Intraday Stock Price Prediction using Machine Learning: A Case Study on YFinance Stock Data
Bibliographic record
Abstract
This paper explores short-term stock price forecasting using high-frequency 2-minute interval data for Apple Inc. (AAPL), focusing on the application and evaluation of three predictive modeling approaches: Linear Regression, XGBoost, and Long Short-Term Memory (LSTM) neural networks. The dataset, collected via Yahoo Finance (YFinance API), was enriched with technical indicators, rolling statistics, and lag features to support predictive learning. The models were evaluated on their ability to predict the next closing price and directional movement. While Linear Regression achieved the highest R2score (0.9778) and lowest MSE (0.0502), XGBoost demonstrated competitive performance and provided valuable insights into feature importance. LSTM, though promising for capturing sequential dependencies, underperformed with a higher MSE (0.2490) and reduced R2(0.8932), likely due to the noisy and highly volatile nature of intraday data. All models showed limited directional accuracy (~52%), highlighting the challenge of predicting micro-movements in high-frequency financial time series. This study concludes with a discussion on the strengths and limitations of each approach and outlines future improvements including feature selection, hybrid modeling, and enhanced data preprocessing to boost intraday forecast reliability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".