Using the Nasdaq Index to Predict AAPL Price by Linear Regression Analysis
Bibliographic record
Abstract
In this project, we want to predict AAPL’s stock price by the NASDAQ index by the regression model. The dependent variable is AAPL’s stock price, and the independent variable is the NASDAQ index. First, we do some descriptive statistics for the two variables and measure the distribution from the central tendency, variation tendency, and distribution to acknowledge the character of distributions. Based on the strong linear relationship between AAPL stock price and the NASDAQ index, we constructed a simple linear regression model. Considering the scale of the two variables, we tried the other three models with log transformation. And then, it shows that the log-log model has the best performance. However, in the residual analysis of the log-log model, it shows an autocorrelation in the residual, then we generate a new variable that is the one-order term for AAPL and add it into the model, and it surprisingly performs very well, whose R square is up to 99.72%. Therefore, we think combining the linear relationship with the market and the autocorrelation itself can construct a good model, and it can apply to predict much other stock's prices in the market.
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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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.025 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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".