Forecasting Models for Apple Inc. Stock Price Using Regression Smoothing and Box Jenkins Time Series Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In financial markets, stock price forecasting plays a critical role in investment decision-making, especially for globally influential companies like Apple Inc. This study aims to develop and assess models for predicting Apple Inc.'s stock price using various approaches, including regression analysis, smoothing methods, and the Box-Jenkins methodology. We analyzed ten years of Apple Inc.'s historical adjusted closing price data to construct models such as unregularized regression, regularized regression (Ridge and Lasso), smoothing methods (including exponential smoothing and moving averages), and the Box-Jenkins (SARIMA) model. The dataset was divided into training and test sets, and the predictive performance of each model was evaluated using the Average Prediction Squared Error (APSE). The findings indicate that the Simple Exponential Smoothing model performed best for short-term predictions, with an APSE of 0.01455. The Box-Jenkins model achieved an APSE of 0.08270, unregularized regression 0.021, while Ridge and Lasso models yielded APSEs of 0.6 and 4.97, respectively. In summary, smoothing methods are well-suited for short-term forecasting, while the Box-Jenkins method offers greater stability but comes with added complexity. Investors should choose forecasting models based on their specific requirements. This study provides empirical evidence for stock price forecasting and contributes new insights into the application of financial analysis and data science techniques.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 it