Bibliographic record
Abstract
The online stock trading has begun to spread in 1998. And HTS started to be evaluated by Stockpia, 100hot and this reports, which specializes in evaluation of financial online sites from the 1st quarter of year 2000. This study is to examine whether the evaluation on HTS has an impact on the stock prices of the security corporations. For the evaluation of the relation, I classified the security corporations into three groups(high group, middle group, low group) and compared the market share after the 1st quarter of year 2000. And I classified two groups(2 top and 5 top) in high group and compared the value-fluctuation of the securities industry with theirs at the identical time and thoroughly analyzed the results. Accordingly the increase rate of the 5 top ranked corp. showed remarkably higher than the securities industry's. Moreover, the increase rate of the 2 top ranked corporations surpassed the 5 top ranked corporations. As the total volume of online trading has increased in the stock trading market, the number of new customers has also increased for the security corporations, which received high ranks on their HTS based on the evaluation results, and their market shares have risen relatively. Therefore, it seems that receiving high ranks on their HTS has a positive impact on their stock prices.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".