Stock feature dimensionality reduction for closing price prediction using unsupervised machine learning technique (case study of Nigeria Stock Exchange)
Bibliographic record
Abstract
Stock data offers invaluable insights into the world of finance. It encourages investment and savings for both individuals and the nation as a whole. To monitor and predict stock, many stock variables are collected which in turn leads to curse of dimensionality because they occupy much storage space and take more computational time. In order to avoid this, there is a need to reduce the dimensionality of the stock features. Since stock is an unlabeled data with a Gaussian distribution (the features are normally jointly distributed), an unsupervised machine learning technique was applied to discover, establish an association and extract the most important features that have the entire generality of the original dataset for predicting the next day’s closing price. The dataset (daily price list) of Dangote Sugar Refinery Plc was randomly selected from the 27 blue chip companies in Nigeria Stock Exchange. 4 stock features were discovered and extracted from the 9 features in the original dataset.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".