The Role of Accounting Determinants in Predicting Long Term Credit Ratings

Gorana Roje Research Assistant

Institute of Economics, ZagrebCroatia

groje@eizg.hr

AbstractThis paper tests the model of accounting determinants that are anticipated to predict S&P long-term credit ratings. Specifically, it examines the relative importance of various accounting variables in simplifying the process of determining credit ratings. Further, the study explores if various accounting determinants, that prove to make impact on predicting long term credit ratings, vary across industries. The model appears to be suitable for predicting the long-term S&P credit ratings. Further still, the model is additionally simplified. The author tests the hypothesis that although the model is additionally simplified, the predictions for long term credit ratings remain almost as good as for the original model with a very small loss in model explanatory power. Specifically, the study gains to distinguish sets of variables that proxy for some common factors with influence on credit ratings. The analysis of empirical data points out the distinctive differences between industries as far as the accounting determinants of credit ratings are concerned.

Key words: credit ratings, accounting determinants, simplified model, cross- industry disparityJEL classification: M 41

Acknowledgements

I am grateful to my numerous colleagues for worthy suggestions.

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Introduction

The goal of this paper is to investigate the relation between the accounting variables that can influence financial analysts in determining companies' credit worthiness, as well as their influence in predicting long term credit ratings. When assessing long term credit ratings, Standard and Poor(S&P) uses its own proprietary system. The information S&P uses to determine rankings of companies’ financial strength is obtained from a combination of both public sources (e.g., the annual report and accounts) and private information (e.g., managerial statements). Cantor and Packer (1995) show that credit rating agencies often use both quantitative and qualitative information when formulating their rating of a company’s financial condition.

This paper, however, does not analyse the way credit rating firms formulate credit ratings, but tries to determine whether the process of assessing creditworthiness by reducing the number of accounting variables and further more by distinguishing sets of variables, as factors, can be somewhat simplified.

Overall, the paper seeks to gain a better understanding of the relationship between accounting variables and credit ratings. Specifically, the study distinguishes sets of variables that proxy for some common factors, which further on influence credit ratings. By distinguishing sets of variables, the model is additionally simplified with a small loss in explanatory power. Finally, the study shows how various credit determinants differ across industries.

In the first part of the paper a more detailed description of data is given. Research questions and appropriate research techniques are developed in the second section, while data analysis and results are provided in the third part of this paper.

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1. Data sample

The sample consists of firms from Compustat database1. Industrial Annual file, containing annual accounting data, was used. The initial sample consists of all firms that have credit ratings in years 1998-2002. There are 10,940 such firm-year observations. The sample is reduced due to the availability of the accounting data on such firms needed for further analysis. Consequently, 58.4% of observations were lost and the remaining final sample resulted in 4,550 firm-year observations, around 900 per year.

The financial institutions are excluded because the structure of their financial statements is very different from the financial statements'

structure of firms in a non banking sector. Consequently the accounting determinants that are considered to be important in a non banking sector are not the same as the accounting variables common for firms in the data sample of this paper.

Description of variables

The dependent variable in the model is an S&P long-term credit rating, assigned to each company in a particular year. The credit ratings range from AAA to D. There were 22 categories which were initially reduced to 8. However, since the highest and lowest categories included a low number of observations, they were merged with the nearest categories. Consequently, the study compiles 6 credit ratings ranging from 2 to 7, where 2 is the highest rating and 7 is the lowest rating. The list of ratings is as follows:

2 (AAA, AA+, AA, AA-) – indicating categories that consist of firms with very strong capacity to pay interest and repay the principal,

3 (A+, A, A-) – referring to firms that indicate a strong capacity to pay interest and repay the principal. However, these companies are somewhat susceptible to adverse changes in particular circumstances and economic conditions,

1 (http://wrds.wharton.upenn.edu)

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4 (BBB+, BBB, BBB-) – indicating firms with an adequate capacity to pay interest and repay the principal. Still, adverse economic conditions or changing circumstances are more likely to lead to a weakened capacity to meet payments than in a case of firms of higher ratings,

5 (BB+, BB, BB-) – representing a credit rating that indicates less near-term vulnerability to default than other speculative issues. However, firms with such credit rating face major ongoing uncertainties or exposure to adverse, financial or economic conditions that could lead to inadequate capacity to meet timely interest and principal payments,

6 (B+, B, B-) – indicating firms with greater vulnerability to default but still with current capacity to meet interest payments and principal payments. Adverse financial or economic conditions will likely impair capability or willingness to meet these payments.

7 (CCC+, CCC, CCC-, CC, C, D) – indicating firms with an identifiable current vulnerability to default. These firms are dependant upon favourable conditions to meet timely interest payments and repayment of principal. In the event of adverse conditions these firms are not likely to have the capacity to pay the interest or principal. D stands for firms with payments in default.

The frequencies for the credit rating are given in Table 1.Table 1. Credit rating’s frequencies

Ordered value Credit rating Total Frequency1 7 872 6 7913 5 10554 4 13395 3 10066 2 272

When checking for the normality, some of the independent variables exhibited quite big departures from normality2. Therefore, all the variables

2 Only the most important tables and graphs are included in the paper. The transformation into the deciles that resulted in satisfying the normality condition was conducted as a remedy for the departures from normality, but not considered that important to be provided in the paper.

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were transformed into deciles in order to achieve normal distribution. This reduced the possible influence of outliers without deleting observations. This procedure is also motivated by the fact that credit analysts do not only look at the absolute values of ratios but rather at the relative values when comparing several companies. The fact that one company has 20 times higher sales growth ratio than another could indicate that it is a start-up company or company that merged with another. However this ratio, used as an absolute measure, could have unreasonably high impact on the results and make more difficult to uncover the certain relations such as the relation between sales growth and credit worthiness. Furthermore, it is easier to compare and interpret the results when the independent variables have a common scale.

The independent variables and author's predictions concerning their influence on S&P long-term credit ratings are as follows3:

1. ROA= Net Income before Extraordinary Items/ Total Assets2. ROE= Net Income before Extraordinary Items / Book Value of Equity;3. PROFIT= Net Income before Extraordinary Items / SalesThe inclusion of the profitability ratios (ROA, ROE, PROFIT) was necessary since they indicate the financial strength of the company and as such they influence the credit rating. They should be quite highly correlated since they have the same nominator. That is why the logistic analysis is run both with profitability ratios as separate independent variables and with an aggregate profitability ratio expressed as the average of ranked profitability ratios listed above.4. Market value of equity = Number of common shares outstanding *share priceMarket value of equity generally implies the size of the company. Size should reduce the company risk. Bigger companies can be more diversified and this reduces the risk when certain business segments face adverse market conditions. Besides, bigger companies have more market

3 The choice of independent variables expresses which accounting ratios author considers important in influencing credit ratings. The author

does not find these determinants to be the only ones that can be used in predicting long term credit ratings. However , this study is concentrated on these 11 accounting ratios. Variables ROA, ROE, PROFIT, market value of equity, LEVERAGE, LTDEBT and CURRENT RATIO have been emphasised as important accounting determinants of financial statements analysis in literature: Spajic, Ferdo; Danimir Gulin, Silvije Orsag, Vesna Vasicek,  Lajos zager, Slavko Leko, Ivanka Avelini, Holjevac, Josipa  Mrca : “Analysis of Financial Reports” /Analiza financijskih izvjestaja/, Hrvatska zajednica računovođa i financijskih djelatnika, Zagreb, 1994; Žager Katarina, Žager Lajoš : “Analiza financijskih izvještaja”, Masmedia, Zagreb, 2001.

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power and more opportunities to secure additional financing when needed. Higher market value of equity should increase credit rating.5. BV_TANG = Tangible Book Value / AssetsTangible Book Value4 represents a sort of a security for the bondholders, in case of liquidation. The intangible assets are excluded due to the uncertainty of future benefits that may be worthless in the case of bankruptcy, e.g. goodwill. Study predicts that the higher ratio reflects more equity based on tangible assets and more security and better ratings.6. LEVERAGE= Total Liabilities / Total AssetsLeverage addresses the proportion of assets financed by liabilities. Higher ratio means higher indebtedness, so the company becomes more risky and the creditors are less secured by equity. On the other hand the higher ratio can mean that the company is doing well and is able to secure more financing as well as to use this leverage for faster growth. Nevertheless, when comparing two otherwise identical companies the analyst would assign higher credit ratings to the one with lower leverage.7. LTDEBT= Long Term Debt / Total AssetsLong-term debt ratio represents the part of total assets financed by the long term portion of liabilities. The prediction is similar to the one for leverage.8. CURRENT RATIO= Current Assets / Current LiabilitiesCurrent ratio shows the short-term liquidity. In general a higher ratio indicates that the firm has a higher ability to cover the short-term liabilities with current assets that can be transformed into money much faster than long-term assets. However, this ratio is very different in different industries and the lower ratio could indicate, for example, that the company has a stable and financially strong position and can base more of its financing on cheaper short-term financing rather than on long-term financing. This study does not have a strong prediction for this ratio. 9. PEN = (Projected Benefit Obligation-Pension Plan Assets)/ Total AssetsThe accounting treatment of defined benefit pension plans makes pension liability an off-balance sheet item which is not included in debt or assets. The higher liability means that there is additional liability that will have to be covered in the future. If negative, this ratio indicates that company has

4 Tangible book value: Book value minus intangible assets.

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an additional asset and it will have to put less money in the pension plan in the future. Thus, the higher ratio should refer worse credit ratings.10. PRSTDS= Standard deviation of earnings / Total assetsVolatility of earnings is calculated as a standard deviation of net income before extraordinary items for the last 4 years prior to assessment of credit rating divided by total assets. It should have negative impact on credit ratings since the changes in earnings indicate more risk and anticipate the danger that in case of adverse conditions it could lead to default in the debt or even bankruptcy.11. SGROWTH = ((S3-S4)/S4+ (S2-S3)/S3+ (S1-S2)/S2)/3;Growth in sales is calculated as the arithmetic average of growth in sales over the last 3 years prior to assigning credit ratings. The sales growth was introduced in the model to see how analysts perceive the sales growth. At this point no strong predictions concerning the influence of this variable can be stated. The higher sales growth could indicate favourable conditions of the company and industry. Still, fast growth may relate to aggressive growth and risky projects being undertaken by the management. 12. DUMMY VARIABLESYear dummy variables were included (1998-2001) in order to show if the overall economic conditions influence the credit ratings. Dummy variables for industries were also included to explain whether certain industries are more or less affected by some other factors that are not accounting related5.

2. Research Questions and TechniquesIn an effort of constructing a model for predicting credit ratings by reducing the number of accounting determinants used by S&P, the following research questions are addressed:

1. How well would this model predict long-term credit ratings? 2. How various accounting variables weight in relative importance of

their impact on credit rating?3. Can the model be further simplified by distinguishing sets of

variables that proxy for some common factors that make influence on credit ratings?

5 The way analysts perceive the risk of particular industries.

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4. How do various credit determinants differ across industries? Referring to the first research question, this paper tests the following hypothesis:A simple accounting model built in this paper, by reducing the number of accounting determinants used by S&P, is to yield good predictions for long terms credit ratings.

Referring to the third research question, this paper tests the following hypothesis:The model further simplified by extracting factors, will yield almost as good predictions for long term credit ratings as the original model addressed in the first research question does and will not result in significant loss of the explanatory power.

Since the dependent variable of the model consists of several ordered categories (from 2 to 7) this study uses logistic model. The OLS was not appropriate because this model does not contain categorical variable. The dependent variable, Credit Rating has six ordered values. Also, the distance between two values is not equal to the distance between other two categories, and that does not satisfy the assumption for the OLS regression to be applied. The distribution of the dependent variable is not continuous but discrete.Main model to be used is ordered logistic regression. This model addresses the first two research questions. The third research question will be addressed by conducting factor analysis. ANOVA analysis is a technique used in providing the results for the last research question.

3. Data Analysis and Results8

Logistic procedure

In addressing the first research question and first hypothesis, this study uses Logistic procedure. The Probit procedure gives similar results, e.g. signs and significance of estimated coefficients6.The overall model is significant and all variables are significant at 5% level. The R-square from this logistic model is 0.577. This implies that the simple model explains 57 % of variation in credit ratings. When running the logistic model with profitability ratios separately, the leverage and ROE are not significant. This could result from multicollinearity since the correlation coefficients for profitability ratios range from 0.76 to 0.87. Multicollinearity check was performed by running OLS and the variance inflation factors (from here on VIF) were all below 10. However, the VIF related to profitability ratios are quite high, ranging from 4.94 to 8.768. The coefficients and significance levels are provided in Table 2. As the reader can notice, the coefficient on profitability is negative and this means that the higher profitability is associated with better credit rating, as expected. The size9 is also predictably negative and the magnitude of the coefficient indicates that it is a very important variable. The negative coefficient on the ratio Tangible Book Value to Total Assets10 indicates that the creditors evaluate a certain company as less risky if the Book Value is greater. However reader must keep in mind that the intangibles are not included here for the intangibles are not highly valued as security. Looking at table 2 in more detail, the positive sign on Leverage and Long-Term Debt indicate that the more debt as a percentage of Total Assets the company is carrying the lower credit ratings it receives. It turns out that greater Current ratio, as positive coefficient, is associated with lower rating. This is to some extend surprising because the higher Current ratio means that the company has more short-tem liquidity and this should limit the default and bankruptcy risk. However, as it is stated, there are other factors that could cause companies with higher credit ratings to have lower current ratio. In addition, volatility of Earnings11 suggests that the

6 The author does not find necessary to provide the results of the Probit analysis within the table. However if reader is interested in achieving more information, author would be more then pleased to provide the information.7 The results provided in appendix.8 The table of VIF results is provided in appendix.9 The size refers to the market value of equity. 10 Previously in the text expressed as BV_TANG.11 Previously in the text expressed as PRSTDS.

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higher volatility also has the predicted effect of lowering the credit ratings by increasing the perceived risk.The sales growth turned out to decrease credit ratings but the significance level is lower than for other variables. Also the magnitude of the coefficient is very small. In general the sales growth variable seems to be less important than other variable. This result can be explained by the fact that due to recent sales growth managers are eager to undertake certain risky investments just because they assume the sales growth to continue increasing. However, the analysts are aware of the risk that the sales growth can decline and that the over-invested company can face big problems in the future.The dummy variables for the four years from table 2 indicate that the credit ratings were better in 1998 and 1999 in comparison to 2002. It indicates that probably better general economic conditions in those years resulted in generally better ratings assigned by credit analysts. The credit ratings for 2001 were the worst, probably because this marked the start of the recession and the outlook for the future was quite bleak.

Table 2. Analysis of maximum likelihood estimates

Parameter DF12 Estimate Standard Error

Wald Chi-Square

Pr > Chi Sq

Intercept 7 1 -49.879 0.2409 4.285.622 <.0001Intercept 6 1 -16.514 0.2089 625.081 <.0001Intercept 5 1 0.3907 0.2067 35.728 0.0587Intercept 4 1 26.503 0.2111 1.576.713 <.0001Intercept 3 1 52.739 0.2247 5.509.564 <.0001PROF_ALL_D 1 -0.2504 0.0131 3.664.486 <.0001BV_TANG_D 1 -0.0836 0.0153 299.109 <.0001LEVERAGE_D 1 0.0450 0.0169 71.058 0.0077CURRENT_D 1 0.1410 0.0125 1.264.790 <.0001LTDEBT_D 1 0.1747 0.0146 1.429.384 <.0001MV_D 1 -0.4202 0.0138 9.213.639 <.0001SGROWTH_D 1 0.0235 0.0109 46.834 0.0305PRSTDS_D 1 0.0498 0.0115 187.442 <.0001PEN_D 1 0.0752 0.0106 499.558 <.0001Y1998 1 -0.2315 0.0959 58.210 0.0158Y1999 1 -0.3037 0.0904 112.899 0.0008Y2000 1 0.00593 0.0895 0.0044 0.9472Y2001 1 0.1881 0.0871 46.649 0.0308IND0: Agricultur. 1 0.9063 0.4513 40.333 0.0446IND1: Mining 1 0.8314 0.1235 453.056 <.0001IND2: Construct. 1 -0.1168 0.3720 0.0986 0.7535IND3: Transp/Uti 1 0.2265 0.0816 77.081 0.0055

12 DF indicates degree of freedom. Size reflects companies' market value(MV).

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IND4: Trade 1 0.3582 0.0934 147.042 0.0001IND5: Services 1 0.9630 0.1005 917.792 <.0001

In addition, further attempts were made in order to determine weather or not the credit ratings varied across industries. For that purpose the most aggregate classification level of industries: Agriculture, Mining, Construction, Manufacturing, Transportation and Utilities, Wholesale and Retail Trade, Services, was done. The sector omitted as an indicator variable is Manufacturing sector for this sector represented almost half of the observations. The results indicate that Agriculture, Mining and Service industries are in general perceived as much riskier than Manufacturing. Transportation and Trade also seem to receive lower ratings but the difference is of much lower magnitude than for the previous industries. Finally, Construction seems not to differ from Manufacturing (p-value = 0.75). When running the OLS, the results related to the signs of the coefficients and the levels of significance were very similar. The conclusions at this point would be the same if using OLS instead of logistic model.

Table 3. Odd ratio estimatesEffect Point estimate 95% Wald Confidence LimitsPROF_ALL_D 0.778 0.759 0.799BV_TANG_D 0.920 0.893 0.948LEVERAGE_D 1.046 1.012 1.081CURRENT_D 1.151 1.124 1.180LTDEBT_D 1.191 1.157 1.225MV_D 0.657 0.639 0.675SGROWTH_D 1.024 1.002 1.046PRSTDS_D 1.051 1.028 1.075PEN_D 1.078 1.056 1.101Y1998 0.793 0.657 0.957Y1999 0.738 0.618 0.881Y2000 1.006 0.844 1.199Y2001 1.207 1.018 1.431IND0 2.475 1.022 5.994IND1 2.297 1.803 2.926IND2 0.890 0.429 1.845IND3 1.254 1.069 1.472IND4 1.431 1.191 1.718IND5 2.619 2.151 3.190

The odd estimates in the table 3 indicate how the odds of a company being in a higher or lower credit rating category would change with the change in a given variable holding other variables constant. Some of the variables have much higher impact on the odds than others, so they are more important in determining credit ratings. For example, the reader can

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see that the odds of a company being assigned with worse credit rating decrease by 34.3% (1 - 0.657), if there is a change in MV variable by moving up one decile higher . While the change in profitability ratio decreases such odds by 22.2%, changes in Long-term debt ratio (LTDEBT) results in 19.1% increase in odds for company of falling in a worse rating category. Sales growth (SGROWH), Volatility of earnings (PRSTDS) and Leverage seem to be the least important variables increasing the odds only by 2.4%, 5.1%, and 4.6% respectively.

The association of predicted probabilities and observed responses is given in the table below.

Table 4. Predictive probabilities and observed responses

Association of Predicted Probabilities and Observed ResponsesPercent Concordant 69.0 Somers' D 0.598Percent Discordant 9.2 Gamma 0.766

In order to check how appropriate the model is in predicting ratings, cumulative probabilities of the individual firm for each of the credit rating category (predicted probabilities from proc logistic) are obtained. Subsequently, a decision was made to assign firm-year observation to a particular category based on the 0.5 rule. That means that the firm was assigned to a credit rating for which the cumulative probability of a firm belonging to this or a worse category exceeded 0.5 for the first time. The predictions for OLS regression were also calculated. The decision rule was to assign the firm with a credit rating which was closest to the predicted value. The following table (Table 5) shows the frequencies where the predicted ratings match the actual ratings.Table 5. Predicted rating by actual rating

Predicted Rating/Actual rating

2 3 4 5 6 7 Total

2Frequency 19 6 0 0 0 0 25Percent 0.42 0.13 0.00 0.00 0.00 0.00 0.55Row Pct 76.00 24.00 0.00 0.00 0.00 0.00

3Col Pct 6.99 0.60 0.00 0.00 0.00 0.00

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Frequency 217 580 284 52 14 4 1151Percent 4.77 12.75 6. 24 1.14 0.31 0.09 25.30Row Pct 18.85 50.39 24.67 4.52 1.22 0.35

4Col Pct 79.78 57.65 21.21 4.93 1.77 4..60Frequency 33 383 765 293 95 5 1574Percent 0.73 8.42 16.81 6.44 2.09 0.11 34.59Row Pct 2.10 24.33 48.60 18.61 6.04 0.32

5Col Pct 12.13 38.07 57.13 27.77 12.01 5.75Frequency 3 33 268 524 282 17 1127Percent 0.07 0.73 5.89 11.52 6.20 0.37 24.77Row Pct 0.27 2.93 23.78 46.50 25.02 1.51

6Col Pct 1.10 3..28 20.1 49.67 35.65 19.54Frequency 0 4 22 186 400 61 673Percent 0.00 0.09 0.48 4.09 8.79 1.34 14.79Row Pct 0.00 0.59 3..27 27.64 59.44 9.06Col Pct 0.00 0.40 1.64 17.63 50.57 70.11

Total 272 1006 1339 1055 791 87 45505.98 22.11 29.43 23.19 17.38 1.91 100.00

Tables 6 and 7 show the percentages of the observations for which the model predicts perfectly and percentages that present departure of logistic model predictions from the actual credit rating by 1, 2, 3, 4, or 5 categories. The model predicts quite well with 50% of perfect matches and the number of bad predictions is very small since 93.8% of predictions do not miss the actual rating by more than one category. Surprisingly, the model without the industry dummy variables performs almost as well as the model that includes them despite the fact that the dummy variables seemed to be important determinants of credit ratings. Interestingly, OLS model which from the theory point of view is not the most correct model for this analysis, as stated in the beginning, gives predictions which are almost as good as the logistic model (see table 8).

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Factor analysis

This section addresses the third research question of this study and second hypothesis. Prior to conducting factor analysis the correlation matrix was examined to assess the appropriateness of the data. It appeared that there were a substantial number of correlations above 0.3, leading to the belief that factor analysis would be appropriate. Several different extraction methods, such as Principal method and Prinit method, were used and they yielded very similar results in terms of eigenvalues, suggesting proceeding with 4 or 5 factors. Finally Image method was used because it reduced the number of factors and made their interpretation more reasonable. This method suggested three factors even though the

Table 6. Prediction Accuracy from the logistic model with industry indicator variables.

 Percentage of Observations

Cumulative Percentage

PerfectMatch 50.29% 50.29%Missed by 1 43.52% 93.80%Missed by 2 5.54% 99.34%Missed by 3 0.57% 99.91%Missed by 4 0.09% 100.00%Missed by 5 0.00% 100.00%

Table 7. Prediction Accuracy from the logistic model without industry indicator variables.

 Percentage of Observations

Cumulative Percentage

Perfect Match 49.45% 49.45%Missed by 1 43.96% 93.41%Missed by 2 5.87% 99.27%Missed by 3 0.64% 99.91%Missed by 4 0.09% 100.00%Missed by 5 0.00% 100.00%

Table 8. Prediction Accuracy from the OLS model with industry indicator variables.

 Percentage of Observations

Cumulative Percentage

PerfectMatch 49.38% 49.38%Missed by 1 44.44% 93.82%Missed by 2 5.63% 99.45%Missed by 3 0.51% 99.96%Missed by 4 0.04% 100.00%Missed by 5 0.00% 100.00%

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eigenvalues are greater than 1 only for the first two factors. The scree plot using the image method is shown by graph 2.

Graph 2 . S cree P lot

0

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The scree plot suggested 3 factors to be retained. The Varimax rotated pattern of factor loadings, suppressing values below 0.4, is presented in Table 9. These factors are the following:

1. Profitability factorThree accounting profitability ratios (ROA, ROE, and Profit Margin) load highly on this factor. It represents the overall profitability of the company operations using different metrics. Higher profitability should lead to better credit ratings. Cronbach Alpha indicates a very high conformity between these variables (0.93).

2. Debt factorThis factor yields high positive loadings of Leverage, Long-Term Debt to Total Assets and high negative loading of Tangible Book Value to Total Assets. This last variable acts as a reversed leverage but after subtracting the intangible assets that are probably not valued as by bond holders as highly as tangible assets. This factor should lower the credit ratings since more debt in comparison to assets means less security for those who extend credit in case of financial problems. Cronbach Alpha also indicates high level of agreement (0.82).

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3. Size/Volatility factorThis factor consolidates four variables that are less reliable than the previous ones (Cronbach Alpha 0.39). The inverse relation between the Current ratio and Market Value (MV) suggests that smaller firms have to maintain higher short-term liquidity (higher current assets in relation to current liabilities). Smaller firms seem to have more volatile earnings; one of the reasons can be that they are less diversified than big firms or that they have less market power. So, the higher factor in general means more volatility and smaller size. This should lead to lower earnings.

Table 9. Rotated Factor Pattern

Variables Factor1:Factor2:Debt

Factor3:Size/Volatillity

Profitability

ROA_D 0.96063 . .ROE_D 0.92127 . .PROFIT_D 0.85628 . .LEVERAGE_D . 0.93620 .LTDEBT_D . 0.74285 .BV_TANG_D . -0.81952 .CURRENT_D . . 0.60890PRSTDS_D . . 0.41825PEN_D . . .SGROWTH_D . . -0.48184MV_D . . -0.58763

Cronbach Alpha( variables with loading above 0.4) 0.9346 0.8224 0.3866

Values less than 0.4 are not printed.

Table 10. Variance explained by extracted factors  Factor1 Factor2 Factor3Eigenvalues 2.831 2.438 1.346As percent of Total 25.7% 22.2% 12.2%Cumulative percent 25.7% 47.9% 60.1%

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Table 10 implies that all three factors combined explain 60.1% of variance. After performing the factor analysis, the logistic regression on these three factors using factor scores, obtained from SAS Proc Factor, was run.

The results indicate that predictions are correct and that the factors seem to summarize the underlying concepts well as indicated in the table 11.

Table 11. Logistic regression on the three extracted factorsAnalysis of Maximum Likelihood Estimates

Parameter DFEstimate

Standard error

Wald Chi-Sq

Pr > ChiSq

Intercept 7 1 -59.717 0.1455 16.851.333 <.0001Intercept 6 1 -26.129 0.0827 9.978.319 <.0001Intercept 5 1 -0.6943 0.0723 922.629 <.0001Intercept 4 1 13.472 0.0745 3.273.545 <.0001Intercept 3 1 37.631 0.0957 15.447.132 <.0001Factor1 :Profitability

1 -11.153 0.0324 11.852.353 <.0001

Factor2: Debt 1 10.717 0.0321 11.127.748 <.0001Factor3: Size/Volatillity

1 11.426 0.0334 11.711.580 <.0001

Y1998 1 -0.2366 0.0915 66.854 0.0097Y1999 1 -0.2970 0.0882 113.284 0.0008Y2000 1 -0.0177 0.0860 0.0425 0.8367Y2001 1 0.2409 0.0848 80.651 0.0045IND0: Agriculture 1 0.8342 0.4460 34.981 0.0614IND1: Mining 1 10.048 0.1169 738.505 <.0001IND2: Construction

1 0.3605 0.3665 0.9678 0.3252

IND3: Transp/Utilities

1 0.2368 0.0734 104.149 0.0013

IND4: Trade 1 0.3457 0.0916 142.542 0.0002IND5: Services 1 0.9891 0.0977 1.024.410 <.0001

The effectiveness of the predictive ability of the model based on the extracted factors is also shown in the table 12. When comparing this table with tables 6-8 the reader can notice that the percentage of observation

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with correct predictions did not change much (e.g. perfect match 47.32% vs. 50.29% for the full model).

Table 12. Accuracy prediction

 Percentage of Observations

Cumulative Percentage

Perfect Match 47.32% 47.32%Missed by 1 45.16% 92.48%Missed by 2 6.64% 99.12%Missed by 3 0.79% 99.91%Missed by 4 0.09% 100.00%Missed by 5 0.00% 100.00%

ANOVA analysis

In order to explore the differences in credit determinants across industries and in that way answer the last research question of this paper, ANOVA analysis was conducted. Factor scores are calculated in the factor analysis as summary measures of credit determinants, and are shown by the following line chart.

G r a p h 3 . M e an Industr Factor S coresy

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P rofitab ility D e bt S ize /Vo la tility

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The graph shows that the patterns of the different determinants of credit rating are very different across industries. For example, agriculture seems to be high on all factors and Construction seems to be quite low on all

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factors. Other category seems to display such pattern but the evidence does not imply the reasons behind this phenomenon. However since the factors have different impact, shown by different signs of the coefficient estimates, on credit ratings and there are also industry specific perceptions independent of current accounting numbers one can not know a priori which industry would tend to have lower credit ratings. The graph below shows that Agriculture has in fact worse creditworthiness assessments than Construction industry.

G r a p h 4 . M ean S Indus tr&P Long-Te rm Credit Ra ting for y

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Transport/Utilities seem to be more leveraged than some other industries but have lower volatility/bigger size. The inverse pattern is displayed by Wholesale/Retail trade that shows higher volatility/smaller size and much less debt.

4. Conclusion

The paper presents a model based on accounting variables that were anticipated to predict S&P long-term credit ratings quite well. The model constructed shows the relative importance of different financial ratios in determining credit ratings. Second scope of the paper was to further simplify the constructed model by extracting factors considered as the most important for credit analysts in their credit assessment decisions.

19

Constructing such simplified model resulted in a very small loss in model explanatory power and distinguished three factors: Profitability, Debt, and Size/Volatility. The result gained confirms the hypothesis that although the model is additionally simplified, the predictions for long term credit ratings remain almost as good as for the original model with a very small loss in model explanatory power. Finally, some interesting observations were made concerning different patterns of these determinants across industries that differ a lot in terms of operational and financial characteristics. The results were as predicted: the patterns of credit rating determinants differ across industries. Overall this paper provides a better understanding of the relationship between accounting variables and credit ratings. Applying such simplified model to the data sample consisting of Croatian firms is however left for future research.

Literature

Cantor, R. and F. Packer (1995): “The Credit Rating Industry”, The Journal of Fixed Income Vol. 5 (December), 10-34.

Compustat data base (http://wrds.wharton.upenn.edu).

Delwiche Lora D. and Slaughter Susan J. (1998): "The Little SAS Book: A Primer", Second Edition, SAS Publishing.

Hair Joseph F., Tatham Ronald L., Anderson Rolph E. (1998): "Multivariate Data Analysis" (5th Edition), William Black Prentice Hall; 5th edition.

Kutner Michael H., Nachtschiem Christopher J., Wasserman William, and Neter John (1996): "Applied Linear Statistical Models", McGraw-Hill/Irwin; 4th edition.

Spajic, Ferdo; Danimir Gulin, Silvije Orsag, Vesna Vasicek,  Lajos zager, Slavko Leko, Ivanka Avelini, Holjevac, Josipa  Mrca (1994) : “Analysis of Financial Reports” /Analiza financijskih izvjestaja/, Hrvatska zajednica računovođa i financijskih djelatnika, Zagreb

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Tabachnick Barbara and Fidell Linda (2000): "Computer-Assisted Research Design and Analysis", Pearson Allyn & Bacon.

Žager Katarina, Žager Lajoš (2001): “Analiza financijskih izvještaja”, Masmedia, Zagreb

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Appendix

1. Logistic regression: Odds ratio estimates

Odds Ratio Estimates

EffectPoint

Estimate95% Wald

Confidence Limits

PROF_ALL_D 0.778 0.759 0.799

BV_TANG_D 0.920 0.893 0.948

LEVERAGE_D 1.046 1.012 1.081

CURRENT_D 1.151 1.124 1.180

LTDEBT_D 1.191 1.157 1.225

MV_D 0.657 0.639 0.675

SGROWTH_D 1.024 1.002 1.046

PRSTDS_D 1.051 1.028 1.075

PEN_D 1.078 1.056 1.101

Y1998 0.793 0.657 0.957

Y1999 0.738 0.618 0.881

Y2000 1.006 0.844 1.199

Y2001 1.207 1.018 1.431

IND0 2.475 1.022 5.994

IND1 2.297 1.803 2.926

IND2 0.890 0.429 1.845

IND3 1.254 1.069 1.472

IND4 1.431 1.191 1.718

IND5 2.619 2.151 3.190

Testing Global Null Hypothesis: BETA=0

Test Chi-Square DF Pr > ChiSq

Likelihood Ratio 3854.7750 19 <.0001

Score 2601.8773 19 <.0001

Wald 2699.3732 19 <.0001

22

2. OLS results:

Analysis of Variance

Source DFSum of

SquaresMean

Square F Value Pr > F

Model 19 3811.93805 200.62832 315.06 <.0001

Error 4530 2884.69843 0.63680

Corrected Total 4549 6696.63648

Root MSE 0.79800 R-Square 0.5692

Dependent Mean 4.29626 Adj R-Sq 0.5674

Coeff Var 18.57420

Parameter Estimates

Variable Label DFParameter

EstimateStandard

Error t Value Pr > |t|VarianceInflation

Intercept Intercept 1 4.71095 0.08698 54.16 <.0001 0

PROF_ALL_D

1 -0.10399 0.00533 -19.53 <.0001 1.43678

BV_TANG_D Rank for Variable BV_TANG 1 -0.03528 0.00646 -5.46 <.0001 2.41408

LEVERAGE_D

Rank for Variable LEVERAGE

1 0.01541 0.00719 2.14 0.0320 2.91649

CURRENT_D Rank for Variable CURRENT 1 0.06142 0.00524 11.72 <.0001 1.58164

LTDEBT_D Rank for Variable LTDEBT 1 0.07138 0.00614 11.62 <.0001 2.15617

MV_D Rank for Variable MV 1 -0.17223 0.00523 -32.93 <.0001 1.61632

SGROWTH_D

Rank for Variable SGROWTH 1 0.01033 0.00456 2.27 0.0234 1.19734

PRSTDS_D Rank for Variable PRSTDS 1 0.01828 0.00473 3.87 0.0001 1.28064

PEN_D Rank for Variable PEN 1 0.03465 0.00436 7.95 <.0001 1.11148

Y1998 1 -0.09874 0.04028 -2.45 0.0143 1.62162

Y1999 1 -0.12031 0.03797 -3.17 0.0015 1.58150

Y2000 1 0.01908 0.03764 0.51 0.6123 1.66993

Y2001 1 0.08256 0.03659 2.26 0.0241 1.64626

IND0 1 0.41100 0.18931 2.17 0.0300 1.00901

IND1 1 0.32616 0.05163 6.32 <.0001 1.18017

IND2 1 -0.03666 0.15838 -0.23 0.8170 1.01829

IND3 1 0.07778 0.03425 2.27 0.0232 1.51357

IND4 1 0.13793 0.03926 3.51 0.0004 1.12800

IND5 1 0.39519 0.04160 9.50 <.0001 1.15803

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3. ANOVA results

FACTOR 1:Source DF Sum of Squares Mean Square F Value Pr > F

Model 7 91.307798 13.043971 13.29 <.0001

Error 4542 4457.692202 0.981438

Corrected Total 4549 4549.000000

R-Square Coeff Var Root MSE Factor1 Mean

0.020072 1.09613E18 0.990676 9.0379E-17

Source DF Anova SS Mean Square F Value Pr > F

INDUST 7 91.30779763 13.04397109 13.29 <.0001

Alpha 0.05

Error Degrees of Freedom 4542

Error Mean Square 0.981438

Critical Value of Studentized Range 4.28834

FACTOR 2:

Source DF Sum of Squares Mean Square F Value Pr > F

Model 7 127.789264 18.255609 18.75 <.0001

Error 4542 4421.210736 0.973406

Corrected Total 4549 4549.000000

R-Square Coeff Var Root MSE Factor2 Mean

0.028092 1.57847E17 0.986613 6.2504E-16

Alpha 0.05

Error Degrees of Freedom 4542

Error Mean Square 0.973406

Critical Value of Studentized Range 4.28834

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FACTOR 3

Source DF Sum of Squares Mean Square F Value Pr > F

Model 7 356.511745 50.930249 55.18 <.0001

Error 4542 4192.488255 0.923049

Corrected Total 4549 4549.000000

R-Square Coeff Var Root MSE Factor3 Mean

0.078371 -7.8926E16 0.960754 -0.000000

Source DF Anova SS Mean Square F Value Pr > F

INDUST 7 356.5117450 50.9302493 55.18 <.0001

Alpha 0.05

Error Degrees of Freedom 4542

Error Mean Square 0.923049

Critical Value of Studentized Range 4.28834

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Tuckey pairwise comparisons of factor 1 means across industries (The insignificant comparisons are not shown).

FACTOR1: PROFITABILITY

Comparisons significant at the 0.05 level INDUSTRY Difference

Simultaneous 95% Confidence Limits  

Comparison Between  Means

6 - 3 0.25763 0.14505 0.37021 ***

6 -5 0.36341 0.21095 0.51588 ***

6 -2 0.68115 0.08838 127.391 ***

4 -3 0.19725 0.03752 0.35697 ***

4 -5 0.30303 0.11307 0.49298 ***

4 -2 0.62076 0.01726 122.426 ***

3 -6 -0.25763 -0.37021 -0.14505 ***

3 -4 -0.19725 -0.35697 -0.03752 ***

5 -6 -0.36341 -0.51588 -0.21095 ***

5 -4 -0.30303 -0.49298 -0.11307 ***

2 -6 -0.68115 -127.391 -0.08838 ***

2 -4 -0.62076 -122.426 -0.01726 ***

Tukey pairwise comparisons of factor 2 means across industries. (The insignificant comparisons not shown.)

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FACTOR1: DEBT

Comparisons significant at the 0.05 level

INDUSTComparison

DifferenceBetween

MeansSimultaneous 95% Confidence

Limits

0 - 6 0.77299 0.06483 1.48116 ***

0 - 4 0.77509 0.05799 1.49218 ***

0 - 2 0.94349 0.02616 1.86082 ***

0 - 1 1.00150 0.27564 1.72737 ***

0 - 7 1.10045 0.12846 2.07244 ***

3 - 6 0.30467 0.19255 0.41678 ***

3 - 4 0.30676 0.14769 0.46582 ***

3 - 1 0.53317 0.33835 0.72800 ***

5 - 6 0.25258 0.10074 0.40442 ***

5 - 4 0.25468 0.06550 0.44385 ***

5 - 1 0.48109 0.26100 0.70118 ***

6 - 0 -0.77299 -1.48116 -0.06483 ***

6 - 3 -0.30467 -0.41678 -0.19255 ***

6 - 5 -0.25258 -0.40442 -0.10074 ***

6 - 1 0.22851 0.04442 0.41260 ***

4 - 0 -0.77509 -1.49218 -0.05799 ***

4 - 3 -0.30676 -0.46582 -0.14769 ***

4 - 5 -0.25468 -0.44385 -0.06550 ***

4 - 1 0.22642 0.01050 0.44234 ***

2 - 0 -0.94349 -1.86082 -0.02616 ***

1 - 0 -1.00150 -1.72737 -0.27564 ***

1 - 3 -0.53317 -0.72800 -0.33835 ***

1 - 5 -0.48109 -0.70118 -0.26100 ***

1 - 6 -0.22851 -0.41260 -0.04442 ***

1 - 4 -0.22642 -0.44234 -0.01050 ***

7 - 0 -1.10045 -2.07244 -0.12846 ***

Tukey pairwise comparisons of factor 3 means across industries.(The insignificant comparisons not shown.)

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Comparisons significant at the 0.05 level are indicated by ***.

INDUSTComparison

DifferenceBetween

MeansSimultaneous 95% Confidence Limits

0 - 3 0.99126 0.29887 1.68366 ***

4 - 6 0.24550 0.10360 0.38741 ***

4 - 1 0.25484 0.04458 0.46510 ***

4 - 5 0.26637 0.08215 0.45058 ***

4 - 3 0.83857 0.68367 0.99346 ***

6 - 4 -0.24550 -0.38741 -0.10360 ***

6 - 3 0.59307 0.48389 0.70224 ***

1 - 4 -0.25484 -0.46510 -0.04458 ***

1 - 3 0.58373 0.39402 0.77345 ***

5 - 4 -0.26637 -0.45058 -0.08215 ***

5 - 3 0.57220 0.41183 0.73257 ***

3 - 0 -0.99126 -1.68366 -0.29887 ***

3 - 4 -0.83857 -0.99346 -0.68367 ***

3 - 6 -0.59307 -0.70224 -0.48389 ***

3 - 1 -0.58373 -0.77345 -0.39402 ***

3 - 5 -0.57220 -0.73257 -0.41183 ***

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