Technology Shocks and the Efficiency of Equity Markets in the Developed and Emerging Economies: A Global VAR Approach
Bibliographic record
Abstract
We tested the connection between technology shocks and the efficiency of equity markets in developed and emerging economies. We augmented the Global Vector Autoregressive (GVAR) database that covers data on 33 developed and emerging markets with the newly constructed data for technology shocks involving two variants, one with 164 countries (GTS-164), and the other, which is more region-specific. covering only Organization for Economic Co-operation and Development (OECD) countries (GTS-OECD). Our analysis was then modeled with GVAR methodology. We found that a one standard positive innovation shock to global technology (GTS-164) raises real equity prices in nearly 70% of the markets considered, and this is sustained over the forecast periods. However, the response of real equity prices to a global-specific technology shock (GTS-OECD) is rather different. While this shock resulted in the immediate rise in real equity prices, it is only transient and dissipated after the third quarter of the forecast horizon in about 85% of these markets. By implication, the efficiency of the real equity market was assured for the region-specific technology shock rather than for the more encompassing measurement that takes account of numerous markets, not minding whether these markets are developed or emerging. In sum, technological shocks seem to have greater impacts on the efficiency of developed (including Euro) markets than other markets.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".