THE ROLE OF REGRESSION ANALYSIS IN OPTIMIZING THE INTERPRETATION OF KEY METRICS AND TRENDS IN CORPORATE FINANCIAL REPORTING

This study examines the role of regression analysis in optimizing the interpretation of key metrics and trends in corporate financial reporting. The research aims to evaluate the effectiveness of regression analysis in identifying financial anomalies, improving forecast accuracy, and ensuring compliance with international reporting standards. A mixed-methods approach was employed, analyzing financial statements from 50 publicly listed companies between 2020 and 2024 using multiple regression models. Key findings indicate that regression analysis explains 84% of financial data variations (R2=0.84R^2 = 0.84R2=0.84), demonstrating its predictive accuracy in revenue forecasting and financial trend analysis. The Pearson correlation coefficient (r=0.87r = 0.87r=0.87) confirms a strong positive relationship between financial transparency and corporate performance. Additionally, logistic regression models achieved an 89.4% classification accuracy in predicting IFRS compliance. The study concludes that regression techniques significantly enhance corporate reporting accuracy and strategic decision-making. It recommends integrating machine learning-enhanced regression models, robust data validation measures, and continuous model optimization to improve financial transparency and predictive accuracy.