Integrating AI and Econometrics for Equity Forecasting: A Case Study on Apple and Microsoft Stocks
Bibliographic record
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— driven by nonlinear dynamics and high-frequency trading—have exposed the limitations of these classical approaches.This research aims to evaluate and compare the predictive performance of traditional econometric models and AI-augmented methods, with a special focus on the Prophet model, in forecasting stock prices and volatility for major US firms, specifically Apple (AAPL) and Microsoft (MSFT). The study seeks to determine whether hybrid AI-econometric 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
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".