Bibliographic record
Abstract
В данной статье исследуется динамика акций 98 североамериканских (США и Канады) и 43 европейских финтех-компаний с первичным публичным размещением акций в период с 2008 по 2020 год. Для краткосрочных результатов обнаружены значительные уровни недооценки: 17% для североамериканских и 10% для европейских финтех-компаний. Североамериканские финтех-компании имеют значительно более высокую степень недооценки при IPO, чем европейские FinTech-компании. Из результатов регрессии следует, что венчурный капитал и возраст фирм оказывают значительное влияние на степень недооценки. This article examines the performance of 98 North American (US and Canada) and 43 European fintech IPOs from 2008 to 2020. For short-term results, significant levels of underestimation were found: 17% for North American and 10% for European fintech companies. North American fintechs are significantly more undervalued in IPOs than European fintechs. The regression results show that venture capital and the age of firms have a significant impact on the degree of underestimation.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".