Analyzing the Moderating Role of Earnings Predictive Power in the Relationship Between the Feedback Value of Accounting Information and Credit Rating

Abstract

The present study aimed to investigate the moderating role of earnings predictive power in the relationship between the feedback value of accounting information and the credit rating of companies listed on the Tehran Stock Exchange. This study was conducted using an applied, descriptive-correlational, and ex post facto research design based on panel data analysis. The statistical population consisted of all companies listed on the Tehran Stock Exchange during the period 2015–2024. After applying screening criteria, including fiscal year consistency, availability of financial information, and exclusion of financial intermediaries and investment firms, 155 companies were selected as the final sample. Data were extracted from audited financial statements, explanatory notes, board reports, and official financial databases. Credit rating was measured using the Emerging Market Score model. The feedback value of accounting information was measured based on the reduction in earnings forecast error after incorporating current earnings information, while earnings predictive power was measured using the residuals of an earnings prediction model based on operating cash flows and accruals. Firm size, return on assets, growth opportunity, sales growth, and auditor type were included as control variables. Data analysis was performed using panel regression models after testing stationarity, heteroskedasticity, and autocorrelation assumptions. The results showed that earnings predictive power has a positive and statistically significant effect on credit rating (β=0.023," " p=0.022). More importantly, the interaction effect between the feedback value of accounting information and earnings predictive power was positive and significant (β=0.015," " p=0.039), indicating that earnings predictive power strengthens the relationship between the feedback value of accounting information and credit rating. Among the control variables, firm size (β=0.016," " p<0.001), return on assets (β=0.085," " p=0.001), growth opportunity (β=0.010," " p=0.049), and sales growth (β=0.007," " p=0.005)had positive and significant effects on credit rating, whereas auditor type did not show a significant effect (pⓜ=0.952). The regression model was statistically significant overall (F=334.19," " p<0.001)and explained approximately 72.36% of the variation in corporate credit rating. The findings indicate that the usefulness of accounting information in the credit-rating process depends not only on its feedback value but also on the predictive strength of earnings. Firms with stronger earnings predictive power provide accounting information that is more informative and reliable for evaluating future financial performance and default risk. Therefore, earnings predictive power enhances the effectiveness of accounting feedback information in reducing uncertainty and improving credit evaluations. The study highlights the importance of integrating both historical and forward-looking dimensions of accounting quality in credit risk assessment and financial decision-making processes.