The Impact of Artificial Intelligence on Financial Forecasting Accuracy in Corporate Finance

https://doi.org/10.5281/zenodo.17702974

Abstract

This study explores how artificial intelligence improves financial forecasting accuracy within corporate finance and compares the performance of traditional statistical models with modern machine learning and deep learning techniques. Using a rolling-origin evaluation from 2014 to 2024, the analysis examines short- and long-horizon forecasts for revenue, operating cash flow, and earnings. The results show that AI models, particularly XGBoost, LSTM, and ensemble approaches, consistently deliver lower forecasting errors and remain stable during shifts in economic conditions. Traditional models perform reasonably well in short windows but lose accuracy when market volatility increases or when the forecasting horizon extends. Statistical significance tests confirm that the gains achieved by AI models are meaningful and not due to chance. The findings indicate that firms that integrate AI-driven forecasting into their planning processes can strengthen budgeting, reduce uncertainty, and support more dependable long-term decisions.