{"paper_id":"62cc26f4-6755-476f-8d67-c46a446f0f4e","body_text":"MATHEMATICAL FRAMEWORKS FOR FORECASTING PROFITABILITY AND RISK ASSESSMENT IN CORPORATE ACCOUNTING\nDescription\nThis research examines the application of mathematical frameworks in forecasting profitability and assessing risk in corporate accounting. The objective is to evaluate the effectiveness of advanced quantitative models such as regression analysis, Monte Carlo simulations, Bayesian inference, and time series analysis in enhancing financial decision-making. Using a quantitative approach, secondary financial data from corporate reports (2020-2024) were analyzed using statistical software and machine learning algorithms. The findings indicate a strong positive correlation (r = 0.85) between revenue growth and net profit margins, while regression analysis (R² = 0.82) confirms that 82% of profitability variations are explained by financial indicators. Time series models predict a 5% annual revenue growth trend, validating the reliability of mathematical forecasting techniques. The study concludes that integrating mathematical frameworks improves financial stability, enhances decision-making, and mitigates risk exposure. However, challenges such as data inconsistencies and technological constraints must be addressed. The research recommends investing in high-performance analytics, enhancing data governance, and adopting adaptive modeling approaches to optimize financial forecasts and corporate sustainability.\nFiles\n25-36.pdf\nFiles\n(1.0 MB)\n| Name | Size | Download all |\n|---|---|---|\n|\nmd5:dd76dc322662c26351275d279b25b5e5\n|\n1.0 MB | Preview Download |\nAdditional details\nIdentifiers\n- ISSN\n- 3081-0086\nRelated works\n- Is published in\n- 3081-0086 (ISSN)\nDates\n- Accepted\n-\n2025-06-26\nReferences\n- 3081-0086","source_license":"CC0","license_restricted":false}