Bibliographic record
Abstract
This study employs an event study methodology to thoroughly analyze the short-term and long-term impact of ChatGPT’s launch on NVIDIA’s stock price. The findings reveal that the initial release of ChatGPT significantly boosted market enthusiasm for investing in NVIDIA, driven by its central role in AI computing infrastructure (e.g., surging demand for GPUs), which propelled short-term stock price gains. However, in the long run, NVIDIA’s stock performance is constrained by multiple factors, including intensified industry competition (e.g., technological catch-up by rivals like AMD and Intel), uncertainties in AI technology iteration, and market skepticism about the sustainability of computing demand. Additionally, macroeconomic fluctuations and geopolitical risks have influenced long-term investor expectations. This case highlights the nonlinear relationship between technological breakthroughs and capital market reactions, underscoring the tension between short-term sentiment and long-term fundamentals in the valuation of AI-related stocks. The research findings provide valuable insights for investors analyzing the valuation logic of technology-driven companies and offer empirical evidence for policymakers to understand the interplay between the AI industry and capital markets. Future research could further quantify the differential impact of technological milestones on various segments of the industry chain across different event windows.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".