Unveiling Market Sentiments: A Comprehensive Analysis of Stock Market Responses to Diverse News Events Using Data Mining Techniques
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
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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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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