Risks and Benefits of Initial Coin Offerings: Evidence from impak Finance, a Regulated ICO<sup>*</sup>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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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 | 0.000 | 0.000 |
| 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 it