A comparative study of machine learning techniques for stock price prediction
Why this work is in the frame
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Bibliographic record
Abstract
Stock price prediction has garnered significant interest among researchers and investors. Machine learning has shown great potential to produce accurate forecasts in the past few years. This paper has applied several machine learning techniques to develop a valid forecast consisting of linear models and various artificial neural networks. We have tested our models on the daily EURUSD pair dataset from the foreign exchange market and the daily S& P 500 dataset from the US stock market. Lastly, we have generated a fair comparison between different models and defined best practices for each domain. Our results indicate the efficiency of the linear models on the EURUSD dataset. Moreover, although deep neural networks have the best performance in predicting the exact price of the S& P 500, we found out that the ARIMA model can forecast the direction of the stock price better than any other model.
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it