Stock Price Prediction in Undirected Graphs Using a Structural Support Vector Machine
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
Business analytics techniques help mine and analyze business/financial data. For instance, a structural support vector machine (SSVM) can be used to perform classification on complex inputs such as the nodes of a graph structure. We connect collaborating companies in the information technology sector in an undirected graph and use an SSVM to predict positive or negative movement in their stock prices. By using a minimum graph-cutting algorithm to drive the cutting plane optimization problem of the SSVM, an exact solution is achieved in polynomial time. The learned model exploits the associative relationship between the prices of the collaborating companies to outperform the accuracy of a regular SVM. Experiments were conducted using the companies in the Standard and Poor's 500-45 Information Technology Sector index. Trades based on the learned model achieved superior returns in the range of 10% to 17% while tracking the index alone over the same time periods yielded returns in the range of -17% to 9%.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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