IPO Activity in Europe under the Covid-19 Pandemic : With a focus on the Norwegian IPO market
Bibliographic record
Abstract
This thesis seeks to provide comprehensive insights into the IPO activities in the top eight European IPO markets by capital raised in 2020, with an emphasis on the Norwegian IPO market, from the beginning of 2020 to the end of the first quarter in 2021. The top eight European countries include Norway, the United Kingdom, Sweden, France, Netherlands, Germany, Poland, and Italy.\nThe analysis reveals that most European IPOs in 2020 were conducted in the third and fourth quarters. Norway has achieved the most IPOs in Europe during the Pandemic, the highest growth in IPOs, and reached a decade-high level in IPO, despite the hit by Covid-19 Pandemic and Oil price collapse. In the meantime, the UK raised the most capital from IPOs in 2020. Moreover, the vast majority of IPOs in Norway, Sweden, Poland, and France were conducted on the Growth Market, while on the Main Regulated Market in the other four countries. Financials, energy, healthcare, industrials, utilities, and technology are the most active industries in 2020 in Europe. Furthermore, a flurry of green companies was noticed in the Norwegian IPOs. In addition, the average first-day return for the Norwegian IPOs reached 20.5% in 2020 and 16.22% in the first quarter of 2021, compared to around 2% in the previous three years. In particular, the average first-day return for the “Green” IPOs is nearly 40% in 2020 and 25.5% in the first quarter of 2021. Lastly, the number and market value of private investors had significant growth in Norway in 2020.\nIn addition, this thesis also aims to investigate factors that may drive the Norwegian IPO boom in 2020 through an exploratory approach, which is based on facts and empirical research results. The exploration findings suggest that the smoother listing process on Merkur Market lacks evidence in driving the IPO activities in 2020. The relationship between high first-day return and IPO volume needs further research because both favorable and opposed data and research results were discovered. Furthermore, according to previous studies in Asian markets, the early commitment from cornerstones in Norwegian IPOs may have boosted the market confidence and improved the IPO success probability. Nevertheless, more extensive research in the Norwegian market is needed to verify this argument. Besides, the innovation culture in Norway may have enhanced the adaptability of companies in changing conditions. However, the adaptability may also be influenced by the less intensive pandemic situation in Norway. A scientific study with the elimination of interference from other factors is needed to prove this argument further. Lastly, the leading green transition wave with enormous support from the Norwegian government may be a pivotal contributor to the “green” IPOs. Notwithstanding, research with sufficient data is needed to make a thorough comparison with the other European countries to evince why only Norway saw a green IPO wave in 2020.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".