Rating systems for legal entities-comparative approach and improvement-

Coordinator:Conf. Univ. Dr. Laura Obreja BrasoveanuMasterand:David [Bruteanu] Violeta Monica

Key statements in describing the client rating:

satisfies internal and external information requirements;

it is a basic source of information of thorough credit approval process based on which it provides support for the preparation of the decision, allowing the measurement of credit risk and its mitigation on a long term basis.

Rating models (1) Altman Model (1968). The Z-Score -measures the probability of a

company entering bankruptcy within the next two years.Z = 0.012X1 + 0.014X2 + 0.033X3 + 0.006X4 +0.999X5

J. Conan - M. Holder Model (1978) -This model can be used for companies with 10 to 500 employees and it is based on liquidity-exigibilityanalysis. The model was developed by observation of 190 small and medium enterprises, half of witch were bankrupted between 1970 and 1975.

Z = 0,24X1 + 0,22X2 + 0,16X3 - 0,87X4 - 0,10X5

Springate (CANADIAN - 1978) -Springate used multiple discriminate analysis to select four out of 19 popular financial ratios that best distinguished between sound business and those that actually failed, using a sample of 20 failed companies matched with 20 non-failed companies.

Z = 1.03A + 3.07B + 0.66C + 0.4D

Rating models (2) Altman and Levallee(1980) -The sample consisted of 27 bankrupt and

27 non-bankrupt firms where 16 firms in each set were from the manufacturing sector and 11 were from the retail and service sector.

Z = -1.626 + 0.234 X1 - 0.531 X2 + 1.002 X3 + 0.972 X4+ 0.612 X5

Fulmer Model (U.S. - 1984) - It uses nine variables and a two-level system for estimations of the company’s status for prediction of bankruptcy. The two levels are “Bankrupt” and “No Bankrupt

H = 5.528 (V1) + 0.212 (V2) + 0.073 (V3)+ 1.270 (V4) - 0.120 (V5) + 2.335 (V6)+ 0.575 (V7) + 1.083 (V8) + 0.894 (V9)- 6.075

Anghel Model -The model used data between 1994 and 1998 and an initial number of 20 financial indicators.

A=5,676+6,3718X1+5,3932X2-5,1427X3-0,0105X4

The grounding of a rating system for corporate business clients

Credit analysis consists of: Quantitative and Qualitative analysis of a company,

identifying the elements that contribute to the establishing of a company’s capacity to honor its debt.

Qualitative and quantitative rating Company management

and shareholders Industry-specific risks Relationship with

partners and state budget

Competitiveness International exposure Risk factors of the

activity sector Credit history

Profit&Loss Analysis Balance Sheet Analysis Collateral analysis and

evaluation

Case Study – Rating system evaluation

Every rating model starts from a definition of failure which defines two sub-samples of firms: performing and non-performing firms. We will use one or several ratios commonly used to evaluate firm situation to discriminate between both groups. In the case study I used nine quantitative factors, those used in the analyzed bank, to which I added three more:

Sales variation (Turnover trend), Interest coverage and

Rate of non-paying customers.

The evaluation of the scoring system regarding the classification of clients according to their financial performance.

Factor analysis Cluster analysis

Factor analysis – Principal components analysis (1) I used Factor analysis to uncover the latent structure

(dimensions) of the set of variables selected for the rating system analyzed. Because there are different methods of extracting the factors from a set of data and the method chosen matters more to the extent that the sample is small, the variables are few, and/or the communality estimates of the variables differ, I choose Principal components analysis (PCA) which is by far the most common form of factor analysis.

I identified the first 6 main factors that explain a percentage of 78,977% of the total variation, the conclusion being that we can use them without losing too much information.

Factor analysis – Principal components analysis (2)

Factor analysis – Principal components analysis (3) the information contained by the first factors explains a larger part of the

total data variance (main factors).

Scree Plot

Component Number

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Factor analysis – Principal components analysis (4)Using Rotated Component Matrix we identified strong correlations between: Factor 1 and Company management and shareholders, Industry-specific

risks, Relationship with partners and state budget, Competitiveness, Risk factors of the activity sector;

Factor 2 and FX risk, Coverage of direct cash costs, Financing rate of current activity, Gross profitability;

Factor 3 and Solvency, Interest coverage, Debt service ratio, Coverage ofdirect cash costs;

Factor 4 and Rate of non-paying customers; Factor 5 and Current Liquidity; Factor 6 and International exposure and Coverage rate of raw materials

cost.That means that the abstract factors can be explained by the aggregate

variables with who are correlated.

Cluster analysis (1)

also called segmentation analysis or taxonomy analysis, seeks to identify homogeneous subgroups of cases in a population (a set of groups which both minimize within-group variation and maximize between-group variation).

I calculated the Proximity matrix (Annex 4- Proximity matrix). Each element of this matrix represents the Euclidean distance between the companies, calculated based on the 18 variables involved.

Cluster analysis (2) Applying Statistica cluster procedure we obtain the following representations of the 27 companies grouped by the variables (Complete Linkage Method)

Tree Diagram for 27 CasesCo mplete Linkage

Euclidean dis tances

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Cluster analysis (3)The Linkage distances are presented below:

Plot of Linkage Distances across StepsEuclidean distances

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Cluster analysis (4)Clients that remained in the same financial performance after the new rating

Cluster analysis (4)Clients that had changes in their financial performance after the new rating

Cluster analysis for the variables (1)Tree Diagram for 18 Variables

Single LinkageEuclidean distances

Var14Var15

Var17Var11

Var12Var10

Var9Var8

Var7Var18

Var16Var13

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Cluster analysis for the variables (2)

Using this classification method we can state that the financial indicators used to rate the 27 companies can be grouped in 3 classes (you can find in Annex no. 7 the indicators with their corresponding number):

the class formed by indicators 1,2,3,4,5,6,13,16,18 the class formed by indicators 7,8,9,10,11,12,15,17 the class formed by indicator 14 (Rate of non-paying

customers- indicator that we added to the existing rating);

Conclusions the rating system used by the bank is a valid one and is in compliance with

the current NBR regulations, with peculiar attention to the future development of their rating system, the recommendation being to use more relevant financial indicators in the quantitative rating and instruct their credit analysts to conduct an objective qualitative rating (considering that is a possibility of diminishing the clients’ financial performances if the qualitative rate is less than 3).

in order to use the available data in the most efficient way, each player (from central banks to commercial banks or to international rating agencies) tries to take into account the proprietary information to design its rating scale and, sometimes, its definition of default.

we have to keep in mind that the financial performance of the company, besides being the strongest point in assessing the credit risk (along with collaterals and the complexity and grounding of the financed project), is a criteria used in establishing provision expenses (along with debt service and the starting of legal procedures it serves at loan classification for provisioning purposes).

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