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The Impact of Value Creation (Tobin’s Q), Total Shareholder Return (TSR), and Survival (Altman’s Z) on Credit Ratings

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25 March 2024

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26 March 2024

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Abstract
The research explores the impact of financial indicators on the credit ratings of companies listed on the S&P 500, employing a Sys-GMM Model to address endogeneity concerns. The study investigated three independent variables categorized as market and survival factors alongside with seven control variables sourced from leverage, liquidity, interest coverage, profitability, market, survival, and macroeconomic domains. The sample comprised 2398 observations from Capital IQ Pro, spanning nine years (2013 to 2021) and encompassing 240 public companies. The findings suggest that neither Tobin's Q (TQ) and Total Shareholder Return (TSR) lack significant correlations with credit ratings, implying that stock market performance and total shareholder return do not directly impact credit ratings. In contrast, the Altman Z-score (AZS) emerged as a significant predictor, indicating its importance in assessing credit risk. These insights enhance understanding of financial indicators' impact on credit ratings, aiding financial institutions and companies in prudent lending and financing decisions.
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Subject: Business, Economics and Management  -   Finance

1. Introduction

The provided text discusses the intricate nature of credit risk assessment in finance, emphasizing its importance for decision-making in the global financial market. Ganguin and Bilardello (2005) describe credit risk assessment as a blend of art and science, requiring continuous monitoring of crucial factors. Gray et al. (2006) highlight the substantial influence of credit ratings on a company's cost of debt, financing structure, and trading viability.
Investors heavily rely on credit ratings as a primary source of information about bond quality and marketability (Pinches and Singleton, 1978). The central research question of the study is focused on the extent to which financial indicators can predict credit ratings, contributing to the reduction of financial losses for investors.
The study aims to analyze the impact of market and survival indicators on credit ratings, considering Total Shareholder Return (TSR), Tobin's Q (TQ), and Altman's Z-score (AZS) as independent variables. Control variables, including leverage, profitability, interest coverage, liquidity, and various macroeconomic factors, are also investigated.
An innovative aspect of the study lies in the simultaneous analysis of TSR, TQ, and AZS, addressing gaps in the literature. The goal is to provide a comprehensive insight into the interconnectedness of these financial indicators and their impact on credit ratings.
Credit risk assessment is crucial for risk management in the financial market, assisting lenders and investors in decision-making by gauging the likelihood of default or a company's inability to meet financial obligations. The text distinguishes risk from uncertainty and introduces credit as synonymous with trust, emphasizing the anticipation of future cash flows.
Credit Rating Agencies (CRAs) play a vital role in credit risk assessment, offering neutral and independent opinions to evaluate the creditworthiness of potential borrowers. The origins of CRAs trace back to the early 1900s, and their role remains crucial despite technological advancements. Ferri and Liu (2002) emphasize the growing global importance of CRAs as financial markets evolve and regulations intensify. Tang (2009) underscores the critical role of rating agencies in reducing information asymmetry and providing essential creditworthiness information to market participants.

2. Literature Review and Hypothesis

The text explores the research problem of assessing the ability of financial indicators to forecast credit ratings, aiming to mitigate financial losses for investors. Crouhy et al. (2006) define risk as predicting budgeting costs and the threat of unexpected cost overruns due to uncontrolled rising cost factors. Risk management, crucial for effective financial management, cannot prevent market disruptions or scandals but remains vital.
Fridson (2007) argues for incorporating risk into financial products, enhancing market organization understanding, volatility levels, margin requirements, and profit distribution. Van Deventer et al. (2013) stress the importance of integrated credit risk analysis, considering market risk, asset and liability management, and performance measurement, particularly for financial institutions.
The theory of efficient frontier by Markowitz (1952), promoting diversification in asset portfolios, has been widely applied by financial institutions to reduce exposure to credit risks and maximize returns. Modigliani and Miller (1958) emphasize incorporating credit risk factors into the cost of debt, impacting a company's financial structure and decision-making regarding new loans and financing.
Merton (1974) links a company's credit risk profile to its asset value, proposing a model predicting default probability based on expected asset value and debt. Altman and Hotchkiss (2011) identify reasons for corporate bankruptcy, while Frost (2007) attributes the increased use of credit ratings to the globalization of financial markets and complex financial innovations.
S&P Global (2021) defines credit rating as a forward-looking assessment of creditworthiness. Pinches and Singleton (1978) highlight the crucial role of credit ratings in providing confidential information about bond issues, influencing decision-making in lending. Ganguin and Bilardello (2005) stress the comprehensive analysis of a company's capacity and willingness to pay financial obligations.
Graham and Harvey (2001) and Damodaran (2010) underscore the importance of credit ratings and financial flexibility in deciding to issue more debt. Singal (2013) notes credit ratings as reliable indicators of a company's past, present, and future performance. Vipond (2022) mentions rating agencies assessing the ability of entities to make payments and providing benchmarks for financial market regulation.
Overall, credit ratings serve as crucial indicators, impacting financial decisions for companies, investors, and regulators, with rating agencies evolving methodologies and criteria over time (Crouhy et al., 2006; Vipond, 2022).
Table 1. Credit Ratings Global Scale.
Table 1. Credit Ratings Global Scale.
S&P Global Ratings Description
Investment Grade AAA Extremely strong capacity to meet its financial commitments.
AA Very strong capacity to meet its financial commitments.
A Strong capacity to meet its financial commitments.
BBB Adequate protection parameters to meet its financial commitments.
Speculative Grade BB Less vulnerable to nonpayment than other speculative issues. However, it faces major ongoing uncertainties or exposure to adverse business, financial, or economic conditions.
B More vulnerable to nonpayment than obligations rated 'BB', but the obligor currently has the capacity to meet its financial commitments on the obligation.
CCC Currently vulnerable to nonpayment and is dependent upon favorable business, financial, and economic conditions.
CC An obligation rated 'CC' is currently highly vulnerable to nonpayment.
C An obligation rated 'C' is currently highly vulnerable to nonpayment, and the obligation is expected to have lower relative seniority recovery compared with obligations rated higher.
D An obligation rated 'D' is in default.
Source: S&P Global (2021).
Financial institutions utilize credit ratings from rating agencies to determine the risk premium charged on bonds and loans, where a low credit rating implies a high-risk premium and higher costs for companies with poor credit profiles (Vipond, 2022). The reliability of credit risk analysis by rating agencies is acknowledged due to their access to confidential information, but criticisms arise from accusations of assigning high ratings to high-risk debts, prompting calls for industry accountability.
Vipond (2022) highlights a potential conflict of interest between issuers and rating agencies, as issuers pay for evaluations, potentially influencing the assigned rating. This underscores the importance of transparency and impartiality in the credit rating process. Papaikonomou (2010) argues that regulators recognize the use of credit ratings in calculating investment risks.
Table 2. Literature Reference Relative to the impact of financial metrics on Credit Ratings.
Table 2. Literature Reference Relative to the impact of financial metrics on Credit Ratings.
Authors Methodology Dependent Variables Independent Variables
Murcia et al. (2014) Generalized Estimating Equations (GEE) model considering a panel structure credit rating Leverage, Profitability, Size, Financial coverage, Growth, Liquidity, Corporate governance, Control, Financial market performance and Internationalization
Hwang (2013) GEE and Ordered probit model Credit Rating Leverage, Coverage, Cash flow, Profitability, Liquidity
Gray et al. (2006) Ordered probit model Credit Rating EBIT interest coverage, EBITDA interest coverage, Operating funds/Total debt, Operating cash flows/Total debt, Return on capital, Operating margin, LT debt leverage, Total debt leverage, Industry beta, and Industry concentration
Soares et al. (2012) Ordered probit model Credit Rating ROA, Operational Margin, EBIT margin, EBITDA margin, Liquid Margin
Krichene and Khoufi (2015) Ordered probit model Credit Rating EBITDA/INT-aver’, ‘Bus-Seg-aver’, ‘Geo-Seg-aver’, ‘Rev-aver’, ‘FCF/TD-aver’, ‘ROA-aver’, ‘CUR-Rat-aver’ and ‘TD/CE-aver
Mushafiq et al. (2023) Panel Regression Return on Assets (ROA), Return on Equity (ROE) Z-score, Leverage, Liquidity, Firm Size
Rafay et al. (2018) Ordered Probit Model and Panel Data Regression Return on Assets (ROA), Tobin's Q Credit Ratings, Entity Size, Leverage, Liquidity, Dividend per Share, Loss Propensity, Industry Type, Stock Price, Stock Returns
Gupta (2023) Ordered probit model Credit Rating Size, Liquidity, Leverage, Interest coverage, Growth
Wang and Ku (2021) Use of AI methods.
Damasceno et al. (2008) Ordered probit model Credit Rating Brazilian Index Dummy Variable, Size, Payment Capacity, Capital Structure, Profitability
Hung et al. (2013) Ordered probit model Credit Rating Free Cash Flow, Cash Turnover, Debt Ratio, Fixed Ratio, Working Capital, Cash to Current Liabilities Ratio, Receivable Turnover, Days to pay Accountable Payable, Debt to EBITDA, EBITDA Interest Coverage, Industry Factors, ROA, Dividend Payout, Total Assets
Archana and Jayanna (2016) ANOVA Credit Rating Current Ratio, Quick Ratio, Debt Equity, Interest Coverage, Profit Margin, Return on Capital Employed, Return on Net Worth, EBIT Margin, Cash Profit Margin
Hirk et al. (2022) Multivariate ordinal regression model Credit Rating Size, Profitability, Liquidity, Leverage and Capital structure, risk based on market prices (BETA, SIGMA) and whether the company is a dividend payer (div_payer)
Al-Khawaldeh (2013) Ordinary least squares (OLS) model Credit Rating Leverage, Profitability, Capital Intensity, Size, Tobin`s q, Loss propensity, Type of Sector, Audit type
Hamid et al. (2019) Logistic regression model Bond Rating Company size, liquidity, leverage and profitability
Sajjad and Zakaria (2018) Panel data analysis and generalized method of moment (GMM) estimation techniques Capital Structure (Leverage= TDA=TD/TA) (1) Credit Ratings, (2) Firm's Factors: Lag_TDA, Tangibility, Liquidity, Size, Profitability, Growth opportunities, (3) Country's Factors: DSM, GDPG, INF, RIR, (4)Industrial Dummies: Technology, Industrial, Consumer Services, Consumer good, Health care, Utility, Basic material, Oil and gas, Telecommunication
Utami et al. (2018) Logistic regression Bond Rating Profitability, Liquidity, Solvency, Activity ratio
Hwang et al. (2010) Ordered semiparametric probit model Credit Rating (1) Market-driven variables, Size, Financial Leverage, Coverage, Cash Flow, Profitability, Liquidity, and Industry Indicators.
Source: Own authorship.
The mentioned articles collectively contribute valuable insights into credit risk, risk management, and the significance of credit ratings. The research problem, focused on the role of financial indicators in predicting credit ratings and minimizing financial losses for investors, aligns with the provided insights on the complexities of credit risk assessment and underscores the importance of transparent and impartial credit rating processes.
To assess the influence of the independent variables on credit ratings, a hypothesis was formulated as follows:
H: Companies with higher TQ, TSR, or AZS positively impact credit ratings.

2.1. Ha: Tobin'Q (TQ)

TQ is a market value ratio that compares a company's market value to the replacement cost of its assets, as per the definition provided by Carton and Hofer (2006). Unlike profit measures, TQ has an advantage, as Barney (2002) pointed out, in that it does not rely on accounting profits or the weighted average cost of capital (WACC). A TQ ratio greater than 1.0 indicates that the company is expected to perform better than the industry average. In contrast, a ratio below 1.0 implies that the company will likely underperform in the overall industry. The authors suggest that a positive correlation between TQ and credit ratings is expected because companies with higher TQ ratios tend to have valuable assets, profitable operations, and growth prospects, all contributing to a firm's creditworthiness.
In Rafay et al.'s (2018) investigation into the impact of credit ratings on the performance and share returns of companies listed on the Taiwan Stock Exchange (TSE), with Return on Assets (ROA) and TQ as dependent variables, they found that credit rating relates positively with TQ measure.

2.2. Hb: Total Shareholder Return

According to Ganti (2021), TSR is a comprehensive metric combining a stock's share price appreciation and total dividends paid within a specific timeframe. It indicates the overall financial benefits to stockholders, providing insights into how the market perceives a company during a defined period. Ganti suggests a reasonable expectation of a positive correlation between TSR and credit ratings, especially in significant share price growth, indicating a potential association between higher TSR and improved credit ratings. However, Ganti also notes that TSR may encounter challenges if a fundamentally strong company undergoes a substantial short-term decline in its share price due to negative publicity or unpredictable market behavior.
Angeline Ng and M. Ariff (2019) state a significant correlation between stock prices and credit change disclosures. This suggests a linkage between credit rating changes and stock price movements.
Based on the above points, a higher TSR may signal improved financial performance, enhanced profitability, and increased shareholder value. CRAs could view these positive indicators favorably when evaluating a company's creditworthiness.
Also, Companies with a higher TSR will likely enjoy heightened market confidence, potentially fostering increased trust from creditors and lenders. This positive market perception could influence CRAs to hold a more favorable view of the company's creditworthiness.
Although TSR is susceptible to short-term market fluctuations, a consistently higher TSR may suggest that a company is resilient to temporary setbacks and capable of delivering sustained shareholder value. This resilience could alleviate concerns from CRAs, contributing to a positive assessment of creditworthiness.

2.3. Hc: Altman's Z-Score (AZS)

In 1968, Altman developed a discriminant analysis model that used a set of financial ratios to predict the probability of a company's bankruptcy. This model served as a pattern for rating agencies to develop their methodologies, which included using financial ratios to promote transparency and consistency in credit analysis. Altman's model which provides for five financial ratios such as working capital/total assets, retained earnings/total assets, earnings before interest and taxes/total assets, the market value of equity/book value of total liabilities, and sales/total assets, is one of the tools that rating agencies use to evaluate credit risk.
Czombera (2014) suggests that the relationship between Z-Score and credit rating is complex and not straightforward. Although AZS offers some insights into credit ratings, especially for homogenous portfolios, its application is limited, and caution should be exercised when attempting to replace sophisticated agency ratings.

3. Materials & Methods

Gujarati (2006) suggests treating categorical variables with inherent ordering, like credit ratings, as ordinal in statistical analysis to preserve ordering information. Gupta (2023) converted credit ratings into numerical values in their study on Indian companies. Our study employs the entire S&P Global rating grade, converting each credit rating category into a Weighted Long-Term Average (WLTA) based on 2022 Annual Global Corporate Default and Rating Transition Study (S&P Global, 2022). The WLTA incorporates weights for each category, creating a Credit Rating Weighted Long-Term Average (CRWLTA) scale that combines the ordinal scale with default weighted averages, enhancing study consistency for accurate measurement of the impact of independent variables on credit ratings.
Table 3. Dependent Variable Classes.
Table 3. Dependent Variable Classes.
Grade S&P CLASS WLTA CRWLTA
Investment Grade AAA 22 0 22
AA+ 21 0.0002 21.0042
AA 20 0.0002 20.004
AA- 19 0.0002 19.0038
A+ 18 0.0005 18.009
A 17 0.0005 17.0085
A- 16 0.0005 16.008
BBB+ 15 0.0014 15.021
BBB 14 0.0014 14.0196
BBB- 13 0.0014 13.0182
Speculative Grade BB+ 12 0.0059 12.0708
BB 11 0.0059 11.0649
BB- 10 0.0059 10.059
B+ 9 0.0307 9.2763
B 8 0.0307 8.2456
B- 7 0.0307 7.2149
CCC+ 6 0.257 7.542
CCC 5 0.257 6.285
CCC- 4 0.257 5.028
CC 3 0.257 3.771
C 2 0.257 2.514
D/SD 1 0 1
Source: Own authorship.

3.1. Data and Sample

The dataset comprises 2398 observations from 240 public companies active in the United States market. The data was acquired through Capital IQ Pro, encompassing the timeframe spanning 2013 to 2021, resulting in a panel-format presentation. It is crucial to note that the initial database encompassed 500 companies. After a comprehensive analysis, financial institutions, government entities, and companies lacking essential information were excluded, refining the dataset to the final count of 240 companies.
The independent variables used in the model are presented in Table 4.
An initial analysis will be conducted for the database. Therefore, a descriptive analysis and a study of variable correlations will be applied.
The data preparation involves two steps. Panel data, often exhibiting time series characteristics, may encounter non-stationarity. The Levin-Lin-Chu (LLC) test will be employed for unit root testing, with a focus on identifying and differentiating non-stationary series. Before model specification, assessing correlation among independent variables is essential to identify multicollinearity. The Variance Inflation Factor (VIF) will be used, and variables causing multicollinearity will be removed if detected.
For model specification, a System-Generalized Method of Moments (Sys-GMM) will be adopted, incorporating elements from both difference and level equations proposed by Blundell and Bond (1998). Sys-GMM, designed for dynamic panel data models, addresses endogeneity concerns, using moment conditions from individual and system-level equations.
After model estimation, assumptions and model validation will be checked. The Sargan/Hansen test will assess overidentification of restrictions, ensuring instrument exogeneity. Autocorrelation in differences will be tested using first-order (AR1) and second-order (AR2) models. The finite instrument test will ensure an appropriate number of instruments, preventing overfitting. Robustness of model specification will be verified using tests like Wald or LM to enhance the model if necessary (Davidson and MacKinnon, 1993).

4. Results

Table 5 presents a comprehensive analysis of key variables in the study.
Notable findings include CRWLTA exhibiting relatively low variation (mean of 15.09, SD of 2.46), Quick Ratio (QR) suggesting companies generally cover short-term debts (average of 1.11), and Total Debt to Total Asset Ratio (TDTA) indicating debts represent 32% of total assets on average. EBITDA interest coverage (EBITDAICOV) shows varying interest coverage, with an average of 16.12 but a high SD of 14.81. ROA averages 11.16%, with some companies facing operational challenges (negative ROA of -12.91).
TQ demonstrates a market-to-book relationship (average of 0.33), TSR shows significant variation in shareholder returns, and AZS suggests moderate distribution. Economic metrics like gross domestic product (GDP) growth (average of 2.13%) and Consumer Price Index (CPI) inflation (average of 1.86%) indicate moderate economic conditions. Federal Reserve Interest Rate (FDRI) has an average of 0.70, suggesting a manageable range of Federal Reserve interest rates.
In summary, the table 5 provides insights into financial and operational performance, showcasing heterogeneity among companies. Macroeconomic metrics offer additional context about the external environment.
Table 6 highlights correlations between independent and dependent variables.
Notable findings include a moderate negative correlation between CRWLTA and TDTA, suggesting higher leverage associates with lower credit ratings. A positive correlation between CRWLTA and EBITDAICOV (0.37) implies that companies covering interest with EBITDA tend to have higher credit ratings, reflecting financial strength.
Positive correlations exist between CRWLTA and ROA, indicating more profitable companies tend to have higher credit ratings, and between CRWLTA and AZS, reflecting financial health. A negative correlation with TQ suggests companies with higher market value relative to book value might have lower credit ratings.
The almost negligible correlation between CRWLTA and TSR suggests market stock performance isn't directly tied to credit ratings. Similarly, the weak correlation between CRWLTA and GDP suggests little direct effect of GDP growth on credit ratings. Other correlations with credit ratings are relatively low, emphasizing the need for nuanced interpretation and consideration of external factors and industry characteristics (Table 6).
Table 7 reveals high VIFs for both "TDTA" and "TQ" exceeding the threshold, indicating potential multicollinearity. One explanation could be that TQ, comparing market value with asset replacement cost, is influenced by highly leveraged companies (high TDTA), seen as risky by investors, leading to lower market valuation relative to asset replacement cost and a lower TQ. Additionally, companies with high debts (high TDTA) may face challenges raising additional capital, limiting future growth and impacting TQ.
Certain industries or situations may naturally exhibit both high TDTA and low TQ, especially in capital-intensive sectors with high entry barriers. The potential interdependence or calculation overlap between variables could also contribute to multicollinearity.
To address this issue, the TDTA variable will be removed from the model, considering the potential reasons outlined above (Table 7).
According to the LLC test results presented in Table 8, the variables CRWLTA, QR, TDTA, EBITDAICOV, ROA, QT, TSR, AZS, and FDRI are stationary, as their p-values are significant (less than 0.05) and the adjusted t* statistic is negative. Therefore, the null hypothesis for these variables is rejected.
On the other hand, the variables GDP and CPI are non-stationary, as their p-values are not significant (equal to 1.00), and the adjusted t* statistic is positive. Therefore, the null hypothesis is not rejected for these variables. Consequently, the two mentioned variables will be differentiated (Table 8).
Finally, the Sys-GMM model results in Table 9 should be analyzed from the perspective of the relationship between the independent variable of interest, TQ, and the dependent variable, Credit Rating. The results indicate that the coefficient for TQ is negative (-0.122) but not statistically significant (p-value of 0.936), suggesting that, based on the data and the model used, there is not enough evidence to assert a relationship between TQ and the Credit Rating of the analyzed companies.
The negative and nonsignificant coefficient of TQ suggests that, within this model, no direct relationship is observed between a company's market value (measured by TQ) and its Credit Rating. Economically, this may indicate that factors other than the market's perception of the company influence the Credit Rating. This finding might be surprising, as TQ is often interpreted as an indicator of the market's future value attributed to a company. Based on the points above, we rejected the Ha hypothesis that a higher TQ could positively impact credit ratings.
The other coefficients in the model also exhibit various levels of statistical significance. For instance, the coefficient for the variable EBITDAICOV is positive and close to statistical significance (p-value of 0.071), suggesting a potential positive relationship between interest coverage by EBITDA and Credit Rating.
While statistical significance is an essential indicator of result reliability, economic significance is also crucial. For example, the positive and close-to-statistical-significance coefficient of EBITDAICOV suggests that a company's ability to cover its interest may be associated with a higher Credit Rating. This economically intuitive result reflects a company's capability to fulfil its financial obligations.
Furthermore, it is crucial to note that the model has a high Wald chi^2 value (13220.20 with a near-zero p-value), indicating that the model is statistically significant overall. Arellano-Bond autocorrelation tests indicate no first- or second-order autocorrelation issues, as p-values are greater than 0.05. The Sargan and Hansen tests do not reject the null hypothesis of instrument validity with high p-values. However, the Hansen Difference test suggests that when many instruments are used, instrument robustness might weaken, serving as a warning for potential model fragility concerning the number of instruments employed.
The tests confirm Instrument validity, signifying that the statistical tools used to identify relationships are appropriate. Nevertheless, the Hansen test suggests that using numerous instruments may weaken results in robustness, a crucial consideration for economic interpretation. This implies that the model may need to be more balanced, or some instruments might not contribute relevant information.
The high Wald chi^2 value indicates that the model as a whole is significant. Economically, this implies that the set of variables and instruments used in the model can explain variations in Credit Rating, even if Tobin's specific Q is insignificant.
Thus, the economic analysis of the results underscores the need to consider a range of financial and operational factors beyond market expectations when evaluating a company's Credit Rating. Corporate policy decisions should account for this complexity and the results of the model's diagnostic tests (Table 9).
Table 10 provides results focusing on the independent variable of interest, TSR, and the dependent variable, Credit Rating, revealing important econometric aspects with relevant economic implications.
The coefficient for TSR is positive (0.0006) but not statistically significant (p-value of 0.7460). This suggests that, in this model, there needs to be more evidence to claim a direct relationship between TSR and the Credit Rating of companies. Econometrically, this may indicate that TSR, incorporating capital gains and dividends relative to the initial stock price, is not a significant predictor for credit ratings in this study. Considering the information above, the Hb hypothesis was rejected.
For QR, with a negative coefficient (-0.0662) and a high p-value (0.8260), it is suggested that there is no significant relationship between companies' immediate liquidity and their credit rating. EBITDAICOV (EBITDA Coverage) presents a positive and nearly significant coefficient (p-value of 0.0900), indicating a trend that a higher ability to cover interest and other financial obligations may be associated with a higher Credit Rating. Economically, this is relevant as it reflects a company with better financial health and lower credit risk.
With a very high Wald chi^2 value (12587.70) and a p-value of 0.000, the model as a whole is significant. This means that although TSR is not individually significant, the set of considered variables helps explain variations in Credit Rating. The Arellano-Bond test shows no evidence of first or second-order autocorrelation, confirming the appropriateness of the lags used as instruments. The Sargan test rejects the validity of instruments (p-value of 0.000), while the Hansen test does not reject it (p-value of 0.235). This is concerning, suggesting potential over-identification and that not all instruments may be exogenous. The difference in Hansen tests does not suggest significant issues but is something to monitor.
Economically, the lack of a significant relationship between TSR and Credit Rating may have implications for investors and managers, indicating that investors may not perceive total return as an indicator of the company's credit risk.
The close-to-significance relationship of EBITDAICOV with Credit Rating suggests that rating agencies and investors closely scrutinize operational performance metrics and payment capacity. The discrepancy between the Sargan and Hansen tests indicates the need for caution in instrument selection and potentially revising the model to ensure exogeneity and avoid over-identification.
Thus, the analysis demonstrates that the model is globally valid in explaining Credit Rating, but TSR as an individual variable does not provide significant explanatory power. The results underscore the importance of considering a variety of financial and operational metrics when assessing companies' credit risk, along with the need for careful instrument selection to avoid validity issues in the statistical model (Table 10).
Finally, Table 11 presents the results of a Sys-GMM model with AZS as the independent variable of interest and Credit Rating as the dependent variable.
The coefficient for AZS is positive (0.236) and statistically significant at the 5% level (p-value of 0.035), suggesting a positive relationship between AZS and Credit Rating. Economically, this indicates that companies with a higher Z-score, interpreted as a lower probability of bankruptcy, tend to have a higher Credit Rating. Considering the information above, the Hc hypothesis was accepted. This aligns with economic literature associating lower insolvency risk with better credit ratings. QR continues to show a negative coefficient (-0.116) with no statistical significance (p-value of 0.697), implying that immediate liquidity is not a decisive factor for Credit Rating in this model. In EBITDAICOV, the coefficient is positive (0.030) and statistically significant (p-value of 0.042), reinforcing that better interest coverage is favourable for Credit Rating.
The high Wald chi^2 statistic (14231.84) with a p-value of 0.000 indicates that the model as a whole is highly significant in explaining Credit Rating variability. Meanwhile, the Arellano-Bond index suggests no evidence of problematic autocorrelation, as indicated by the p-values of AR(1) and AR(2) tests. The Sargan test indicates instrument validity issues (p-value of 0.000), while the Hansen test does not indicate problems (p-value of 0.226). This may suggest overidentification in the model, although the Hansen test does not confirm this concern.
The significance of AZS in the model is a crucial finding, suggesting that comprehensive measures of financial health, such as the AZS, are relevant indicators for CRAs. The consistent significance of EBITDAICOV in different models indicates that this metric is reliable in assessing credit risk. The overall high significance of the model reaffirms the importance of a diverse set of variables in determining Credit Rating. Concerns about instrument validity, suggested by the Sargan test, require attention. Proper selection and use of instruments are crucial to ensuring reliable economic conclusions.
Thus, the model demonstrates that the AZS is a significant predictor of Credit Rating, highlighting the relevance of overall financial conditions for credit assessment. Liquidity and solvency metrics appear to be the most important, while other variables, such as GDP variation and inflation, do not show statistical significance. This reinforces that CRAs focus on financial strength indicators when assessing companies' credit risk (Table 11).

5. Conclusions

This research investigated the influence of financial indicators on companies' Credit Ratings, applying the Sys-GMM method to address endogeneity and capture the temporal dynamics of the data. TQ, TSR, and AZS were the independent variables of interest in different model specifications.
The results indicate that neither TQ nor TSR are statistically significant in explaining variations in Credit Rating. This suggests that the stock market and TSR are not direct determinants in evaluating companies' credit risk.
In contrast, the AZS was a significant predictor of Credit Rating, with a positive and significant coefficient. This discovery reaffirms the importance of financial stability and a company's ability to avoid bankruptcy as critical components in determining its credit risk. This aligns with literature and market practices that value financial stability and long-term viability.
The model's robustness was confirmed by overall significance and diagnostic tests. However, the Sargan test revealed concerns about over-identification, emphasizing the need for caution in instrument selection. The discrepancy between the Sargan and Hansen tests suggests that, while the latter validates the instruments, the former indicates the possibility of instruments not contributing valuable information. This highlights the inherent complexity of economic modelling and the need for careful instrument selection to avoid overfitting and ensure reliable interpretations.
Additionally, the Arellano-Bond tests for AR(1) and AR(2) autocorrelation did not indicate issues, suggesting that lags are appropriately used as instruments. The validity of instruments and the absence of autocorrelation are crucial for the reliability of the Sys-GMM model, reinforcing the robustness of the obtained results.
The practical implications of these findings are significant for managers and policymakers. To improve their credit rating, companies should strengthen their overall financial position by increasing profitability and operational efficiency rather than exclusively concentrating on increasing market value or maximizing shareholder returns. This understanding can guide corporate strategies, investment decisions, and regulatory policies related to financial information disclosure and credit risk assessment.
Finally, this research contributes to the academic body by elucidating the complex dynamics influencing Credit Rating, demonstrating the need for robust and sophisticated economic models to capture the nuances of this relationship. The results reinforce the premise that credit risk assessment is multidimensional, and models like Sys-GMM are valuable tools for unravelling these intricate relationships. For future research, exploring additional variables such as market share, Industry Risk, Country Risk, financial policy, and cost structure is recommended to further understand their influence on credit ratings.

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Table 4.
Independent Variables Proxy Reference Literature
TQ Enterprise Value/Replacement Cost of Assets Fu et al. (2017); Yang and Gan (2021)
TSR [(Ending Stock Price - Begining Stock Price) + Dividends]/Beginning Stock Price Desai et al. (2022); Makhija and Trivedi (2021)
AZS Z = 1.2x1 + 1.4x2 + 3.3x3 + 0.6x4 + 1.0x5Where: x1 = Working capital / Total Assets, x2 = Retained earnings / Total Assets, x3 = Earnings before interest and taxes / Total Assets, x4 = Market Value of Equity / Bool Value of Total Liabilities, and x5 = Sales / Total Assets. Kablan (2020); Nelissen (2018)
Control Variables
Debt to Total Asset Debt to Total Asset Yahya and Hidayat (2020)
QR (Current Assets - Inventory)/Current Liabilities (Current Assets - Inventory)/Current Liabilities
EBITDAICOV EBITDA/Interest Expenses Foss (1995); Hung et al. (2013)
ROA Net Income/Average Total Assets Azhar and Meutia (2022); Kurniawan (2021)
GDP Agu et al. (2022); Gaertner et al. (2020)
CPI Naqvi et al. (2018)
FDRI Basha et al. (2021); Hoang et al. (2020)
Source: Own authorship.
Table 5. Descriptive Analysis.
Table 5. Descriptive Analysis.
Variables Obs. Mean Std. Dev. Min. Max.
CRWLTA 2142 15,09 2,46 7,21 22,00
QR 2142 1,11 0,82 0,01 9,19
TDTA 2142 0,32 0,17 0,00 2,44
EBITDAICOV 2142 16,12 14,81 -22,05 100,11
ROA 2142 11,16 7,40 -12,91 59,44
TQ 2142 0,33 0,18 0,00 2,45
TSR 2142 14,93 27,54 -89,22 109,86
AZS 2142 3,43 1,89 0,00 10,77
GDP 2142 2,13 2,11 -2,77 5,95
CPI 2142 1,86 1,18 0,12 4,70
FDRI 2142 0,70 0,76 0,08 2,27
Table 6. Correlation Matrix.
Table 6. Correlation Matrix.
CRWLTA QR TDTA EBITDAICOV ROA QTobin TSR AZS GDP CPI FDRI
CRWLTA 1,00
QR 0,10a 1,00
TDTA -0,33a -0,07a 1,00
EBITDAICOV 0,37a 0,16a -0,31a 1,00
ROA 0,21a 0,07a 0,22a 0,28a 1,00
TQ -0,32a -0,06a 0,99a -0,31a 0,22a 1,00
TSR 0,02 0,03 -0,04 0,07a 0,13a -0,03 1,00
AZS 0,37a 0,21a -0,17a 0,37a 0,50a -0,16a 0,07a 1,00
GDP 0,01 -0,02 -0,04 0,07a 0,10a -0,03 0,06a 0,06a 1,00
CPI -0,02 -0,04 0,07a 0,02 0,04 0,07a 0,14a -0,01 0,62a 1,00
FDRI 0,01 -0,07a 0,05b -0,02b 0,03 0,05 -0,10a -0,01 0,14a 0,12a 1,00
Note: a b c Indicates statistical significance at the 1%, 5%, and 10% levels respectively. Source: Stata
Table 7. reveals show the VIF Test for Multicollinearity.
Table 7. reveals show the VIF Test for Multicollinearity.
Variables VIF 1/VIF
TDTA 206,05 0,005
Tobin`s Q 204,52 0,005
CPI 1,68 0,595
GDP 1,67 0,599
ROA 1,67 0,600
AZS 1,61 0,621
EBITDAICOV 1,34 0,746
QR 1,07 0,938
TSR 1,06 0,941
FDRI 1,04 0,962
VIF Médio 42,17
Source: Stata.
Table 8. LLC Test for Unit Roots.
Table 8. LLC Test for Unit Roots.
Variables Adjusted t*-statistic p-valor Interpretation
CRWLTA -7,24 0,00 Stationary panel
QR -24,46 0,00 Stationary panel
TDTA -17,02 0,00 Stationary panel
EBITDAICOV -21,27 0,00 Stationary panel
ROA -21,10 0,00 Stationary panel
Tobin’s Q -16,84 0,00 Stationary panel
TSR -22,16 0,00 Stationary panel
AZS -20,19 0,00 Stationary panel
GDP 22,50 1,00 Non-stationary panel
CPI 20,05 1,00 Non-stationary panel
FDRI -38,10 0,00 Stationary panel
Table 9. Results for the Sys-GMM Model with Tobin's Q as the Variable of Interest.
Table 9. Results for the Sys-GMM Model with Tobin's Q as the Variable of Interest.
Dynamic panel-data estimation, one-step system GMM
Group variable: id Number of obs = 1904
Time variable : Year Number of groups = 238
Number of instruments = 148 Obs per group: min = 8
Wald chi2(7) = 13220,20 avg = 8,00
Prob > chi2 = 0,000 max = 8
Coef Erro Padrão Robusto Estatística Z Valor-p [95% Intervalo de Confiança]
Tobin`s -0,122 1,534 -0,080 0,936 -3,130 2,885
QR -0,016 0,274 -0,060 0,955 -0,553 0,522
EBITDAICOV 0,028 0,016 1,810 0,071 -0,002 0,059
ROA 0,030 0,026 1,140 0,254 -0,021 0,081
diff_GDP -0,006 0,009 -0,720 0,473 -0,023 0,011
diff_CPI -0,021 0,023 -0,940 0,350 -0,066 0,023
FDRI 0,017 0,031 0,540 0,589 -0,044 0,077
_cons 14,359 0,752 19,090 0,000 12,885 15,833
Arellano-Bond test for AR(1) in first differences: z = -0,87 Pr > z = 0,383
Arellano-Bond test for AR(2) in first differences: z = -1,19 Pr > z = 0,233
Sargan test of overid, restrictions: chi2(140) =4061,42 Prob > chi2 = 0,000
(Not robust, but not weakened by many instruments.)
Hansen test of overid, restrictions: chi2(140) = 137,60 Prob > chi2 = 0,542
(Robust, but weakened by many instruments.)
Difference-in-Hansen tests of exogeneity of instrument subsets:
GMM instruments for levels
Hansen test excluding group: chi2(109) = 120,56 Prob > chi2 = 0,211
Difference (null H = exogenous): chi2(31) = 17,04 Prob > chi2 = 0,980
gmm(QR EBITDAICOV ROA QTobin, lag(2 .))
Hansen test excluding group: chi2(0) = 0,00 Prob > chi2 = ,
Difference (null H = exogenous): chi2(140) = 137,60 Prob > chi2 = 0,542
gmm(diff_GDP diff_CPI FDRI, collapse lag(2 .))
Hansen test excluding group: chi2(133) = 132,79 Prob > chi2 = 0,489
Difference (null H = exogenous): chi2(7) = 4,81 Prob > chi2 = 0,683
Source: Stata.
Table 10. Results for the Sys-GMM Model with TSR as the Variable of Interest.
Table 10. Results for the Sys-GMM Model with TSR as the Variable of Interest.
Dynamic panel-data estimation, one-step system GMM
Group variable: id Number of obs = 1904
Time variable : Year Number of groups = 238
Number of instruments = 148 Obs per group: min = 8
Wald chi2(7) = 12587.70 avg = 8.00
Prob > chi2 = 0.000 max = 8
Coef Erro Padrão Robusto Estatística Z Valor-p [95% Intervalo de Confiança]
TSR 0,0006 0,0020 0,3200 0,7460 -0,0033 0,0045
QR -0,0662 0,3014 -0,2200 0,8260 -0,6569 0,5246
EBITDAICOV 0,0416 0,0159 2,6200 0,0090 0,0105 0,0727
ROA -0,0049 0,0304 -0,1600 0,8720 -0,0645 0,0547
diff_GDP -0,0030 0,0084 -0,3500 0,7230 -0,0195 0,0135
diff_CPI -0,0251 0,0203 -1,2400 0,2160 -0,0649 0,0146
FDRI 0,0286 0,0334 0,8600 0,3920 -0,0369 0,0940
_cons 14,5297 0,5290 27,4700 0,0000 13,4930 15,5665
Arellano-Bond test for AR(1) in first differences: z = -1.36 Pr > z = 0.174
Arellano-Bond test for AR(2) in first differences: z = -1.49 Pr > z = 0.135
Sargan test of overid. restrictions: chi2(140) =3211.73 Prob > chi2 = 0.000
(Not robust, but not weakened by many instruments.)
Hansen test of overid. restrictions: chi2(140) = 151.72 Prob > chi2 = 0.235
(Robust, but weakened by many instruments.)
Difference-in-Hansen tests of exogeneity of instrument subsets:
GMM instruments for levels
Hansen test excluding group: chi2(109) = 124.93 Prob > chi2 = 0.141
Difference (null H = exogenous): chi2(31) = 26.79 Prob > chi2 = 0.683
gmm(QR EBITDAICOV ROA QTobin, lag(2 .))
Hansen test excluding group: chi2(0) = 0.00 Prob > chi2 = .
Difference (null H = exogenous): chi2(140) = 151.72 Prob > chi2 = 0.235
gmm(diff_GDP diff_CPI FDRI, collapse lag(2 .))
Hansen test excluding group: chi2(133) = 142.39 Prob > chi2 = 0.273
Difference (null H = exogenous): chi2(7) = 9.33 Prob > chi2 = 0.230
Source: Stata.
Table 11. Results for the Sys-GMM Model with AZS as the Variable of Interest.
Table 11. Results for the Sys-GMM Model with AZS as the Variable of Interest.
Dynamic panel-data estimation, one-step system GMM
Group variable: id Number of obs = 1904
Time variable : Year Number of groups = 238
Number of instruments = 148 Obs per group: min = 8
Wald chi2(7) = 14231,84 avg = 8,00
Prob > chi2 = 0,000 max = 8
Coef Erro Padrão Robusto Estatística Z Valor-p [95% Intervalo de Confiança]
AZS 0,236 0,112 2,100 0,035 0,016 0,455
QR -0,116 0,298 -0,390 0,697 -0,701 0,469
EBITDAICOV 0,030 0,015 2,040 0,042 0,001 0,059
ROA -0,016 0,032 -0,490 0,627 -0,079 0,047
diff_GDP -0,007 0,009 -0,820 0,411 -0,024 0,010
diff_CPI -0,010 0,021 -0,490 0,623 -0,051 0,030
FDRI 0,027 0,030 0,910 0,365 -0,032 0,087
_cons 14,092 0,569 24,770 0,000 12,977 15,207
Arellano-Bond test for AR(1) in first differences: z = -1,47 Pr > z = 0,142
Arellano-Bond test for AR(2) in first differences: z = -0,28 Pr > z = 0,779
Sargan test of overid, restrictions: chi2(140) =2888,12 Prob > chi2 = 0,000
(Not robust, but not weakened by many instruments.)
Hansen test of overid, restrictions: chi2(140) = 152,27 Prob > chi2 = 0,226
(Robust, but weakened by many instruments.)
Difference-in-Hansen tests of exogeneity of instrument subsets:
GMM instruments for levels
Hansen test excluding group: chi2(109) = 130,30 Prob > chi2 = 0,080
Difference (null H = exogenous): chi2(31) = 21,97 Prob > chi2 = 0,884
gmm(QR EBITDAICOV ROA QTobin, lag(2 .))
Hansen test excluding group: chi2(0) = 0,00 Prob > chi2 = ,
Difference (null H = exogenous): chi2(140) = 152,27 Prob > chi2 = 0,226
gmm(diff_GDP diff_CPI FDRI, collapse lag(2 .))
Hansen test excluding group: chi2(133) = 144,15 Prob > chi2 = 0,240
Difference (null H = exogenous): chi2(7) = 8,11 Prob > chi2 = 0,323
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