Research on the Stock Price Forecasting of Netflix Based on Linear Regression, Decision Tree, and Gradient Boosting Models
Why this work is in the frame
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Bibliographic record
Abstract
Stock return forecasting has always been a popular research topic in the stock market.This paper adopts three models, including linear regression, decision tree, and gradient boosting approaches, to predict the eighth day's stock return of Netflix stock based on its last seven days' stock return, based on the price data of Netflix stock from 2002 to 2021.Prediction results and model performances are compared with the five-fold cross-validation and Python score method.The results indicates that the linear regression model is the best model for predicting Netflix-type stocks' return on a long-term scale and has no sharp nor abnormal fluctuations.This research result enriches the existed stock return forecasting literature and provides a certain revelation for investors towards predicting stock return growth trends and stock investment values accurately.
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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 | 0.032 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.010 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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