Stock Prediction Methodology using Artificial Neural Network: Application in Tesla stock price
Bibliographic record
Abstract
Contemporarily, the stock price fluctuates dramatically under the impact of lots of stochastic events (e.g., COVID-19, Russian-Ukraine conflicts). With the progress of machine learning techniques, it is feasible to predict the price accurately so that to inhibit the impacts of price variation. In this paper, the feasibility to forecast the price of underlying assets based on artificial neural network is investigated and discussed. For the sake of implementing the forecasting approach, the python Keras model is applied and different parameters are scanned. To give an intuitive example, the high volatility stock Tesla is selected as the target. According to the analysis, the state-of-art deep learning scenario is capable of prediction the price with high accuracy (i.e., above 95% R-square value). Nevertheless, some of the overfitting effects should be considered for applying such approach. Overall, these results shed light on guiding further exploration of implementing advanced machine learning approach to forecast the price of stock.
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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 | 0.014 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".