Leveraging Machine Learning for Profit Forecasting and Cost Optimization Using Public Financial Data

Corporate finance depends significantly on the correctness of financial forecasting and cost control in strategic decision-making. The complexity and nonlinearity of financial data are often not fully captured by traditional models, particularly when they depend on macroeconomic factors. The proposed paper will introduce a machine learning (ML)-based profit prediction and cost optimization framework that uses publicly available SEC XBRL financial data and macroeconomic indicators. Time-aligned panel data is constructed by combining SEC filings with market data, which are preprocessed, engineered with financial ratios, and normalized. Time-series cross-validation is used to train various ML models, such as Linear Regression, Ridge, Lasso, Random Forest, XGBoost, and LSTM. SHAP analysis provides model interpretation, and a cost decomposition model provides simulations of five strategic cost-adjustment scenarios. XGBoost shows a better performance with an R² of 0.87 and RMSE of 14.8. The cost optimization scenarios prove up to 5.1% perceived improvement in profits, where SG&A and COGS reductions have the most significant effect. SHAP analysis creates relevant drivers, including Operating Margin and SG&A Ratio. The proposed framework will emphasize the potential of ML to facilitate financial decision-making by improving forecasting accuracy and optimizing cost structures using publicly available financial information.