Accounting Determinants of Credit Ratings

The Role of Accounting Determinants in Predicting Long Term Credit RatingsGorana Roje Research Assistant Institute of Economics, Zagreb Croatia [email protected]Abstract This 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 disparity JEL classification: M 41Acknowledgements I am grateful to my numerous colleagues for worthy suggestions.1 IntroductionThe 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. 2 1. Data sampleThe 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 variablesThe 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,  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 or1 (http://wrds.wharton.upenn.edu)3 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 Frequency 1 7 87 2 6 791 3 5 1055 4 4 1339 5 3 1006 6 2 272When checking for the normality, some of the independent variables exhibited quite big departures from normality2. Therefore, all the variables 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 follows3:2 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. 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 4 1. ROA= Net Income before Extraordinary Items/ Total Assets 2. ROE= Net Income before Extraordinary Items / Book Value of Equity; 3. PROFIT= Net Income before Extraordinary Items / Sales The 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 price Market 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 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 / Assets Tangible 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 Assets Leverage 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 Assets Long-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 Liabilities Current 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 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. 4 Tangible book value: Book value minus intangible assets. 5 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 Assets The 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 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 assets Volatility 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 VARIABLES Year 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 Techniques In 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?5 The way analysts perceive the risk of particular industries.6 3. Can the model be further simplified by distinguishing sets of variables that proxy for some common factors that make influence on credit ratings? 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 Results Logistic procedureIn 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 estimated7 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 Earnings 11 suggests that the 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 general6 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.8 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 <.0001 Intercept 6 1 -16.514 0.2089 625.081 <.0001 Intercept 5 1 0.3907 0.2067 35.728 0.0587 Intercept 4 1 26.503 0.2111 1.576.713 <.0001 Intercept 3 1 52.739 0.2247 5.509.564 <.0001 PROF_ALL_D 1 -0.2504 0.0131 3.664.486 <.0001 BV_TANG_D 1 -0.0836 0.0153 299.109 <.0001 LEVERAGE_D 1 0.0450 0.0169 71.058 0.0077 CURRENT_D 1 0.1410 0.0125 1.264.790 <.0001 LTDEBT_D 1 0.1747 0.0146 1.429.384 <.0001 MV_D 1 -0.4202 0.0138 9.213.639 <.0001 SGROWTH_D 1 0.0235 0.0109 46.834 0.0305 PRSTDS_D 1 0.0498 0.0115 187.442 <.0001 PEN_D 1 0.0752 0.0106 499.558 <.0001 Y1998 1 -0.2315 0.0959 58.210 0.0158 Y1999 1 -0.3037 0.0904 112.899 0.0008 Y2000 1 0.00593 0.0895 0.0044 0.9472 Y2001 1 0.1881 0.0871 46.649 0.0308 IND0: Agricultur. 1 0.9063 0.4513 40.333 0.0446 IND1: Mining 1 0.8314 0.1235 453.056 <.0001 IND2: Construct. 1 -0.1168 0.3720 0.0986 0.7535 IND3: Transp/Uti 1 0.2265 0.0816 77.081 0.0055 IND4: Trade 1 0.3582 0.0934 147.042 0.0001 IND5: Services 1 0.9630 0.1005 917.792 <.0001In 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 estimates Effect Point estimate 95% Wald Confidence Limits PROF_ALL_D 0.778 0.759 0.799 BV_TANG_D 0.920 0.893 0.94812 DF indicates degree of freedom. Size reflects companies' market value(MV).9 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 3.190The 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 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 Responses Percent Concordant 69.0 Somers' D 0.598 Percent Discordant 9.2 Gamma 0.766In 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 predicted10 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 2 3 4 5 6 7 Total rating 2 Frequency 19 6 0 0 0 0 25 Percent 0.42 0.13 0.00 0.00 0.00 0.00 0.55 Row Pct 76.00 24.00 0.00 0.00 0.00 0.00 3 Col Pct 6.99 0.60 0.00 0.00 0.00 0.00 Frequency 217 580 284 52 14 4 1151 Percent 4.77 12.75 6. 24 1.14 0.31 0.09 25.30 Row Pct 18.85 50.39 24.67 4.52 1.22 0.35 4 Col Pct 79.78 57.65 21.21 4.93 1.77 4..60 Frequency 33 383 765 293 95 5 1574 Percent 0.73 8.42 16.81 6.44 2.09 0.11 34.59 Row Pct 2.10 24.33 48.60 18.61 6.04 0.32 5 Col Pct 12.13 38.07 57.13 27.77 12.01 5.75 Frequency 3 33 268 524 282 17 1127 Percent 0.07 0.73 5.89 11.52 6.20 0.37 24.77 Row Pct 0.27 2.93 23.78 46.50 25.02 1.51 6 Col Pct 1.10 3..28 20.1 49.67 35.65 19.54 Frequency 0 4 22 186 400 61 673 Percent 0.00 0.09 0.48 4.09 8.79 1.34 14.79 Row Pct 0.00 0.59 3..27 27.64 59.44 9.06 Col Pct 0.00 0.40 1.64 17.63 50.57 70.11Total 272 1006 1339 1055 791 87 4550 5.98 22.11 29.43 23.19 17.38 1.91 100.00Tables 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).11 Table 6. Prediction Accuracy from the logistic model with industry indicator variables.Percentage of Observations Cumulative PercentagePerfectMatch 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 PercentagePerfect 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 PercentagePerfectMatch 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%Factor analysisThis 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.12 This method suggested three factors even though the 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 Plot 3.532.521.510.50 1 2 3 4 5 6 7 8 9 10 11The 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 factor Three 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 factor This 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). 3. Size/Volatility factor This 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 liquidity13 (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 . . 0.60890 PRSTDS_D . . 0.41825 PEN_D . . . SGROWTH_D . . -0.48184 MV_D . . -0.58763Cronbach Alpha 0.9346 0.8224 0.3866 ( variables with loading above 0.4)Values less than 0.4 are not printed.Table 10. Variance explained by extracted factors Factor1 Factor2 Factor3 Eigenvalues 2.831 2.438 1.346 As percent of Total 25.7% 22.2% 12.2% Cumulative percent 25.7% 47.9% 60.1%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. 14 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 factors Analysis of Maximum Likelihood Estimates Parameter DF Estimate Standard error Wald Chi-Sq Pr > ChiSq Intercept 7 1 -59.717 0.1455 16.851.333 <.0001 Intercept 6 1 -26.129 0.0827 9.978.319 <.0001 Intercept 5 1 -0.6943 0.0723 922.629 <.0001 Intercept 4 1 13.472 0.0745 3.273.545 <.0001 Intercept 3 1 37.631 0.0957 15.447.132 <.0001 Factor1 :Profitability 1 -11.153 0.0324 11.852.353 <.0001 Factor2: Debt 1 10.717 0.0321 11.127.748 <.0001 Factor3: 1 11.426 0.0334 11.711.580 <.0001 Size/Volatillity Y1998 1 -0.2366 0.0915 66.854 0.0097 Y1999 1 -0.2970 0.0882 113.284 0.0008 Y2000 1 -0.0177 0.0860 0.0425 0.8367 Y2001 1 0.2409 0.0848 80.651 0.0045 IND0: Agriculture 1 0.8342 0.4460 34.981 0.0614 IND1: Mining 1 10.048 0.1169 738.505 <.0001 IND2: 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 0.0002 IND5: Services 1 0.9891 0.0977 1.024.410 <.0001The 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 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 analysis15 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.Graph 3. Mean Indus try Factor S cores0.80.6 N O I 0.4 T C U R T0.2 S N O C 0 S E E I T R I L U I G T E T-0.2 L N D U I UA R C D R I S U T N R T E-0.4 A G C R CG I . A A & P V N R F I S W R E U N E I N H -0.6 N A S T A M R O T M -0.8Profitability Debt Size/VolatilityThe 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 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. 16Graph 4. Mean S&P Long-Term Credit Ra ting for Indus tr y5.55 s 4.5 g n i t a R4 t i d e rC 3.53 S R N E S E G G E E E O R D N I N I I I C T H A U T I I R T N T R I L V C L I U T O R U T T M U E R R U C CI T S & A D R S F W G N N U A A O N. C A P S M N A R TTransport/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. ConclusionThe 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. 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.17 LiteratureCantor, 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, ZagrebTabachnick Barbara and Fidell Linda (2000): "Computer-Assisted Research Design and Analysis", Pearson Allyn & Bacon.Žager Katarina, Žager Lajoš (2001): “Analiza financijskih izvještaja”, Masmedia, Zagreb18 Appendix 1. Logistic regression: Odds ratio estimatesOdds Ratio EstimatesPoint 95% Wald Effect Estimate 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 3.190Testing Global Null Hypothesis: BETA=0Test Chi-Square DF Pr > ChiSqLikelihood Ratio 3854.7750 19 <.0001Score 2601.8773 19 <.0001Wald 2699.3732 19 <.000119 2. OLS results:Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model 19 3811.93805 200.62832 315.06 <.0001 Error 4530 2884.69843 0.63680 Corrected Total 4549 6696.63648Root MSE 0.79800 R-Square 0.5692 Dependent Mean 4.29626 Adj R-Sq 0.5674 Coeff Var 18.57420Parameter EstimatesParameter Standard Variance Variable Label DF Estimate Error t Value Pr > |t| InflationIntercept Intercept 1 4.71095 0.08698 54.16 <.0001 0PROF_ALL_D 1 -0.10399 0.00533 -19.53 <.0001 1.43678BV_TANG_D Rank for Variable BV_TANG 1 -0.03528 0.00646 -5.46 <.0001 2.41408LEVERAGE_ Rank for Variable 1 0.01541 0.00719 2.14 0.0320 2.91649 D LEVERAGECURRENT_D Rank for Variable CURRENT 1 0.06142 0.00524 11.72 <.0001 1.58164LTDEBT_D Rank for Variable LTDEBT 1 0.07138 0.00614 11.62 <.0001 2.15617MV_D Rank for Variable MV 1 -0.17223 0.00523 -32.93 <.0001 1.61632SGROWTH_D Rank for Variable SGROWTH 1 0.01033 0.00456 2.27 0.0234 1.19734PRSTDS_D Rank for Variable PRSTDS 1 0.01828 0.00473 3.87 0.0001 1.28064PEN_D Rank for Variable PEN 1 0.03465 0.00436 7.95 <.0001 1.11148Y1998 1 -0.09874 0.04028 -2.45 0.0143 1.62162Y1999 1 -0.12031 0.03797 -3.17 0.0015 1.58150Y2000 1 0.01908 0.03764 0.51 0.6123 1.66993Y2001 1 0.08256 0.03659 2.26 0.0241 1.64626IND0 1 0.41100 0.18931 2.17 0.0300 1.00901IND1 1 0.32616 0.05163 6.32 <.0001 1.18017IND2 1 -0.03666 0.15838 -0.23 0.8170 1.01829IND3 1 0.07778 0.03425 2.27 0.0232 1.51357IND4 1 0.13793 0.03926 3.51 0.0004 1.12800IND5 1 0.39519 0.04160 9.50 <.0001 1.158033. ANOVA results20 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 4549.000000R-Square Coeff Var Root MSE Factor1 Mean 0.020072 1.09613E18 0.990676 9.0379E-17Source DF Anova SS Mean Square F Value Pr > F INDUST 7 91.30779763 13.04397109 13.29 <.0001Alpha 0.05 Error Degrees of Freedom 4542 Error Mean Square 0.981438 Critical Value of Studentized Range 4.28834FACTOR 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 4549.000000R-Square Coeff Var Root MSE Factor2 Mean 0.028092 1.57847E17 0.986613 6.2504E-16Alpha 0.05 Error Degrees of Freedom 4542 Error Mean Square 0.973406 Critical Value of Studentized Range 4.2883421 FACTOR 3Source 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 4549.000000R-Square Coeff Var Root MSE Factor3 Mean 0.078371 -7.8926E16 0.960754 -0.000000Source DF Anova SS Mean Square F Value Pr > F INDUST 7 356.5117450 50.9302493 55.18 <.0001Alpha 0.05 Error Degrees of Freedom 4542 Error Mean Square 0.923049 Critical Value of Studentized Range 4.2883422 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 DifferenceComparison Between Simultaneous 95% Means Confidence Limits 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.)23 FACTOR1: DEBT Comparisons significant at the 0.05 level Difference INDUST Between Simultaneous 95% Confidence Comparison Means 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.)24 Comparisons significant at the 0.05 level are indicated by ***. Difference INDUST Between Simultaneous 95% Comparison Means 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 ***25

Accounting Determinants of Credit Ratings

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