A methodology for stock market analysis utilizing rough set theory
Bibliographic record
Abstract
Quants are aiding brokers and investment managers for stock market analysis and prediction. The Quant's black magic stems from many of the evolving artificial intelligence (AI) techniques. Extensive literature exists describing attempts to use AI techniques, and in particular neural networks, for analyzing stock market variations. The main problem with neural networks, however is the tremendous difficulty in interpreting the results. The neural nets approach is a black box approach in which no new knowledge regarding the nature of the interactions between the market indicators and the stock market fluctuations is extracted from the market data. Consequently, there is a need to develop methodologies and tools which would help in increasing the degree of understanding of market processes and, at the same time, would allow for relatively accurate predictions. The methods stemming from the research on knowledge discovery in databases (KDD) seem to provide a good mix of predictive and knowledge acquisition capabilities for the purpose of market prediction and market data analysis. This paper describes the methodology of rough sets while citing two applications which apply rough set theory (BST) for stock market analysis using Datalogic/R+. This is based on the variable precision model of rough sets (VPRS) to acquire new knowledge from market data.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".