Risks and Benefits of Initial Coin Offerings: Evidence from impak Finance, a Regulated ICO<sup>*</sup>
Bibliographic record
Abstract
ABSTRACT This study provides a better understanding of the business and the regulated environment surrounding initial coin offerings (ICOs). An ICO is a call for funding to raise funds through a blockchain, where cryptoassets are issued. Key stakeholders involved are the firms launching ICOs, the investors, and the financial regulators. We conducted a case study of a firm that launched an ICO, impak Finance, the first regulated ICO in Canada. Based on the interviews of key respondents, we developed a framework identifying the main risks and benefits for firms to performing an ICO, showing differences between unregulated and regulated ICOs. Our study makes a number of research and practical contributions. First, we document the case of the first regulated ICO in Canada. The interviews conducted provided access to privileged insider information. Second, very few studies have been conducted on the impact of blockchains as a financing vehicle. ICOs using blockchains may be disruptive not only from a technology standpoint but also from a financial standpoint. While the possible applications of blockchains are unknown to us to date, we do know that blockchains have the potential to challenge the traditional financial system monitored by financial regulators. Last, the study identifies, through a framework, the risks and benefits of performing an ICO in an unregulated versus a regulated context, which has practical implications for firms operating in the fintech space. We trust that this framework will be useful for firms using ICOs, for investors, and for financial regulators.
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 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 | 0.012 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".