Unveiling Market Sentiments: A Comprehensive Analysis of Stock Market Responses to Diverse News Events Using Data Mining Techniques
Bibliographic record
Abstract
In modern economics, one of the most talked-about subjects is stock market volatility. Numerous factors have a significant daily impact on the stock market. This study aims to determine the effects of various news events, such as interest rate hikes, inflation rate announcements, stock analyst rating changes, macroeconomic shifts, earnings reports, and so on. In order to do that, we take into account a number of significant corporations (such as AAPL, AMD, AMZN, GOOGL, INTC, META, MSFT, NFLX, NVDA, SHOP, and TSLA), compile historical stock data for these businesses, and connect it with various kinds of news events. The evaluation's findings indicate that various news sources have distinct effects on the stock prices of various companies. The outcomes show how data mining methods can be used to carry out an extensive analysis on stock market responses to diverse news events. Specifically, it unveils interesting market sentiments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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 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".