on the number of credt ratng categores formed and the stablty of the reflecton onto the credt ratng scale. The more credt ratng scores exst on the credt ratng scale, the hgher the number of transton. 21

22 Chapter IV 4. Model valdty testng Model valdty testng must nvolve the montorng of predctve power and model stablty, analyses of model correlatons and testng the results predcted by the model compared to the actual results n terms of occurrence of a payment default event. The Basel II approach requres that the model valdty testng process s descrbed n the documentaton accompanyng the credt ratng model. Ths explct requrement ndcates the mportance of model valdty testng at the model development level. Testng must also nclude testng outsde the observaton sample, as well as testng outsde the tme of observaton, ndcatng the qualty of the model usng unknown data. In statstcal models quanttatve testng represents a part of model development. Regardless, statstcal credt ratng models requre use of data obtaned through practcal use of the model n order to perform quanttatve model testng. benchmark data may be used as a substtute. Ths especally apples when the same sample s used to check the qualty of a large number of models. The key crtera whch need to be checked n quanttatve model valdty testng nclude: - the model's dscrmnatory power, - accuracy of model calbraton and - stablty of the model outsde the sample and tme of observaton. The dscrmnatory power of the model means ts ablty to dstngush, on an ex-ante bass, between companes where a payment default event wll occur n a gven tme horzon and companes where the payment default event wll not occur. Ths s the so-called qualty of classfcaton. Model valdty testng must also be performed on an ndependent database,.e. outsde the sample and tme of observaton. Otherwse over-fttng can occur on the exstng data sample, resultng n poor dstngushng power nsde the observaton sample. In other words, ths means that the credt ratng model has low stablty. A stable credt ratng model s characterzed by a good correlaton between credt ratng rsk and ndvdual rsk factors, even wthn the development sample, meanng that the found correlaton s not merely the result of the selected data sample. Ths correlaton and consequently the qualty of the model s preserved through tme, as well. Calbraton qualty depends on the (ds)smlarty of calbrated lkelhoods of payment default wth actual rates of default observed n practce. Verfyng the calbraton of the credt ratng model s often called "back-testng". 22

23 Chapter V 5. Updatng of credt ratng scores 5.1. Credt ratng score update due to the ocurrence of a payment default event Credt ratng scores are determned once per year based on submtted annual fnancal statements. Credt ratng scores based on 2010 fnancal statements nclude a calculaton of lkelhood of ndvdual companes ncurrng a payment default event wthn a year (.e. n 2011). Even after assgnng annual model credt ratng scores usng the AJPES S.BON model, we can keep track of payment default n companes on an ongong bass. Model assessments obtaned on the bass of fnancal statements are therefore constantly updated based on actual data upon occurrence of a payment default event. If a company was assgned a credt ratng score based on ts 2010 fnancal statements (e.g. SB9), then actually became a non-payer on a gven date n 2011, the credt ratng score s updated from SB9 to SB10d on that date Credt ratng score updated due to deteroraton of the company s short-term solvency In order to ensure that credt ratng scores reflect as much as possble all avalable nformaton about the company s ongong busness operatons whch we can use to determne ts shortterm solvency, the credt ratng scores ascrbed on the bass of the company s annual report are also updated md-year, or after beng ascrbed based on the annual report. Md-year updates of credt ratng scores are based on nformaton about transacton account suspensons and court announcements for the relevant company and ts assocated companes, or for the group. 23