Forecasting Bank ROE with Gradient Boosting: A Machine Learning Approach

Return on Equity (ROE) is a critical indicator of a firm's profitability, influenced by various operational, financial, and external factors. Traditional linear models often fail to capture the complex, nonlinear relationships that impact ROE, necessitating the use of advanced modeling techniques. This study employs a Gradient Boosting model to forecast the ROE for 21 banks, providing insights into their expected future performance.The analysis identifies consistently high-performing banks, which are recommended for potential investment and further analysis. Conversely, two banks consistently exhibit poor performance, suggesting the need for strategic interventionsThe research highlights the importance of addressing data quality issues and continuously updating the model to account for changing macroeconomic conditions and regulatory environments. The findings support strategic decision-making, emphasizing the need for a diversified investment portfolio to mitigate risks. This study emphasizes the potential of advanced machine learning models in financial forecasting, offering a robust approach to predicting key financial metrics and enhancing decision-making in the banking sector.