Artificial Intelligence Hybrid AI-Econometric Models for Forecasting Volatile US Equities: A Comparative Study of Apple and Microsoft

Abstract

Financial forecasting in the US stock market has traditionally relied on econometric models such as ARIMA, SARIMA, and GARCH, which offer interpretability and robust performance in stable environments. However, the increasing complexity and volatility of modern markets— 25 Oct 2025 (Published Online) driven by nonlinear dynamics and high-frequency trading—have exposed the limitations of these AI-augmented forecasting, Prophet traditional econometric models and AI-augmented methods, with a special focus on the Prophet model, ARIMA-GARCH hybrid, stock model, in forecasting stock prices and volatility for major US firms, specifically Apple (AAPL) price prediction, volatility clustering, and Microsoft (MSFT). The study seeks to determine whether hybrid AI-econometric US equities. frameworks provide superior accuracy and risk quantification compared to standalone models. Historical daily price data (January–June 2024) from Yahoo Finance underwent preprocessing: log-return transformation, stationarity enforcement (ADF/PP tests), outlier winsorization, and volatility clustering validation. Models were trained on 80% of the data (105 observations) and tested on 20% (26 observations). Performance was measured via RMSE, MAE, AIC/BIC, and uncertainty interval accuracy. Prophet outperformed traditional models, reducing Apple’s RMSE by 6% (7.02 vs. 7.46) and MAE by 8.9% (4.70 vs. 5.16) compared to AI-augmented ARIMA. For Microsoft, Prophet achieved 11% lower RMSE (9.46 vs. 10.64) and 14.4% better MAE (5.89 vs. 6.88). AI-augmented GARCH improved volatility forecasts by 19% for Apple, capturing asymmetric responses missed by classical GARCH. Hybrid models (e.g., Prophet-GARCH) demonstrated superior trend reversal detection but increased operational complexity. Integrating AI with econometric models significantly enhances forecasting accuracy and risk quantification, particularly through Prophet’s uncertainty intervals and adaptability to structural breaks. While computational demands and small-sample biases remain challenges, these hybrids offer actionable insights for portfolio optimization and crisis preparedness in volatile markets.