Bibliographic record
Abstract
The purpose of this study is to detect stock switching points from historical stock data and analyze corresponding financial news to predict upcoming stock switching points. Various change point detection methods have been investigated in the literature, such as online bayesian change point detection technique. Prediction of stock changing points using financial news has been implemented by different types of text mining techniques. In this study, online bayesian change point detection is implemented to detect stock switching points from historical stock data. Relevant news to detected change points are retrieved in the past and Latent Dirichlet Allocation technique is used to learn the hidden structures in the news data. Unseen news are then transferred to the trained topic representation. Similarity of relevant news and unseen news are used for prediction of future stock change points. Results show that stock switching points can be detected by historical stock data with better performance comparing to random guessing. It is possible to predict stock switching points by only fraction of financial news and with good result in terms of common performance metrics. According to this research, traders can take advantage of financial news to enhance prediction of future stock switching points.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".