The Impact of Internet Information Flow Regarding ‘Innovation’ on Common Stock Returns: Volume vs Google Search Quarries
Bibliographic record
Abstract
A number of scholars examine the impact of information flow associated with Google search queries of various search terms on heteroskedasticity of stock return data using the framework of Lamoureux and Lastrapes (1990). This paper examines the role of internet search queries in carrying the new information flow regarding economic innovation to the stock market for ten countries from a sample of top twenty innovative counties (based on Global Innovation Index 2018). When the internet search volume is included in the conditional variance equation of GJR-GRACH model, the ARCH coefficient becomes statistically insignificant for Canada, South Korea, Switzerland and USA (Hong Kong and South Korea to some extent). These findings suggest that the number of internet search volume is a manifestation of residual heteroskedasticity (ARCH type) in stock return data. As such, the internet provides a much-needed infrastructure for carrying the new information flow attached to economic innovation to the stock market as the common stockholder (i.e. capital providers for innovation) expectations reflect such persistent flow of new knowledge to the stock market. Trading volume testifies the specification used and, as such, the internet search queries could possibly be interpreted as an absorptive capacity variable as stock of new knowledge flow of the economy could be successfully traced by volume.
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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 | 0.005 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".