Comparison of Classification Algorithms on Financial Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Today’s life, big data can be seen in many fields. There are many computer-based methods developed and continuing to be developed to assess the big data more efficiently. Data mining is one of them. In this paper, two Canadian banks’ daily stock market price changes are examined by ten data mining algorithms to see which algorithm or algorithms classify the financial data well. For this purpose, thirty-seven years of daily stock price changes for two Canadian banks with 21 independent variables and one dependent variable, price, were obtained from NASDAQ. Ten data mining algorithms were applied to two datasets separately and the performances of the algorithms were compared and tested based on accuracy, kappa statistic, process time and confusion matrix. It was observed that tree algorithm, J48, and meta-analysis algorithms, Meta-Attribute Selected Classifier, Meta-Classification via Regression and Meta-Logitboost, classified the financial data with high accuracy. The results show that tree algorithm, J48, and the meta-analysis algorithms, Meta-Attribute Selected Classifier, Meta-Classification via Regression and Meta-Logitboost, are promising alternative to the conventional methods for financial prediction.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| 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 it